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Bibliography on: Brain-Computer Interface

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ESP: PubMed Auto Bibliography 26 May 2022 at 01:33 Created: 

Brain-Computer Interface

Wikipedia: A brain–computer interface (BCI), sometimes called a neural control interface (NCI), mind–machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature. BCI-effected sensory input: Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s. BCI-effected motor output: When artificial intelligence is used to decode neural activity, then send that decoded information to some kind of effector device, BCIs have the potential to restore communication to people who have lost the ability to move or speak. To date, the focus has largely been on motor skills such as reaching or grasping. However, in May of 2021 a study showed that an AI/BCI system could be use to translate thoughts about handwriting into the output of legible characters at a usable rate (90 characters per minute with 94% accuracy).

Created with PubMed® Query: bci OR (brain-computer interface) OR (brain-machine interface) OR (mind-machine interface) OR (neural-control interface) NOT 26799652[PMID] NOT pmcbook NOT ispreviousversion

Citations The Papers (from PubMed®)

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RevDate: 2022-05-25

Costello JT, Nason SR, An H, et al (2022)

A low-power communication scheme for wireless, 1000 channel brain-machine interfaces.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Brain-machine interfaces (BMIs) have the potential to restore motor function but are currently limited by electrode count and long-term recording stability. These challenges may be solved through the use of free-floating "motes" which wirelessly transmit recorded neural signals, if power consumption can be kept within safe levels when scaling to thousands of motes. Here, we evaluated a pulse-interval modulation (PIM) communication scheme for infrared (IR)-based motes that aims to reduce the wireless data rate and system power consumption.

APPROACH: To test PIM's ability to efficiently communicate neural information, we simulated the communication scheme in a real-time closed-loop BMI with non-human primates. Additionally, we performed circuit simulations of an IR-based 1000-mote system to calculate communication accuracy and total power consumption.

MAIN RESULTS: We found that PIM at 1kb/s per channel maintained strong correlations with true firing rate and matched online BMI performance of a traditional wired system. Closed-loop BMI tests suggest that lags as small as 30 ms can have significant performance effects. Finally, unlike other IR communication schemes, PIM is feasible in terms of power, and neural data can accurately be recovered on a receiver using 3mW for 1000 channels.

SIGNIFICANCE: These results suggest that PIM-based communication could significantly reduce power usage of wireless motes to enable higher channel-counts for high-performance BMIs.

RevDate: 2022-05-23

Lenarz T, Büchner A, A Illg (2022)

Cochlear Implantation: Concept, Results Outcomes and Quality of Life.

Laryngo- rhino- otologie, 101(S 01):S36-S78.

Cochlear implant today are an essential method of auditory rehabilitation in patients with severe to profound hearing loss. Due to the rapid development of implant technology the results have been markedly improved. Today about 80 % of patients can use the telephone and children achieve near to normal hearing and speech development. In consequence, more patients are candidates for a cochlear implant today including those with high frequency deafness and single sided deafness. However, today only 60,000 out of 1 Million CI-candidates in Germany have been implanted so far. In future multi modal universal auditory implants will provide combined electric-mechanical stimulation to make best use of the residual auditory hearing and the electrical stimulation of the auditory nerve. They allow a continuous adaptation of the stimulation strategy onto the given functional status of haircells and auditory nerve fibers especially in cases of progressive hearing loss. Brain computer interfaces will allow the automated fitting and adaptation to the acoustic scene by optimizing the signal processing for best possible auditory performance. Binaural hearing systems will improve directional hearing and speech perception in noise. Advanced implants are composed of individualized electrodes by additive manufacturing which can be inserted atraumaticly by a computer and robot assisted surgery. After insertion they automatically adept to the anatomy of the individual cochlear. These advanced implants are composed with additional integrated biological components for the preservation of residual hearing and regeneration of neural elements to improve the electrode nerve interface. This will allow to increase the number of electrical contacts as a major step towards the bionic ear. This will allow overcoming the principal limits of today's cochlear implant technology. Advanced care models will allow an easy way for the patient towards hearing preservation cochlear implantation under local anesthesia using minimal invasive high precision cochlear implant surgery. These implant systems will become a personal communicator with improved connectivity. Remote care and self-fitting will empower the patient to optimize his own hearing.

RevDate: 2022-05-23

Du Y, J Liu (2022)

IENet: a robust convolutional neural network for EEG based brain-computer interfaces.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) develop into novel application areas with more complex scenarios, which put forward higher requirements for the robustness of EEG signal processing algorithms. Deep learning can automatically extract discriminative features and potential dependencies via deep structures, demonstrating strong analytical capabilities in numerous domains such as computer vision (CV) and natural language processing (NLP). Making full use of deep learning technology to design a robust algorithm that is capable of analyzing EEG across BCI paradigms is our main work in this paper.

APPROACH: Inspired by InceptionV4 and InceptionTime architecture, we introduce a neural network ensemble named InceptionEEG-Net (IENet), where multi-scale convolutional layer and convolution of length 1 enable model to extract rich high-dimensional features with limited parameters. In addition, we propose the average receptive field gain for convolutional neural networks (CNNs), which optimizes IENet to detect long patterns at a smaller cost. We compare with the current state-of-the-art method across five EEG-BCI paradigms: steady-state visual evoked potentials, epilepsy EEG, overt attention P300 visual-evoked potentials, covert attention P300 visual-evoked potentials and movement-related cortical potentials.

MAIN RESULTS: The classification results show that the generalizability of IENet is on par with the state-of-the-art paradigm-agnostic models on test datasets. Furthermore, the feature explainability analysis of IENet illustrates its capability to extract neurophysiologically interpretable features for different BCI paradigms, ensuring the reliability of algorithm. Significance. It can be seen from our results that IENet can generalize to different BCI paradigms. And it is essential for deep CNNs to increase the receptive field size using average receptive field gain.

RevDate: 2022-05-23

Jiang H, Wang R, Zheng Z, et al (2022)

Short report: surgery for implantable brain-computer interface assisted by robotic navigation system.

Acta neurochirurgica [Epub ahead of print].

We present an implantable brain-computer interface surgical case assisted by robotic navigation system in an elderly patient with tetraplegia caused by cervical spinal cord injury. Left primary motor cortex was selected for implantation of microelectrode arrays based on fMRI location of motor imagery. Robotic navigation system was used during this procedure for precise and stable manipulation. A design of bipartite incision was made to reduce the risk of surgery-related infection and facilitate BCI training. At 1-year follow-up, the neural signals were robust, and this patient was able to control three-dimensional movement of a prosthetic limb without any complications.

RevDate: 2022-05-23

Serruya MD, Napoli A, Satterthwaite N, et al (2022)

Neuromotor prosthetic to treat stroke-related paresis: N-of-1 trial.

Communications medicine, 2:37 pii:105.

Background: Functional recovery of arm movement typically plateaus following a stroke, leaving chronic motor deficits. Brain-computer interfaces (BCI) may be a potential treatment for post-stroke deficits.

Methods: In this n-of-1 trial (NCT03913286), a person with chronic subcortical stroke with upper-limb motor impairment used a powered elbow-wrist-hand orthosis that opened and closed the affected hand using cortical activity, recorded from a percutaneous BCI comprised of four microelectrode arrays implanted in the ipsilesional precentral gyrus, based on decoding of spiking patterns and high frequency field potentials generated by imagined hand movements. The system was evaluated in a home setting for 12 weeks.

Results: Robust single unit activity, modulating with attempted or imagined movement, was present throughout the precentral gyrus. The participant acquired voluntary control over a hand-orthosis, achieving 10 points on the Action Research Arm Test using the BCI, compared to 0 without any device, and 5 using myoelectric control. Strength, spasticity, the Fugl-Meyer scores improved.

Conclusions: We demonstrate in a human being that ensembles of individual neurons in the cortex overlying a chronic supratentorial, subcortical stroke remain active and engaged in motor representation and planning and can be used to electrically bypass the stroke and promote limb function. The participant's ability to rapidly acquire control over otherwise paralyzed hand opening, more than 18 months after a stroke, may justify development of a fully implanted movement restoration system to expand the utility of fully implantable BCI to a clinical population that numbers in the tens of millions worldwide.

RevDate: 2022-05-23

Zhang L, Zhang R, Guo Y, et al (2022)

Assessing residual motor function in patients with disorders of consciousness by brain network properties of task-state EEG.

Cognitive neurodynamics, 16(3):609-620.

Recent achievements in evaluating the residual consciousness of patients with disorders of consciousness (DOCs) have demonstrated that spontaneous or evoked electroencephalography (EEG) could be used to improve consciousness state diagnostic classification. Recent studies showed that the EEG signal of the task-state could better characterize the conscious state and cognitive ability of the brain, but it has rarely been used in consciousness assessment. A cue-guide motor task experiment was designed, and task-state EEG were collected from 18 patients with unresponsive wakefulness syndrome (UWS), 29 patients in a minimally conscious state (MCS), and 19 healthy controls. To obtain the markers of residual motor function in patients with DOC, the event-related potential (ERP), scalp topography, and time-frequency maps were analyzed. Then the coherence (COH) and debiased weighted phase lag index (dwPLI) networks in the delta, theta, alpha, beta, and gamma bands were constructed, and the correlations of network properties and JFK Coma Recovery Scale-Revised (CRS-R) motor function scores were calculated. The results showed that there was an obvious readiness potential (RP) at the Cz position during the motor preparation process in the MCS group, but no RP was observed in the UWS group. Moreover, the node degree properties of the COH network in the theta and alpha bands and the global efficiency properties of the dwPLI network in the theta band were significantly greater in the MCS group compared to the UWS group. The above network properties and CRS-R motor function scores showed a strong linear correlation. These findings demonstrated that the brain network properties of task-state EEG could be markers of residual motor function of DOC patients.

Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-021-09741-7.

RevDate: 2022-05-23

Wang F, Liu H, Zhao L, et al (2022)

Improved Brain-Computer Interface Signal Recognition Algorithm Based on Few-Channel Motor Imagery.

Frontiers in human neuroscience, 16:880304.

Common spatial pattern (CSP) is an effective algorithm for extracting electroencephalogram (EEG) features of motor imagery (MI); however, CSP mainly aims at multichannel EEG signals, and its effect in extracting EEG features with fewer channels is poor-even worse than before using CSP. To solve the above problem, a new combined feature extraction method has been proposed in this study. For EEG signals from fewer channels (three channels), wavelet packet transform, fast ensemble empirical mode decomposition, and local mean decomposition were used to decompose the band-pass filtered EEG into multiple time-frequency components, and the corresponding components were selected according to the frequency characteristics of MI or the correlation coefficient between its time-frequency components and the original EEG signal. Furthermore, phase space reconstruction (PSR) was performed on the selected components after the three time-frequency decompositions, the maximum Lyapunov index was calculated, and the features were reconstructed; then, CSP projection mapping was used for the reconstructed features. The support vector machine probability output model was trained by the obtained three mappings. Probability outputs by three different support vector machines were then obtained. Finally, the classification of test samples was determined by the fusion of the Dempster-Shafer evidence theory at the decision level. The results showed that the accuracy of the proposed method was 95.71% on data set III of BCI competition II (left- and right-hand MI), which was 2.88% higher than the existing methods. On data set IIb of BCI competition IV, the average accuracy was 86.60%, which was 2.3% higher than the existing methods. This study verified the effectiveness of the proposed method and provided an approach for the research and development of the MI-BCI system based on fewer channels.

RevDate: 2022-05-23

Watters PA, Ivanov PC, Ning X, et al (2021)

Editorial: Neural Dynamics - Models and Complexity.

Frontiers in neuroscience, 15:841077.

RevDate: 2022-05-23

Peterson V, Galván C, Hernández H, et al (2022)

A motor imagery vs. rest dataset with low-cost consumer grade EEG hardware.

Data in brief, 42:108225 pii:S2352-3409(22)00428-0.

The data consist of electroencephalography (EEG) signals acquired by means of low-cost consumer-grade devices from 10 participants (four females, right-handed, mean age ± SD = 26.1 ± 4.0 years) without any previous experience in Brain-Computer Interfaces (BCIs) usage. The BCI protocol consisted of two conditions, namely the kinesthetic imagination of grasping movement (motor imagery, MI) of the dominant hand and a rest/idle condition. Five protocol runs were required to be performed by each participant in a single-day session, of about 1.5 h. The first run, called RUN0, involved 5 trials of real grasping movement together with the same number of trials in a rest condition. This first run was done to both better explain the protocol and to encourage the participant to focus on the sensation of executing the movement. The rest of the runs (RUN1-RUN4) were identical, consisting of 20 trials for each condition presented in a random order. The electrical brain activity was registered from 15 electrodes covering the sensorimotor area, at a sampling frequency of 125 Hz. Muscle activity of the dominant hand was controlled via the electromyography (EMG) activity by two electrodes placed at two antagonist muscles involved in the flexion/extension of the wrist. The recordings were performed in a non-shielded office, by means of low-cost consumer grade devices and free multi-platform open source software. The EMG corruption level was analyzed and EEG trials for which the EMG activity was higher than a prescribed threshold value, were discarded. During acquisition, EEG data was digitally band-pass filtered between 0.5 and 45 Hz. These data provide a motor imagery vs. rest EEG dataset, relevant for BCI for motor rehabilitation applications. Since the recordings were performed by means of low-cost consumer grade devices in a non-controlled environment, this dataset provides an excellent source for exploring robust brain decoding techniques for future in-home BCI usage.

RevDate: 2022-05-23
CmpDate: 2022-05-23

Li F, Wang C, Li Y, et al (2022)

Phase Preservation Neural Network for Electroencephalography Classification in Rapid Serial Visual Presentation Task.

IEEE transactions on bio-medical engineering, 69(6):1931-1942.

Neuroscience studies have demonstrated the phase-locked characteristics of some early event-related potential (ERP) components evoked by stimuli. In this study, we propose a phase preservation neural network (PPNN) to learn phase information to improve the Electroencephalography (EEG) classification in a rapid serial visual presentation (RSVP) task. The PPNN consists of three major modules that can produce spatial and temporal representations with the high discriminative ability of the EEG features for classification. We first adopt a stack of dilated temporal convolution layers to extract temporal dynamics while avoiding the loss of phase information. Considering the intrinsic channel dependence of the EEG data, a spatial convolution layer is then applied to obtain the spatial-temporal representation of the input EEG signal. Finally, a fully connected layer is adopted to extract higher-level features for the final classification. The experiments are conducted on two public and one collected EEG datasets from the RSVP task, in which we evaluated the performance and explored the capability of phase preservation of our PPNN model and visualized the extracted features. The experimental results indicate the superiority of the proposed PPNN when compared with previous methods, suggesting the PPNN is a robust model for EEG classification in RSVP task.

RevDate: 2022-05-20

Mashrur FR, Rahman KM, Miya MTI, et al (2022)

An Intelligent Neuromarketing System for Predicting Consumers' Future Choice from Electroencephalography Signals.

Physiology & behavior pii:S0031-9384(22)00153-6 [Epub ahead of print].

Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billion annually on marketing, promotion, and advertisement using traditional marketing research tools. In addition, these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequently criticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises to overcome such constraints. In this work, an EEG-based neuromarketing framework is employed for predicting consumer future choice (affective attitude) while they view E-commerce products. After preprocessing, three types of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapper-based Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negative affective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67±2.98, 98±3.22, and 98.67±3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition, among all the channels, Fz achieves best accuracy 90±7.81, 90.67±9.53, and 92.67±7.03 for 5-fold, 10-fold, and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketing framework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEG-based neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferences accurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future.

RevDate: 2022-05-20

Chen X, Hu N, X Gao (2022)

Development of a Brain-Computer Interface-Based Symbol Digit Modalities Test and Validation in Healthy Elderly Volunteers and Stroke Patients.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Standard cognitive assessment tools often involve motor or verbal responses, making them impossible for severely motor-disabled individuals. Brain-computer interfaces (BCIs) are expected to help severely motor-impaired individuals to perform cognitive assessment because BCIs can circumvent motor and verbal requirements. Currently, the field of research to develop cognitive tasks based on BCI is still in its nascent stage and needs further development. This study explored the possibility of developing a BCI version of symbol digit modalities test (BCI-SDMT). Steady-state visual evoked potential (SSVEP) was adopted to build the BCI and a 9-target SSVEP-BCI was realized to send examinees' responses. A training-free algorithm (i.e., filter bank canonical correlation analysis) was used for SSVEP identification. Thus, examinees are able to start the proposed BCI-SDMT immediately. Eighty-nine healthy elderly volunteers and 9 stroke patients were enrolled to validate the technical feasibility of the developed BCI-SDMT. For all participants, the average recognition accuracies of the developed BCI and BCI-SDMT were 93.89±8.48% and 92.58±10.52%, respectively, were considerably above the chance level (i.e., 11.11%). These results indicated that both healthy elderly volunteers and stroke patients could elicit sufficient SSVEPs to control the BCI. Furthermore, patient use of the developed BCI-SDMT was unaffected by the presence of motor impairment. They could understand instructions, pair numbers with specific symbols, and send commands using the BCI. The proposed BCI-SDMT can be used as a complement to the existing versions of the SDMT and has the potential to evaluate cognitive abilities in individuals with severe motor disabilities.

RevDate: 2022-05-20

An H, Nason-Tomaszewski SR, Lim J, et al (2022)

A Power-Efficient Brain-Machine Interface System with a Sub-mW Feature Extraction and Decoding ASIC Demonstrated in Nonhuman Primates.

IEEE transactions on biomedical circuits and systems, PP: [Epub ahead of print].

Intracortical brain-machine interfaces have shown promise for restoring function to people with paralysis, but their translation to portable and implantable devices is hindered by their high power consumption. Recent devices have drastically reduced power consumption compared to standard experimental brain-machine interfaces, but still require wired or wireless connections to computing hardware for feature extraction and inference. Here, we introduce a Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) in 180 nm CMOS that can extract neural spiking features and predict two-dimensional behaviors in real-time. To reduce amplifier and feature extraction power consumption, the NeuRAD has a hardware accelerator for extracting spiking band power (SBP) from intracortical spiking signals and includes an M0 processor with a fixed-point Matrix Acceleration Unit (MAU) for efficient and flexible decoding. We validated device functionality by recording SBP from a nonhuman primate implanted with a Utah microelectrode array and predicting the one- and two-dimensional finger movements the monkey was attempting to execute in closed-loop using a steady-state Kalman filter (SSKF). Using the NeuRAD's real-time predictions, the monkey achieved 100 % success rate and 0.82 s mean target acquisition time to control one-dimensional finger movements using just 581 μW. To predict two-dimensional finger movements, the NeuRAD consumed 588 μW to enable the monkey to achieve a 96 % success rate and 2.4 s mean acquisition time. By employing SBP, ASIC brain-machine interfaces can close the gap to enable fully implantable therapies for people with paralysis.

RevDate: 2022-05-20

Masud U, Saeed T, Akram F, et al (2022)

Unmanned Aerial Vehicle for Laser Based Biomedical Sensor Development and Examination of Device Trajectory.

Sensors (Basel, Switzerland), 22(9): pii:s22093413.

Controller design and signal processing for the control of air-vehicles have gained extreme importance while interacting with humans to form a brain-computer interface. This is because fewer commands need to be mapped into multiple controls. For our anticipated biomedical sensor for breath analysis, it is mandatory to provide medication to the patients on an urgent basis. To address this increasingly tense situation in terms of emergencies, we plan to design an unmanned vehicle that can aid spontaneously to monitor the person's health, and help the physician spontaneously during the rescue mission. Simultaneously, that must be done in such a computationally efficient algorithm that the minimum amount of energy resources are consumed. For this purpose, we resort to an unmanned logistic air-vehicle which flies from the medical centre to the affected person. After obtaining restricted permission from the regional administration, numerous challenges are identified for this design. The device is able to lift a weight of 2 kg successfully which is required for most emergency medications, while choosing the smallest distance to the destination with the GPS. By recording the movement of the vehicle in numerous directions, the results deviate to a maximum of 2% from theoretical investigations. In this way, our biomedical sensor provides critical information to the physician, who is able to provide medication to the patient urgently. On account of reasonable supply of medicines to the destination in terms of weight and time, this experimentation has been rendered satisfactory by the relevant physicians in the vicinity.

RevDate: 2022-05-20

Värbu K, Muhammad N, Y Muhammad (2022)

Past, Present, and Future of EEG-Based BCI Applications.

Sensors (Basel, Switzerland), 22(9): pii:s22093331.

An electroencephalography (EEG)-based brain-computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.

RevDate: 2022-05-20

Topic A, Russo M, Stella M, et al (2022)

Emotion Recognition Using a Reduced Set of EEG Channels Based on Holographic Feature Maps.

Sensors (Basel, Switzerland), 22(9): pii:s22093248.

An important function of the construction of the Brain-Computer Interface (BCI) device is the development of a model that is able to recognize emotions from electroencephalogram (EEG) signals. Research in this area is very challenging because the EEG signal is non-stationary, non-linear, and contains a lot of noise due to artifacts caused by muscle activity and poor electrode contact. EEG signals are recorded with non-invasive wearable devices using a large number of electrodes, which increase the dimensionality and, thereby, also the computational complexity of EEG data. It also reduces the level of comfort of the subjects. This paper implements our holographic features, investigates electrode selection, and uses the most relevant channels to maximize model accuracy. The ReliefF and Neighborhood Component Analysis (NCA) methods were used to select the optimal electrodes. Verification was performed on four publicly available datasets. Our holographic feature maps were constructed using computer-generated holography (CGH) based on the values of signal characteristics displayed in space. The resulting 2D maps are the input to the Convolutional Neural Network (CNN), which serves as a feature extraction method. This methodology uses a reduced set of electrodes, which are different between men and women, and obtains state-of-the-art results in a three-dimensional emotional space. The experimental results show that the channel selection methods improve emotion recognition rates significantly with an accuracy of 90.76% for valence, 92.92% for arousal, and 92.97% for dominance.

RevDate: 2022-05-20

Zapała D, Augustynowicz P, M Tokovarov (2022)

Recognition of Attentional States in VR Environment: An fNIRS Study.

Sensors (Basel, Switzerland), 22(9): pii:s22093133.

An improvement in ecological validity is one of the significant challenges for 21st-century neuroscience. At the same time, the study of neurocognitive processes in real-life situations requires good control of all variables relevant to the results. One possible solution that combines the capability of creating realistic experimental scenarios with adequate control of the test environment is virtual reality. Our goal was to develop an integrative research workspace involving a CW-fNIRS and head-mounted-display (HMD) technology dedicated to offline and online cognitive experiments. We designed an experimental study in a repeated-measures model on a group of BCI-naïve participants to verify our assumptions. The procedure included a 3D environment-adapted variant of the classic n-back task (2-back version). Tasks were divided into offline (calibration) and online (feedback) sessions. In both sessions, the signal was recorded during the cognitive task for within-group comparisons of changes in oxy-Hb concentration in the regions of interest (the dorsolateral prefrontal cortex-DLPFC and middle frontal gyrus-MFG). In the online session, the recorded signal changes were translated into real-time feedback. We hypothesized that it would be possible to obtain significantly higher than the level-of-chance threshold classification accuracy for the enhanced attention engagement (2-back task) vs. relaxed state in both conditions. Additionally, we measured participants' subjective experiences of the BCI control in terms of satisfaction. Our results confirmed hypotheses regarding the offline condition. In accordance with the hypotheses, combining fNIRS and HMD technologies enables the effective transfer of experimental cognitive procedures to a controlled VR environment. This opens the new possibility of creating more ecologically valid studies and training procedures.

RevDate: 2022-05-19

Rubin DB, Hosman T, Kelemen JN, et al (2022)

Learned motor patterns are replayed in human motor cortex during sleep.

The Journal of neuroscience : the official journal of the Society for Neuroscience pii:JNEUROSCI.2074-21.2022 [Epub ahead of print].

Consolidation of memory is believed to involve offline replay of neural activity. While amply demonstrated in rodents, evidence for replay in humans, particularly regarding motor memory, is less compelling. To determine whether replay occurs after motor learning, we sought to record from motor cortex during a novel motor task and subsequent overnight sleep. A 36-year-old man with tetraplegia secondary to cervical spinal cord injury enrolled in the ongoing BrainGate brain-computer interface pilot clinical trial had two 96-channel intracortical microelectrode arrays placed chronically into left pre-central gyrus (PCG). Single- and multi-unit activity was recorded while he played a color/sound sequence matching memory game. Intended movements were decoded from motor cortical neuronal activity by a real-time steady-state Kalman filter that allowed the participant to control a neurally driven cursor on the screen. Intracortical neural activity from PCG and 2-lead scalp EEG were recorded overnight as he slept. When decoded using the same steady-state Kalman filter parameters, intracortical neural signals recorded overnight replayed the target sequence from the memory game at intervals throughout at a frequency significantly greater than expected by chance. Replay events occurred at speeds ranging from one to four times as fast as initial task execution and were most frequently observed during slow-wave sleep. These results demonstrate that recent visuomotor skill acquisition in humans may be accompanied by replay of the corresponding motor cortex neural activity during sleep.Significance Statement:Within cortex, the acquisition of information is often followed by the offline recapitulation of specific sequences of neural firing. Replay of recent activity is enriched during sleep and may support the consolidation of learning and memory. Using an intracortical brain computer interface (iBCI), we recorded and decoded activity from motor cortex as a human research participant performed a novel motor task. By decoding neural activity throughout subsequent sleep, we find that neural sequences underlying the recently practiced motor task are repeated throughout the night, providing direct evidence of replay in human motor cortex during sleep. This approach, using an optimized BCI decoder to characterize neural activity during sleep, provides a framework for future studies exploring replay, learning, and memory.

RevDate: 2022-05-19

Gao Y, Sun X, Meng M, et al (2022)

EEG emotion recognition based on enhanced SPD matrix and manifold dimensionality reduction.

Computers in biology and medicine, 146:105606 pii:S0010-4825(22)00398-5 [Epub ahead of print].

Recently, Riemannian geometry-based pattern recognition has been widely employed to brain computer interface (BCI) researches, providing new idea for emotion recognition based on electroencephalogram (EEG) signals. Although the symmetric positive definite (SPD) matrix manifold constructed from the traditional covariance matrix contains large amount of spatial information, these methods do not perform well to classify and recognize emotions, and the high dimensionality problem still unsolved. Therefore, this paper proposes a new strategy for EEG emotion recognition utilizing Riemannian geometry with the aim of achieving better classification performance. The emotional EEG signals of 32 healthy subjects were from an open-source dataset (DEAP). The wavelet packets were first applied to extract the time-frequency features of the EEG signals, and then the features were used to construct the enhanced SPD matrix. A supervised dimensionality reduction algorithm was then designed on the Riemannian manifold to reduce the high dimensionality of the SPD matrices, gather samples of the same labels together, and separate samples of different labels as much as possible. Finally, the samples were mapped to the tangent space, and the K-nearest neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM) method were employed for classification. The proposed method achieved an average accuracy of 91.86%, 91.84% on the valence and arousal recognition tasks. Furthermore, we also obtained the superior accuracy of 86.71% on the four-class recognition task, demonstrated the superiority over state-of-the-art emotion recognition methods.

RevDate: 2022-05-19

Qian S, Kumar P, FD Testai (2022)

Bilirubin Encephalopathy.

Current neurology and neuroscience reports [Epub ahead of print].

PURPOSE OF REVIEW: Hyperbilirubinemia is commonly seen in neonates. Though hyperbilirubinemia is typically asymptomatic, severe elevation of bilirubin levels can lead to acute bilirubin encephalopathy and progress to kernicterus spectrum disorder, a chronic condition characterized by hearing loss, extrapyramidal dysfunction, ophthalmoplegia, and enamel hypoplasia. Epidemiological data show that the implementation of universal pre-discharge bilirubin screening programs has reduced the rates of hyperbilirubinemia-associated complications. However, acute bilirubin encephalopathy and kernicterus spectrum disorder are still particularly common in low- and middle-income countries.

RECENT FINDINGS: The understanding of the genetic and biochemical processes that increase the susceptibility of defined anatomical areas of the central nervous system to the deleterious effects of bilirubin may facilitate the development of effective treatments for acute bilirubin encephalopathy and kernicterus spectrum disorder. Scoring systems are available for the diagnosis and severity grading of these conditions. The treatment of hyperbilirubinemia in newborns relies on the use of phototherapy and exchange transfusion. However, novel therapeutic options including deep brain stimulation, brain-computer interface, and stem cell transplantation may alleviate the heavy disease burden associated with kernicterus spectrum disorder. Despite improved screening and treatment options, the prevalence of acute bilirubin encephalopathy and kernicterus spectrum disorder remains elevated in low- and middle-income countries. The continued presence and associated long-term disability of these conditions warrant further research to improve their prevention and management.

RevDate: 2022-05-19
CmpDate: 2022-05-19

Soni S, Seal A, Yazidi A, et al (2022)

Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression.

Computers in biology and medicine, 145:105420.

Depression is a major depressive disorder characterized by persistent sadness and a sense of worthlessness, as well as a loss of interest in pleasurable activities, which leads to a variety of physical and emotional problems. It is a worldwide illness that affects millions of people and should be detected at an early stage to prevent negative effects on an individual's life. Electroencephalogram (EEG) is a non-invasive technique for detecting depression that analyses brain signals to determine the current mental state of depressed subjects. In this study, we propose a method for automatic feature extraction to detect depression by first constructing a graph from the dataset where the nodes represent the subjects in the dataset and where the edge weights obtained using the Euclidean distance reflect the relationship between them. The Node2vec algorithmic framework is then used to compute feature representations for nodes in a graph in the form of node embeddings ensuring that similar nodes in the graph remain near in the embedding. These node embeddings act as useful features which can be directly used by classification algorithms to determine whether a subject is depressed thus reducing the effort required for manual handcrafted feature extraction. To combine the features collected from the multiple channels of the EEG data, the method proposes three types of fusion methods: graph-level fusion, feature-level fusion, and decision-level fusion. The proposed method is tested on three publicly available datasets with 3, 20, and 128 channels, respectively, and compared to five state-of-the-art methods. The results show that the proposed method detects depression effectively with a peak accuracy of 0.933 in decision-level fusion, which is the highest among the state-of-the-art methods.

RevDate: 2022-05-18

Xiao J, He Y, Yu T, et al (2022)

Towards Assessment of Sound Localization in Disorders of Consciousness Using a Hybrid Audiovisual Brain-Computer Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Behavioral assessment of sound localization in the Coma Recovery Scale-Revised (CRS-R) poses a significant challenge due to motor disability in patients with disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which can directly detect brain activities related to external stimuli, may thus provide an approach to assess DOC patients without the need for any physical behavior. In this study, a novel audiovisual BCI system was developed to simulate sound localization evaluation in CRS-R. Specifically, there were two alternatively flashed buttons on the left and right sides of the graphical user interface, one of which was randomly chosen as the target. The auditory stimuli of bell sounds were simultaneously presented by the ipsilateral loudspeaker during the flashing of the target button, which prompted patients to selectively attend to the target button. The recorded electroencephalography data were analyzed in real time to detect event-related potentials evoked by the target and further to determine whether the target was attended to or not. A significant BCI accuracy for a patient implied that he/she had sound localization. Among eighteen patients, eleven and four showed sound localization in the BCI and CRS-R, respectively. Furthermore, all patients showing sound localization in the CRS-R were among those detected by our BCI. The other seven patients who had no sound localization behavior in CRS-R were identified by the BCI assessment, and three of them showed improvements in the second CRS-R assessment after the BCI experiment. Thus, the proposed BCI system is promising for assisting the assessment of sound localization and improving the clinical diagnosis of DOC patients.

RevDate: 2022-05-17

Oliveira LJC, Amorim LC, Megid TBC, et al (2022)

Gene expression signatures in early Breast Cancer: better together with clinicopathological features.

Critical reviews in oncology/hematology pii:S1040-8428(22)00132-9 [Epub ahead of print].

Breast cancer (BC) is a highly heterogeneous disease, characterized by a variety of subtypes with distinct biological, molecular, and clinical behavior. Standard clinicopathological and tumor biology information (as assessed by gene expression signatures-GES), have provided enhanced prognostic and predictive information in both node-negative(N0) and positive(N+), hormonal receptor positive/human epidermal growth factor 2 negative (HR+/HER2-) early breast cancer (EBC). Herein, we comprehensively review the clinical data of 5 commonly used GES, namely, Oncotype DX(ODX)®; MammaPrint (MP)®; Prosigna®; Breast Cancer Index (BCI)® and Endopredict® - with sections specifically addressing the role of GES in special histologic subtypes, premenopausal women, late recurrence and adjuvant treatment de-escalation.

RevDate: 2022-05-17

Lei Y, Fei P, Song B, et al (2022)

A loosened gating mechanism of RIG-I leads to autoimmune disorders.

Nucleic acids research pii:6586872 [Epub ahead of print].

DDX58 encodes RIG-I, a cytosolic RNA sensor that ensures immune surveillance of nonself RNAs. Individuals with RIG-IE510V and RIG-IQ517H mutations have increased susceptibility to Singleton-Merten syndrome (SMS) defects, resulting in tissue-specific (mild) and classic (severe) phenotypes. The coupling between RNA recognition and conformational changes is central to RIG-I RNA proofreading, but the molecular determinants leading to dissociated disease phenotypes remain unknown. Herein, we employed hydrogen/deuterium exchange mass spectrometry (HDX-MS) and single molecule magnetic tweezers (MT) to precisely examine how subtle conformational changes in the helicase insertion domain (HEL2i) promote impaired ATPase and erroneous RNA proofreading activities. We showed that the mutations cause a loosened latch-gate engagement in apo RIG-I, which in turn gradually dampens its self RNA (Cap2 moiety:m7G cap and N1-2-2'-O-methylation RNA) proofreading ability, leading to increased immunopathy. These results reveal HEL2i as a unique checkpoint directing two specialized functions, i.e. stabilizing the CARD2-HEL2i interface and gating the helicase from incoming self RNAs; thus, these findings add new insights into the role of HEL2i in the control of antiviral innate immunity and autoimmunity diseases.

RevDate: 2022-05-17
CmpDate: 2022-05-17

Liza K, S Ray (2022)

Local Interactions between Steady-State Visually Evoked Potentials at Nearby Flickering Frequencies.

The Journal of neuroscience : the official journal of the Society for Neuroscience, 42(19):3965-3974.

Steady-state visually evoked potentials (SSVEPs) are widely used to index top-down cognitive processing in human electroencephalogram (EEG) studies. Typically, two stimuli flickering at different temporal frequencies (TFs) are presented, each producing a distinct response in the EEG at its flicker frequency. However, how SSVEP responses in EEGs are modulated in the presence of a competing flickering stimulus just because of sensory interactions is not well understood. We have previously shown in local field potentials (LFPs) recorded from awake monkeys that when two overlapping full-screen gratings are counterphased at different TFs, there is an asymmetric SSVEP response suppression, with greater suppression from lower TFs, which further depends on the relative orientations of the gratings (stronger suppression and asymmetry for parallel compared with orthogonal gratings). Here, we first confirmed these effects in both male and female human EEG recordings. Then, we mapped the response suppression of one stimulus (target) by a competing stimulus (mask) over a much wider range than the previous study. Surprisingly, we found that the suppression was not stronger at low frequencies in general, but systematically varied depending on the target TF, indicating local interactions between the two competing stimuli. These results were confirmed in both human EEG and monkey LFP and electrocorticogram (ECoG) data. Our results show that sensory interactions between multiple SSVEPs are more complex than shown previously and are influenced by both local and global factors, underscoring the need to cautiously interpret the results of studies involving SSVEP paradigms.SIGNIFICANCE STATEMENT Steady-state visually evoked potentials (SSVEPs) are extensively used in human cognitive studies and brain-computer interfacing applications where multiple stimuli flickering at distinct frequencies are concurrently presented in the visual field. We recently characterized interactions between competing flickering stimuli in animal recordings and found that stimuli flickering slowly produce larger suppression. Here, we confirmed these in human EEGs, and further characterized the interactions by using a much wider range of target and competing (mask) frequencies in both human EEGs and invasive animal recordings. These revealed a new "local" component, whereby the suppression increased when competing stimuli flickered at nearby frequencies. Our results highlight the complexity of sensory interactions among multiple SSVEPs and underscore the need to cautiously interpret studies involving SSVEP paradigms.

RevDate: 2022-05-16

Flint RD, Li Y, Wang P, et al (2022)

Noninvasively recorded high-gamma signals improve synchrony of force feedback in a novel neurorehabilitation brain-machine interface for brain injury.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Brain injury is the leading cause of long-term disability worldwide, often resulting in impaired hand function. Brain-machine interfaces (BMIs) offer a potential way to improve hand function. BMIs often target replacing lost function, but may also be employed in neurorehabilitation (nrBMI) by facilitating neural plasticity and functional recovery. Here, we report a novel nrBMI capable of acquiring high-γ (70-115 Hz) information through a unique post-TBI hemicraniectomy window model, and delivering sensory feedback that is synchronized with, and proportional to, intended grasp force.

APPROACH: We developed the nrBMI to use electroencephalogram recorded over a hemicraniectomy (hEEG) in individuals with traumatic brain injury (TBI). The nrBMI empowered users to exert continuous, proportional control of applied force, and provided continuous force feedback. We report the results of an initial testing group of three human participants with TBI, along with a control group of three skull- and motor-intact volunteers.

MAIN RESULTS: All participants controlled the nrBMI successfully, with high initial success rates (2 of 6 participants) or performance that improved over time (4 of 6 participants). We observed high-γ modulation with force intent in hEEG but not skull-intact EEG. Most significantly, we found that high-γ control significantly improved the timing synchronization between neural modulation onset and nrBMI output/haptic feedback (compared to low-frequency nrBMI control).

SIGNIFICANCE: These proof-of-concept results show that high-γ nrBMIs can be used by individuals with impaired ability to control force (without immediately resorting to invasive signals like ECoG). Of note, the nrBMI includes a parameter to change the fraction of control shared between decoded intent and volitional force, to adjust for recovery progress. The improved synchrony between neural modulations and force control for high-γ signals is potentially important for maximizing the ability of nrBMIs to induce plasticity in neural circuits. Inducing plasticity is critical to functional recovery after brain injury.

RevDate: 2022-05-16

Zhou Y, Yang B, C Guan (2022)

Task-Related Component Analysis Combining Paired Character Decoding for Miniature Asymmetric Visual Evoked Potentials.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Brain-computer interface (BCI) technology based on event-related potentials (ERP) of electroencephalography (EEG) is widely used in daily life and medical treatment. However, the research of identifying the miniature and more informative asymmetric visual evoked potentials (aVEPs), which belongs to ERP, needs further exploration. Herein, a task-related component analysis combining paired character decoding (TRCA-PCD) method, which can enhance reproducibility of aVEPs in multiple trials and strengthen the features of different samples, was designed to realize fast decoding of aVEPs. The BCI performance and the influence of repetition times between the TRCA-PCD method, the discriminative canonical pattern matching (DCPM) method and traditional task-related component analysis (TRCA) method were compared using a 32-class aVEPs dataset recorded from 32 subjects. The highest average recognition accuracy and information transfer rate (ITR) of TRCA-PCD after parameter selection were 70.37 ± 2.49% (DCPM: 64.91 ± 2.81%, TRCA: 44.01 ± 3.25%) with the peak value of 97.92% and 28.90 ± 3.83 bits/min (DCPM: 21.29 ± 3.35 bits/min, TRCA: 11.54 ± 2.81 bits/min) with the peak value of 94.55 bits/min respectively. Statistical analysis indicated that the highest average recognition rate could be obtained when the repetition time was six, and the highest ITR could be obtained when the repetition time was one. Overall, the results verified the effectiveness and superiority of TRCA-PCD in recognition of aVEPs and provided a reference for parameter selection. Therefore, the TRCA-PCD method can promote the further application of aVEPs in the BCI speller field.

RevDate: 2022-05-16

Loizidou P, Rios E, Marttini A, et al (2022)

Extending Brain-Computer Interface Access with a Multilingual Language Model in the P300 Speller.

Brain computer interfaces (Abingdon, England), 9(1):36-48.

Brain-computer interfaces (BCI) such as the P300 speller have the potential to restore communication to advanced-stage neuromuscular disease patients. Research has improved typing speed and accuracy through innovations including the use of language models. While significant advances have been made, implementations have largely been restricted to a single language, primarily English. It is unclear whether these improvements would extend to other languages that present potential technical hurdles due to different alphabets and grammatical structures. Here, we adapt a language model-based classifier designed for English to two other languages, Spanish and Greek, to demonstrate the generalizability of these methods. Online experimental trials with 30 healthy native English, Spanish, and Greek speakers showed no significant difference between performances using the different versions of the system (66.20 vs. 61.97 vs. 60.89 bits/minute). Extending these methods across languages allows for expanding access to BCI systems to other populations, particularly in the developing world.

RevDate: 2022-05-16

Fan C, Hu J, Huang S, et al (2022)

EEG-TNet: An End-To-End Brain Computer Interface Framework for Mental Workload Estimation.

Frontiers in neuroscience, 16:869522.

The mental workload (MWL) of different occupational groups' workers is the main and direct factor of unsafe behavior, which may cause serious accidents. One of the new and useful technologies to estimate MWL is the Brain computer interface (BCI) based on EEG signals, which is regarded as the gold standard of cognitive status. However, estimation systems involving handcrafted EEG features are time-consuming and unsuitable to apply in real-time. The purpose of this study was to propose an end-to-end BCI framework for MWL estimation. First, a new automated data preprocessing method was proposed to remove the artifact without human interference. Then a new neural network structure named EEG-TNet was designed to extract both the temporal and frequency information from the original EEG. Furthermore, two types of experiments and ablation studies were performed to prove the effectiveness of this model. In the subject-dependent experiment, the estimation accuracy of dual-task estimation (No task vs. TASK) and triple-task estimation (Lo vs. Mi vs. Hi) reached 99.82 and 99.21%, respectively. In contrast, the accuracy of different tasks reached 82.78 and 66.83% in subject-independent experiments. Additionally, the ablation studies proved that preprocessing method and network structure had significant contributions to estimation MWL. The proposed method is convenient without any human intervention and outperforms other related studies, which becomes an effective way to reduce human factor risks.

RevDate: 2022-05-16

Vasko JL, Aume L, Tamrakar S, et al (2022)

Increasing Robustness of Brain-Computer Interfaces Through Automatic Detection and Removal of Corrupted Input Signals.

Frontiers in neuroscience, 16:858377.

For brain-computer interfaces (BCIs) to be viable for long-term daily usage, they must be able to quickly identify and adapt to signal disruptions. Furthermore, the detection and mitigation steps need to occur automatically and without the need for user intervention while also being computationally tractable for the low-power hardware that will be used in a deployed BCI system. Here, we focus on disruptions that are likely to occur during chronic use that cause some recording channels to fail but leave the remaining channels unaffected. In these cases, the algorithm that translates recorded neural activity into actions, the neural decoder, should seamlessly identify and adjust to the altered neural signals with minimal inconvenience to the user. First, we introduce an adapted statistical process control (SPC) method that automatically identifies disrupted channels so that both decoding algorithms can be adjusted, and technicians can be alerted. Next, after identifying corrupted channels, we demonstrate the automated and rapid removal of channels from a neural network decoder using a masking approach that does not change the decoding architecture, making it amenable for transfer learning. Finally, using transfer and unsupervised learning techniques, we update the model weights to adjust for the corrupted channels without requiring the user to collect additional calibration data. We demonstrate with both real and simulated neural data that our approach can maintain high-performance while simultaneously minimizing computation time and data storage requirements. This framework is invisible to the user but can dramatically increase BCI robustness and usability.

RevDate: 2022-05-16

Li L, N Sun (2022)

Attention-Based DSC-ConvLSTM for Multiclass Motor Imagery Classification.

Computational intelligence and neuroscience, 2022:8187009.

With the rapid development of deep learning, researchers have gradually applied it to motor imagery brain computer interface (MI-BCI) and initially demonstrated its advantages over traditional machine learning. However, its application still faces many challenges, and the recognition rate of electroencephalogram (EEG) is still the bottleneck restricting the development of MI-BCI. In order to improve the accuracy of EEG classification, a DSC-ConvLSTM model based on the attention mechanism is proposed for the multi-classification of motor imagery EEG signals. To address the problem of the small sample size of well-labeled and accurate EEG data, the preprocessing uses sliding windows for data augmentation, and the average prediction loss of each sliding window is used as the final prediction loss for that trial. This not only increases the training sample size and is beneficial to train complex neural network models, but also the network no longer extracts the global features of the whole trial so as to avoid learning the difference features among trials, which can effectively eliminate the influence of individual specificity. In the aspect of feature extraction and classification, the overall network structure is designed according to the characteristics of the EEG signals in this paper. Firstly, depth separable convolution (DSC) is used to extract spatial features of EEG signals. On the one hand, this reduces the number of parameters and improves the response speed of the system. On the other hand, the network structure we designed is more conducive to extract directly the direct extraction of spatial features of EEG signals. Secondly, the internal structure of the Long Short-Term Memory (LSTM) unit is improved by using convolution and attention mechanism, and a novel bidirectional convolution LSTM (ConvLSTM) structure is proposed by comparing the effects of embedding convolution and attention mechanism in the input and different gates, respectively. In the ConvLSTM module, the convolutional structure is only introduced into the input-to-state transition, while the gates still remain the original fully connected mechanism, and the attention mechanism is introduced into the input to further improve the overall decoding performance of the model. This bidirectional ConvLSTM extracts the time-domain features of EEG signals and integrates the feature extraction capability of the CNN and the sequence processing capability of LSTM. The experimental results show that the average classification accuracy of the model reaches 73.7% and 92.6% on two datasets, BCI Competition IV Dataset 2a and High Gamma Dataset, respectively, which proves the robustness and effectiveness of the model we proposed. It can be seen that the model in this paper can deeply excavate significant EEG features from the original EEG signals, show good performance in different subjects and different datasets, and improve the influence of individual variability on the classification performance, which is of practical significance for promoting the development of brain-computer interface technology towards a practical and marketable direction.

RevDate: 2022-05-16

Zeng C, J Zhang (2022)

A narrative review of five multigenetic assays in breast cancer.

Translational cancer research, 11(4):897-907.

Background and Objective: Breast cancer is a highly heterogeneous disease. Its incidence rate is increasing year by year and the mortality rate is the highest in female malignant tumors. Even patients with the same clinical stage and pathological grade have different response to treatment and postoperative recurrence risk. Although the prognosis of breast cancer in China has been gradually improved, there is still a certain gap compared with the 5-year survival rate as high as 89% in developed countries. In recent years, with the continuous enrichment of molecular sequencing data of breast cancer, gene detection technology has important reference value in prognosis judgement and guiding treatment of early breast cancer. This article reviews the current application and latest progress of genetic tests in comprehensive treatment for breast cancer, with a view to promote the precise treatment of breast cancer in clinical practice.

Methods: We conducted searches using the MeSH terms 'breast neoplasms' and 'genetic testing' in the PubMed databases from root to 22 January 2021. We conducted an additional search in the National Comprehensive Cancer Network (NCCN) and American Society of Clinical Oncology (ASCO) guidelines to obtain additional information. The search was limited to English, Dutch, French and German articles and research involving humans. Out of the references screened, 51 articles were found eligible for inclusion finally.

Key Content and Findings: The article reviews the mechanisms and clinical trials of five genetic tests including Oncotype Dx, Mammaprint, Endopredict, mRNA expression of 50 genes (PAM50) and breast cancer index (BCI) in comprehensive treatment for breast cancer. All these tools have been proved to have prognosis value, but only two of them, Oncotype Dx and Mammaprint, are recommended as predictive tools for chemotherapy by National Comprehensive Cancer Network (NCCN) and the American Society of Clinical Oncology (ASCO).

Conclusions: In order to promote the comprehensive treatment of breast cancer to "precision" and "individualization" for further development, people have extensively researched on multigene testing technology represented by Oncotype Dx, Mammaprint, Endopredict and mRNA expression of 50 genes (PAM50) and breast cancer index (BCI). Each of these five tools has its advantages and limitation, which must be weighed in a wise application.

RevDate: 2022-05-15

Zarei A, B Mohammadzadeh Asl (2022)

Classification of code-modulated visual evoked potentials using adaptive modified covariance beamformer and EEG signals.

Computer methods and programs in biomedicine, 221:106859 pii:S0169-2607(22)00241-3 [Epub ahead of print].

OBJECTIVE: In general, brain computer interface (BCI) studies based on code-modulated Visual Evoked Potentials (c-VEP) use m-sequences to decode EEG responses to visual stimuli. BCI systems based on the c-VEP paradigm can simultaneously present a large number of commands, which results in a significantly high information transfer rate (ITR). Spatiotemporal beamforming (STB) is one of the commonly used approaches in c-VEP-based BCI systems.

APPROACH: In the current work, a novel STB-based technique is proposed to detect the gazed targets. The proposed method improves the performance of conventional STB-based techniques by providing a robust estimation of the covariance matrix in short stimulation times. Different user parameter-free methods, including the convex combination (CC), the general linear combination (GLC), and the modified versions of these techniques, are used to estimate a reliable and robust covariance matrix when a small number of repetitions are available.

MAIN RESULTS: The stimulus presentation rate of 120 Hz is used to assess the performance of the proposed structures. Our proposed methods improved the classification accuracy by an average of 20% compared to the conventional STB method at the shortest stimulation time. The proposed method achieves an average ITR of 157.07 bits/min by using only two repetitions of the m-sequences.

SIGNIFICANCE: The results show that our proposed methods perform significantly better than the conventional STB technique in all stimulation times.

RevDate: 2022-05-14

Wang J, Qian L, Wang S, et al (2022)

Directional Preference in Avian Midbrain Saliency Computing Nucleus Reflects a Well-Designed Receptive Field Structure.

Animals : an open access journal from MDPI, 12(9): pii:ani12091143.

Neurons responding sensitively to motions in several rather than all directions have been identified in many sensory systems. Although this directional preference has been demonstrated by previous studies to exist in the isthmi pars magnocellularis (Imc) of pigeon (Columba livia), which plays a key role in the midbrain saliency computing network, the dynamic response characteristics and the physiological basis underlying this phenomenon are unclear. Herein, dots moving in 16 directions and a biologically plausible computational model were used. We found that pigeon Imc's significant responses for objects moving in preferred directions benefit the long response duration and high instantaneous firing rate. Furthermore, the receptive field structures predicted by a computational model, which captures the actual directional tuning curves, agree with the real data collected from population Imc units. These results suggested that directional preference in Imc may be internally prebuilt by elongating the vertical axis of the receptive field, making predators attack from the dorsal-ventral direction and conspecifics flying away in the ventral-dorsal direction, more salient for avians, which is of great ecological and physiological significance for survival.

RevDate: 2022-05-13

Lopez-Bernal D, Balderas D, Ponce P, et al (2022)

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding.

Frontiers in human neuroscience, 16:867281.

Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.

RevDate: 2022-05-13

He W, Yang J, Gao M, et al (2022)

Pelvic reconstruction and lateral prostate capsule sparing techniques improve early continence of robot-assisted radical cystectomy with orthotopic ileal neobladder.

International urology and nephrology [Epub ahead of print].

PURPOSE: To evaluate urinary outcomes of pelvic construction and lateral capsule sparing techniques in robot-assisted radical cystectomy with orthotopic ileal neobladder (RARC-OIN).

METHODS: A total of 107 male patients who underwent RARC-OIN during January 2017 and February 2021 in Sun Yat-sen Memorial Hospital were analyzed retrospectively. Standard RARC-OIN with or without nerve sparing technique was performed in 44 patients (standard group), lateral prostate capsule sparing technique was performed in 20 patients (LCS group), combined pelvic reconstruction (CPR) technique including anterior suspension and posterior reconstruction were performed in 43 patients (CPR group). The urinary function was assessed by the use of pads and the Bladder Cancer Index (BCI). Continence was defined as the use of 0-1 pad during daytime or night-time.

RESULTS: There was no statistical difference between the three groups regarding demographic, perioperative, and pathological data. Continence rates were 6.8, 50.0 and 34.9% for daytime, 4.6, 40.0 and 32.6% for night-time in the standard group, LCS group and CPR group at 1 month post-operation, respectively. Continence rates were 34.1, 80.0 and 69.8% for daytime, 27.3, 75.0 and 65.1% for night-time in the standard group, LCS group and CPR group at 3 month post-operation, respectively. No statistically significant difference was observed in the daytime and night-time continence rates at 12 months.

CONCLUSIONS: Lateral capsule-sparing and combined pelvic reconstruction techniques are feasible to improve early daytime and night-time continence rates in RARC with orthotopic neobladder.

CLINICAL TRIAL REGISTRATION: The trial registration number: ChiCTR2100047606.

RevDate: 2022-05-13

Dag I, Dui LG, Ferrante S, et al (2022)

Leveraging Deep Learning Techniques to Improve P300-Based Brain Computer Interfaces.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular protocols for BCI is based on the extraction of the so-called P300 wave from electroencephalography (EEG) recordings. P300 wave is an event-related potential with a latency of 300 ms after the onset of a rare stimulus. In this paper, we used deep learning architectures, namely convolutional neural networks (CNNs), to improve P300-based BCIs. We propose a novel BCI classifier, called P3CNET, that improved P300 classification accuracy performances of the best state-of-the-art classifier. In addition, we explored pre-processing and training choices that improved the usability of BCI systems. For the pre-processing of EEG data, we explored the optimal signal interval that would improve classification accuracies. Then, we explored the minimum number of calibration sessions to balance higher accuracy and shorter calibration time. To improve the explainability of deep learning architectures, we analyzed the saliency maps of the input EEG signal leading to a correct P300 classification, and we observed that the elimination of less informative electrode channels from the data did not result in better accuracy. All the methodologies and explorations were performed and validated on two different CNN classifiers, demonstrating the generalizability of the obtained results. Finally, we showed the advantages given by transfer learning when using the proposed novel architecture on other P300 datasets. The presented architectures and practical suggestions can be used by BCI practitioners to improve its effectiveness.

RevDate: 2022-05-13

Lubianiker N, Paret C, Dayan P, et al (2022)

Neurofeedback through the lens of reinforcement learning.

Trends in neurosciences pii:S0166-2236(22)00059-5 [Epub ahead of print].

Despite decades of experimental and clinical practice, the neuropsychological mechanisms underlying neurofeedback (NF) training remain obscure. NF is a unique form of reinforcement learning (RL) task, during which participants are provided with rewarding feedback regarding desired changes in neural patterns. However, key RL considerations - including choices during practice, prediction errors, credit-assignment problems, or the exploration-exploitation tradeoff - have infrequently been considered in the context of NF. We offer an RL-based framework for NF, describing different internal states, actions, and rewards in common NF protocols, thus fashioning new proposals for characterizing, predicting, and hastening the course of learning. In this way we hope to advance current understanding of neural regulation via NF, and ultimately to promote its effectiveness, personalization, and clinical utility.

RevDate: 2022-05-13

Kumar A, Gao L, Li J, et al (2022)

Error-Related Negativity-Based Robot-Assisted Stroke Rehabilitation System: Design and Proof-of-Concept.

Frontiers in neurorobotics, 16:837119.

Conventional rehabilitation systems typically execute a fixed set of programs that most motor-impaired stroke patients undergo. In these systems, the brain, which is embodied in the body, is often left out. Including the brains of stroke patients in the control loop of a rehabilitation system can be worthwhile as the system can be tailored to each participant and, thus, be more effective. Here, we propose a novel brain-computer interface (BCI)-based robot-assisted stroke rehabilitation system (RASRS), which takes inputs from the patient's intrinsic feedback mechanism to adapt the assistance level of the RASRS. The proposed system will utilize the patients' consciousness about their performance decoded through their error-related negativity signals. As a proof-of-concept, we experimented on 12 healthy people in which we recorded their electroencephalogram (EEG) signals while performing a standard rehabilitation exercise. We set the performance requirements beforehand and observed participants' neural responses when they failed/met the set requirements and found a statistically significant (p < 0.05) difference in their neural responses in the two conditions. The feasibility of the proposed BCI-based RASRS was demonstrated through a use-case description with a timing diagram and meeting the crucial requirements for developing the proposed rehabilitation system. The use of a patient's intrinsic feedback mechanism will have significant implications for the development of human-in-the-loop stroke rehabilitation systems.

RevDate: 2022-05-13

Zhang Y, Lu S, Huang S, et al (2022)

Optic chiasmatic potential by endoscopically implanted skull base microinvasive biosensor: a brain-machine interface approach for anterior visual pathway assessment.

Theranostics, 12(7):3273-3287 pii:thnov12p3273.

Background: Visually evoked potential (VEP) is widely used to detect optic neuropathy in basic research and clinical practice. Traditionally, VEP is recorded non-invasively from the surface of the skull over the visual cortex. However, its trace amplitude is highly variable, largely due to intracranial modulation and artifacts. Therefore, a safe test with a strong and stable signal is highly desirable to assess optic nerve function, particularly in neurosurgical settings and animal experiments. Methods: Minimally invasive trans-sphenoidal endoscopic recording of optic chiasmatic potential (OCP) was carried out with a titanium screw implanted onto the sphenoid bone beneath the optic chiasm in the goat, whose sphenoidal anatomy is more human-like than non-human primates. Results: The implantation procedure was swift (within 30 min) and did not cause any detectable abnormality in fetching/moving behaviors, skull CT scans and ophthalmic tests after surgery. Compared with traditional VEP, the amplitude of OCP was 5-10 times stronger, more sensitive to weak light stimulus and its subtle changes, and was more repeatable, even under extremely low general anesthesia. Moreover, the OCP signal relied on ipsilateral light stimulation, and was abolished immediately after complete optic nerve (ON) transection. Through proof-of-concept experiments, we demonstrated several potential applications of the OCP device: (1) real-time detector of ON function, (2) detector of region-biased retinal sensitivity, and (3) therapeutic electrical stimulator for the optic nerve with low and thus safe excitation threshold. Conclusions: OCP developed in this study will be valuable for both vision research and clinical practice. This study also provides a safe endoscopic approach to implant skull base brain-machine interface, and a feasible in vivo testbed (goat) for evaluating safety and efficacy of skull base brain-machine interface.

RevDate: 2022-05-13

Yang J, Liu L, Yu H, et al (2022)

Multi-Hierarchical Fusion to Capture the Latent Invariance for Calibration-Free Brain-Computer Interfaces.

Frontiers in neuroscience, 16:824471.

Brain-computer interfaces (BCI) based motor imagery (MI) has become a research hotspot for establishing a flexible communication channel for patients with apoplexy or degenerative pathologies. Accurate decoding of motor imagery electroencephalography (MI-EEG) signals, while essential for effective BCI systems, is still challenging due to the significant noise inherent in the EEG signals and the lack of informative correlation between the signals and brain activities. The application of deep learning for EEG feature representation has been rarely investigated, nevertheless bringing improvements to the performance of motor imagery classification. This paper proposes a deep learning decoding method based on multi-hierarchical representation fusion (MHRF) on MI-EEG. It consists of a concurrent framework constructed of bidirectional LSTM (Bi-LSTM) and convolutional neural network (CNN) to fully capture the contextual correlations of MI-EEG and the spectral feature. Also, the stacked sparse autoencoder (SSAE) is employed to concentrate these two domain features into a high-level representation for cross-session and subject training guidance. The experimental analysis demonstrated the efficacy and practicality of the proposed approach using a public dataset from BCI competition IV and a private one collected by our MI task. The proposed approach can serve as a robust and competitive method to improve inter-session and inter-subject transferability, adding anticipation and prospective thoughts to the practical implementation of a calibration-free BCI system.

RevDate: 2022-05-13

Shishkin SL (2022)

Active Brain-Computer Interfacing for Healthy Users.

Frontiers in neuroscience, 16:859887.

RevDate: 2022-05-10
CmpDate: 2022-05-10

Campos-Arteaga G, Araneda A, Ruiz S, et al (2022)

Classifying brain states and pupillary responses associated with the processing of old and new information.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology, 176:129-141.

Memory retrieval of consolidated memories has been extensively studied using "old-new tasks", meaning tasks in which participants are instructed to discriminate between stimuli they have experienced before and new ones. Significant differences in the neural processing of old and new elements have been demonstrated using different techniques, such as electroencephalography and pupillometry. In this work, using the data from a previously published study (Campos-Arteaga, Forcato et al. 2020), we investigated whether machine learning methods can classify, based on single trials, the brain activity and pupil responses associated with the processing of old and new information. Specifically, we used the EEG and pupillary information of 39 participants who completed an associative recall old-new task in which they had to discriminate between previously seen or new pictures and, for the old ones, to recall an associated word. Our analyses corroborated the differences in neural processing of old and new items reported in previous studies. Based on these results, we hypothesized that the application of machine learning methods would allow an optimal classification of old and new conditions. Using a Windowed Means approach (WM) and two different machine learning algorithms - Logistic Regression (WM-LR) and Linear Discriminant Analysis (WM-LDA) - mean classification performances of 0.75 and 0.74 (AUC) were achieved when EEG and pupillary signals were combined to train the models, respectively. In both cases, when the EEG and pupillary data were merged, the performance was significantly better than when they were used separately. In addition, our results showed similar classification performances when fused classification models (i.e., models created with the concatenated information of 38 participants) were applied to individuals whose EEG and pupillary information was not considered for the model training. Similar results were found when alternative preprocessing methods were used. Taken together, these findings show that it is possible to classify the neurophysiological activity associated with the processing of experienced and new stimuli using machine learning techniques. Future research is needed to determine how this knowledge might have potential implications for memory research and clinical practice.

RevDate: 2022-05-09

Andersen RA, T Aflalo (2022)

Preserved cortical somatotopic and motor representations in tetraplegic humans.

Current opinion in neurobiology, 74:102547 pii:S0959-4388(22)00041-1 [Epub ahead of print].

A rich literature has documented changes in cortical representations of the body in somatosensory and motor cortex. Recent clinical studies of brain-machine interfaces designed to assist paralyzed patients have afforded the opportunity to record from and stimulate human somatosensory, motor, and action-related areas of the posterior parietal cortex. These studies show considerable preserved structure in the cortical somato-motor system. Motor cortex can immediately control assistive devices, stimulation of somatosensory cortex produces sensations in an orderly somatotopic map, and the posterior parietal cortex shows a high-dimensional representation of cognitive action variables. These results are strikingly similar to what would be expected in a healthy subject, demonstrating considerable stability of adult cortex even after severe injury and despite potential plasticity-induced new activations within the same region of cortex. Clinically, these results emphasize the importance of targeting cortical areas for BMI control signals that are consistent with their normal functional role.

RevDate: 2022-05-09

Szlawski J, Feleppa T, Mohan A, et al (2022)

A model for assessing the electromagnetic safety of an inductively coupled, modular brain-machine interface (May 2022).

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Brain-Machine Interfaces (BMI) offer the potential to modulate dysfunctional neurological networks by electrically stimulating the cerebral cortex via chronically-implanted microelectrodes. Wireless transmitters worn by BMI recipients must operate within electromagnetic emission and tissue heating limits, such as those prescribed by the IEEE and International Commission on Non-Ionizing Radiation Protection (ICNIRP), to ensure that radiofrequency emissions of BMI systems are safe. Here, we describe an approach to generating pre-compliance safety data by simulating the Specific Absorption Rate (SAR) and tissue heating of a multi-layered human head model containing a system of wireless, modular BMIs powered and controlled by an externally worn telemetry unit. We explore a number of system configurations such that our approach can be utilized for similar BMI systems, and our results provide a benchmark for the electromagnetic emissions of similar telemetry units. Our results show that the volume-averaged SAR per 10g of tissue exposed to our telemetry field complies with ICNIRP and IEEE reference levels, and that the maximum temperature increase in tissues was within permissible limits. These results were unaffected by the number of implants in the system model, and therefore we conclude that the electromagnetic emissions our BMI in any configuration are safe.

RevDate: 2022-05-09

Pancholi S, Giri A, Jain A, et al (2022)

Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.

IEEE transactions on cybernetics, PP: [Epub ahead of print].

The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation using exoskeleton/exosuit. For system development, the conventional classification-based brain-computer interface (BCI) controls external devices by providing discrete control signals to the actuator. A continuous kinematic reconstruction from EEG signal is better suited for practical BCI applications. The state-of-the-art multivariable linear regression (mLR) method provides a continuous estimate of hand kinematics, achieving a maximum correlation of up to 0.67 between the measured and the estimated hand trajectory. In this work, three novel source aware deep learning models are proposed for motion trajectory prediction (MTP). In particular, multilayer perceptron (MLP), convolutional neural network-long short-term memory (CNN-LSTM), and wavelet packet decomposition (WPD) for CNN-LSTM are presented. In addition, novelty in the work includes the utilization of brain source localization (BSL) [using standardized low-resolution brain electromagnetic tomography (sLORETA)] for the reliable decoding of motor intention. The information is utilized for channel selection and accurate EEG time segment selection. The performance of the proposed models is compared with the traditionally utilized mLR technique on the reach, grasp, and lift (GAL) dataset. The effectiveness of the proposed framework is established using the Pearson correlation coefficient (PCC) and trajectory analysis. A significant improvement in the correlation coefficient is observed when compared with the state-of-the-art mLR model. Our work bridges the gap between the control and the actuator block, enabling real-time BCI implementation.

RevDate: 2022-05-09

Zhu L, Hu Q, Yang J, et al (2021)

EEG Signal Classification Using Manifold Learning and Matrix-Variate Gaussian Model.

Computational intelligence and neuroscience, 2021:6668859.

In brain-computer interface (BCI), feature extraction is the key to the accuracy of recognition. There is important local structural information in the EEG signals, which is effective for classification; and this locality of EEG features not only exists in the spatial channel position but also exists in the frequency domain. In order to retain sufficient spatial structure and frequency information, we use one-versus-rest filter bank common spatial patterns (OVR-FBCSP) to preprocess the data and extract preliminary features. On this basis, we conduct research and discussion on feature extraction methods. One-dimensional feature extraction methods like linear discriminant analysis (LDA) may destroy this kind of structural information. Traditional manifold learning methods or two-dimensional feature extraction methods cannot extract both types of information at the same time. We introduced the bilinear structure and matrix-variate Gaussian model into two-dimensional discriminant locality preserving projection (2DDLPP) algorithm and decompose EEG signals into spatial and spectral parts. Afterwards, the most discriminative features were selected through a weight calculation method. We tested the method on BCI competition data sets 2a, data sets IIIa, and data sets collected by our laboratory, and the results were expressed in terms of recognition accuracy. The cross-validation results were 75.69%, 70.46%, and 54.49%, respectively. The average recognition accuracy of new method is improved by 7.14%, 7.38%, 4.86%, and 3.8% compared to those of LDA, two-dimensional linear discriminant analysis (2DLDA), discriminant locality property projections (DLPP), and 2DDLPP, respectively. Therefore, we consider that the proposed method is effective for EEG classification.

RevDate: 2022-05-09

Kondo T, Saito R, Sato Y, et al (2022)

Treadmill Training for Common Marmoset to Strengthen Corticospinal Connections After Thoracic Contusion Spinal Cord Injury.

Frontiers in cellular neuroscience, 16:858562.

Spinal cord injury (SCI) leads to locomotor dysfunction. Locomotor rehabilitation promotes the recovery of stepping ability in lower mammals, but it has limited efficacy in humans with a severe SCI. To explain this discrepancy between different species, a nonhuman primate rehabilitation model with a severe SCI would be useful. In this study, we developed a rehabilitation model of paraplegia caused by a severe traumatic SCI in a nonhuman primate, common marmoset (Callithrix jacchus). The locomotor rating scale for marmosets was developed to accurately assess the recovery of locomotor functions in marmosets. All animals showed flaccid paralysis of the hindlimb after a thoracic contusive SCI, but the trained group showed significant locomotor recovery. Kinematic analysis revealed significantly improved hindlimb stepping patterns in trained marmosets. Furthermore, intracortical microstimulation (ICMS) of the motor cortex evoked the hindlimb muscles in the trained group, suggesting the reconnection between supraspinal input and the lumbosacral network. Because rehabilitation may be combined with regenerative interventions such as medicine or cell therapy, this primate model can be used as a preclinical test of therapies that can be used in human clinical trials.

RevDate: 2022-05-09

Chandler JA, Van der Loos KI, Boehnke S, et al (2022)

Brain Computer Interfaces and Communication Disabilities: Ethical, Legal, and Social Aspects of Decoding Speech From the Brain.

Frontiers in human neuroscience, 16:841035.

A brain-computer interface technology that can decode the neural signals associated with attempted but unarticulated speech could offer a future efficient means of communication for people with severe motor impairments. Recent demonstrations have validated this approach. Here we assume that it will be possible in future to decode imagined (i.e., attempted but unarticulated) speech in people with severe motor impairments, and we consider the characteristics that could maximize the social utility of a BCI for communication. As a social interaction, communication involves the needs and goals of both speaker and listener, particularly in contexts that have significant potential consequences. We explore three high-consequence legal situations in which neurally-decoded speech could have implications: Testimony, where decoded speech is used as evidence; Consent and Capacity, where it may be used as a means of agency and participation such as consent to medical treatment; and Harm, where such communications may be networked or may cause harm to others. We then illustrate how design choices might impact the social and legal acceptability of these technologies.

RevDate: 2022-05-09

Klee D, Memmott T, Smedemark-Margulies N, et al (2022)

Target-Related Alpha Attenuation in a Brain-Computer Interface Rapid Serial Visual Presentation Calibration.

Frontiers in human neuroscience, 16:882557.

This study evaluated the feasibility of using occipitoparietal alpha activity to drive target/non-target classification in a brain-computer interface (BCI) for communication. EEG data were collected from 12 participants who completed BCI Rapid Serial Visual Presentation (RSVP) calibrations at two different presentation rates: 1 and 4 Hz. Attention-related changes in posterior alpha activity were compared to two event-related potentials (ERPs): N200 and P300. Machine learning approaches evaluated target/non-target classification accuracy using alpha activity. Results indicated significant alpha attenuation following target letters at both 1 and 4 Hz presentation rates, though this effect was significantly reduced in the 4 Hz condition. Target-related alpha attenuation was not correlated with coincident N200 or P300 target effects. Classification using posterior alpha activity was above chance and benefitted from individualized tuning procedures. These findings suggest that target-related posterior alpha attenuation is detectable in a BCI RSVP calibration and that this signal could be leveraged in machine learning algorithms used for RSVP or comparable attention-based BCI paradigms.

RevDate: 2022-05-09

Wang CH, KY Tsai (2022)

Optimization of machine learning method combined with brain-computer interface rehabilitation system.

Journal of physical therapy science, 34(5):379-385.

[Purpose] Stroke patients are unable to move on their own and must be rehabilitated to allow the nervous system to trigger and restore its function. Traditional practice is to use electrode caps to extract brain wave features and combine them with assistive devices. However, there are problems that the electrode cap is not easy to wear, and the potential recognition is not good, and different extraction methods will affect the accuracy of the Brain-Computer Interfaces (BCI), which still has room for improvement. [Participants and Methods] The brainwave headphones used in this experiment do not must a conductive gel to get a good EEG for neural induction and drive the upper limb rehabilitation robot. Next, 8 stroke patients and 200 normal participants were invited for a 4-week rehabilitation training. The effectiveness of the training was determined using Fast Fourier Transform (FFT), Magnitude squared coherence (MSC) feature extraction methods, and five machine learning techniques that induced flicker frequencies. [Results] The results show that the optimal steady-state visual evoked flicker frequency is 6 Hz, and the identification rate of FFT is about 5.2% higher than that of the MSC method. Using an optimized model for different feature extraction methods can improve the recognition rate by 1.3%-9.1%. [Conclusion] The images based on Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS) index improvement, and functional Magnetic Resonance Imaging (fMRI) show that the sensory region of brain movement has become a concentrated activation phenomenon. Besides strengthening the feature extraction method also lets the elbow has an obvious recovery effect.

RevDate: 2022-05-09

DePass M, Falaki A, Quessy S, et al (2022)

A machine learning approach to characterize sequential movement-related states in premotor and motor cortices.

Journal of neurophysiology, 127(5):1348-1362.

Nonhuman primate (NHP) movement kinematics have been decoded from spikes and local field potentials (LFPs) recorded during motor tasks. However, the potential of LFPs to provide network-like characterizations of neural dynamics during planning and execution of sequential movements requires further exploration. Is the aggregate nature of LFPs suitable to construct informative brain state descriptors of movement preparation and execution? To investigate this, we developed a framework to process LFPs based on machine-learning classifiers and analyzed LFP from a primate, implanted with several microelectrode arrays covering the premotor cortex in both hemispheres and the primary motor cortex on one side. The monkey performed a reach-to-grasp task, consisting of five consecutive states, starting from rest until a rewarding target (food) was attained. We use this five-state task to characterize neural activity within eight frequency bands, using spectral amplitude and pairwise correlations across electrodes as features. Our results show that we could best distinguish all five movement-related states using the highest frequency band (200-500 Hz), yielding an 87% accuracy with spectral amplitude, and 60% with pairwise electrode correlation. Further analyses characterized each movement-related state, showing differential neuronal population activity at above-γ frequencies during the various stages of movement. Furthermore, the topological distribution for the high-frequency LFPs allowed for a highly significant set of pairwise correlations, strongly suggesting a concerted distribution of movement planning and execution function is distributed across premotor and primary motor cortices in a specific fashion, and is most significant in the low ripple (100-150 Hz), high ripple (150-200 Hz), and multiunit frequency bands. In summary, our results show that the concerted use of novel machine-learning techniques with coarse grained queue broad signals such as LFPs may be successfully used to track and decode fine movement aspects involving preparation, reach, grasp, and reward retrieval across several brain regions.NEW & NOTEWORTHY Local field potentials (LFPs), despite lower spatial resolution compared to single-neuron recordings, can be used with machine learning classifiers to decode sequential movements involving motor preparation, execution, and reward retrieval. Our results revealed heterogeneity of neural activity on small spatial scales, further evidencing the utility of micro-electrode array recordings for complex movement decoding. With further advancement, high-dimensional LFPs may become the gold standard for brain-computer interfaces such as neural prostheses in the near future.

RevDate: 2022-05-07

Mahmood M, Kim N, Mahmood M, et al (2022)

VR-enabled portable brain-computer interfaces via wireless soft bioelectronics.

Biosensors & bioelectronics, 210:114333 pii:S0956-5663(22)00373-6 [Epub ahead of print].

Noninvasive, wearable brain-computer interfaces (BCI) find limited use due to their obtrusive nature and low information. Currently available portable BCI systems are limited by device rigidity, bulky form factors, and gel-based skin-contact electrodes - and therefore more prone to noise and motion artifacts. Here, we introduce virtual reality (VR)-enabled split-eye asynchronous stimulus (SEAS) allowing a target to present different stimuli to either eye. This results in unique asynchronous stimulus patterns measurable with as few as four EEG electrodes, as demonstrated with improved wireless soft electronics for portable BCI. This VR-embedded SEAS paradigm demonstrates potential for improved throughput with a greater number of unique stimuli. A wearable soft platform featuring dry needle electrodes and shielded stretchable interconnects enables high throughput decoding of steady-state visually evoked potentials (SSVEP) for a text spelling interface. A combination of skin-conformal electrodes and soft materials offers high-quality recordings of SSVEP with minimal motion artifacts, validated by comparing the performance with a conventional wearable system. A deep-learning algorithm provides real-time classification, with an accuracy of 78.93% for 0.8 s and 91.73% for 2 s with 33 classes from nine human subjects, allowing for a successful demonstration of VR text spelling and navigation of a real-world environment. With as few as only four data recording channels, the system demonstrates a highly competitive information transfer rate (243.6 bit/min). Collectively, the VR-enabled soft system offers unique advantages in wireless, real-time monitoring of brain signals for portable BCI, neurological rehabilitation, and disease diagnosis.

RevDate: 2022-05-06

Irmer C, Volkenstein S, Dazert S, et al (2022)

The bone conduction implant BONEBRIDGE increases quality of life and social life satisfaction.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery [Epub ahead of print].

PURPOSE: Transcutaneous active bone conduction hearing aids represent an alternative approach to middle ear surgery and conventional hearing aids for patients with conductive or mixed hearing loss. The aim of this study was to determine quality of life, subjective hearing experience and patients' satisfaction after implantation of a bone conduction hearing aid.

METHODS: This monocentric and retrospective study included twelve adult patients who received a bone conduction hearing aid (Bonebridge, MedEL) consisting of an extracorporeal audio processor and a bone conduction implant (BCI) between 2013 and 2017. On average 40 months after implantation, the patients were asked to answer three questionnaires regarding quality of life (AqoL-8D), self-reported auditory disability (SSQ-12-B) and user's satisfaction (APSQ) after implantation of the Bonebridge (BB). A descriptive statistical analysis of the questionnaires followed.

RESULTS: 12 patients aged 26-85 years (sex: m = 7, w = 5) were recruited. The quality of life of all patients after implantation of the BB (AqoL 8D) averaged an overall utility score of 0.76 (SD ± 0.17). The mean for 'speech hearing' in the SSQ-12-B was + 2.43 (SD ± 2.03), + 1.94 (SD ± 1.48) for 'spatial hearing' and + 2.28 (SD ± 2.32) for 'qualities of hearing'. 11 out of 12 patients reported an improvement in their overall hearing. The APSQ score for the subsection 'wearing comfort' was 3.50 (SD ± 0.87), 'social life' attained a mean of 4.17 (SD ± 1.06). The 'device inconveniences' reached 4.02 (SD ± 0.71) and 'usability' of the device was measured at 4.23 (SD ± 1.06). The average wearing time of the audio processor in the cohort was 11 h per day, with 8 of 12 patients reporting the maximum length of 12 h per day.

CONCLUSION: BB implantation results in a gain in the perceived quality of life (AqoL 8D). The SSQ-12-B shows an improvement in subjective hearing. According to the APSQ, it can be assumed that the BB audio processor, although in an extracorporeal position, is rated as a useful instrument with positive impact on social life. The majority stated that they had subjectively benefited from BB implantation and that there were no significant physical or sensory limitations after implantation.

RevDate: 2022-05-06

Yang M, Jung TP, Han J, et al (2022)

[A review of researches on decoding algorithms of steady-state visual evoked potentials].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 39(2):416-425.

Brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) have become one of the major paradigms in BCI research due to their high signal-to-noise ratio and short training time required by users. Fast and accurate decoding of SSVEP features is a crucial step in SSVEP-BCI research. However, the current researches lack a systematic overview of SSVEP decoding algorithms and analyses of the connections and differences between them, so it is difficult for researchers to choose the optimum algorithm under different situations. To address this problem, this paper focuses on the progress of SSVEP decoding algorithms in recent years and divides them into two categories-trained and non-trained-based on whether training data are needed. This paper also explains the fundamental theories and application scopes of decoding algorithms such as canonical correlation analysis (CCA), task-related component analysis (TRCA) and the extended algorithms, concludes the commonly used strategies for processing decoding algorithms, and discusses the challenges and opportunities in this field in the end.

RevDate: 2022-05-06

Luo J, Ding P, Gong A, et al (2022)

[Applications, industrial transformation and commercial value of brain-computer interface technology].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 39(2):405-415.

Brain-computer interface (BCI) is a revolutionary human-computer interaction technology, which includes both BCI that can output instructions directly from the brain to external devices or machines without relying on the peripheral nerve and muscle system, and BCI that bypasses the peripheral nerve and muscle system and inputs electrical, magnetic, acoustic and optical stimuli or neural feedback directly to the brain from external devices or machines. With the development of BCI technology, it has potential application not only in medical field, but also in non-medical fields, such as education, military, finance, entertainment, smart home and so on. At present, there is little literature on the relevant application of BCI technology, the current situation of BCI industrialization at home and abroad and its commercial value. Therefore, this paper expounds and discusses the above contents, which are expected to provide valuable information for the public and organizations, BCI researchers, BCI industry translators and salespeople, and improve the cognitive level of BCI technology, further promote the application and industrial transformation of BCI technology and enhance the commercial value of BCI, so as to serve mankind better.

RevDate: 2022-05-06

Ji Y, Li F, Fu B, et al (2022)

Spatial-temporal Network for Fine-grained-level Emotion EEG Recognition.

Journal of neural engineering [Epub ahead of print].

Electroencephalogram (EEG)-based affective computing brain-computer interfaces provide the capability for machines to understand human intentions. In practice, people are more concerned with the strength of a certain emotional state over a short period of time, which was called as fine-grained-level emotion in this paper. In this study, we built a fine-grained-level emotion EEG dataset that contains two coarse-grained emotions and four corresponding fine-grained-level emotions. To fully extract the features of the EEG signals, we proposed a corresponding fine-grained emotion EEG network (FG-emotionNet) for spatial-temporal feature extraction. Each feature extraction layer is linked to raw EEG signals to alleviate overfitting and ensure that the spatial features of each scale can be extracted from the raw signals. Moreover, all previous scale features are fused before the current spatial-feature layer to enhance the scale features in the spatial block. Additionally, long short-term memory is adopted as the temporal block to extract the temporal features based on spatial features and classify the category of fine-grained emotions. Subject-dependent and cross-session experiments demonstrated that the performance of the proposed method is superior to that of the representative methods in emotion recognition and similar structure methods with proposed method.

RevDate: 2022-05-06

Farabbi A, Aloia V, L Mainardi (2022)

ARX-Based EEG Data Balancing for Error Potential BCI.

Journal of neural engineering [Epub ahead of print].

Deep learning algorithms employed in Brain Computer Interfaces (BCI) need large electroencephalographic (EEG) datasets to be trained. These datasets are usually unbalanced, particularly when Error Potential (ErrP) experiment are considered, being ErrP's epochs much rarer than Non-ErrP ones. To face the issue of unbalance of rare epochs, this paper presents a novel, data balancing methods based on ARX-modelling.

APPROACH: AutoRegressive with eXogenous input (ARX)-models are identified on the EEG data of the "Monitoring error-related potentials" dataset of the BNCI Horizon 2020 and then employed to generate new synthetic data of the minority class of ErrP epochs. The balanced dataset is used to train a classifier of Non-Errp vs. ErrP epochs based on EEGNet.

MAIN RESULTS: Compared to classical techniques (e.g.: class weights, CW) for data balancing, the new method outperforms the others in terms of resulting accuracy (i.e: ARX 91.5% vs CW 88.3%), F1-score (i.e: ARX 78.3% vs CW 73.7%) and balanced accuracy (i.e: ARX 87.0% vs CW 81.1%) and also reduces the number of false positive detection (i.e: ARX 51 vs CW 104). Moreover, the ARX-based method shows a better generalization capability of the whole model to classify and predict new data.

SIGNIFICANCE: The results obtained suggest that the proposed method can be used in BCI application for tackling the issue of data unbalance and obtain more reliable and robust performances.

RevDate: 2022-05-06

Perez-Valero E, Lopez-Gordo MÁ, Gutiérrez CM, et al (2022)

A self-driven approach for multi-class discrimination in Alzheimer's disease based on wearable EEG.

Computer methods and programs in biomedicine, 220:106841 pii:S0169-2607(22)00223-1 [Epub ahead of print].

Early detection is critical to control Alzheimer's disease (AD) progression and postpone cognitive decline. Traditional medical procedures such as magnetic resonance imaging are costly, involve long waiting lists, and require complex analysis. Alternatively, for the past years, researchers have successfully evaluated AD detection approaches based on machine learning and electroencephalography (EEG). Nonetheless, these approaches frequently rely upon manual processing or involve non-portable EEG hardware. These aspects are suboptimal regarding automated diagnosis, since they require additional personnel and hinder portability. In this work, we report the preliminary evaluation of a self-driven AD multi-class discrimination approach based on a commercial EEG acquisition system using sixteen channels. For this purpose, we recorded the EEG of three groups of participants: mild AD, mild cognitive impairment (MCI) non-AD, and controls, and we implemented a self-driven analysis pipeline to discriminate the three groups. First, we applied automated artifact rejection algorithms to the EEG recordings. Then, we extracted power, entropy, and complexity features from the preprocessed epochs. Finally, we evaluated a multi-class classification problem using a multi-layer perceptron through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best in literature (0.88 F1-score), what suggests that AD can potentially be detected through a self-driven approach based on commercial EEG and machine learning. We believe this work and further research could contribute to opening the door for the detection of AD in a single consultation session, therefore reducing the costs associated to AD screening and potentially advancing medical treatment.

RevDate: 2022-05-06

Wojtkiewicz S, Bejm K, A Liebert (2022)

Lock-in functional near-infrared spectroscopy for measurement of the haemodynamic brain response.

Biomedical optics express, 13(4):1869-1887 pii:448038.

Here we show a method of the lock-in amplifying near-infrared signals originating within a human brain. It implies using two 90-degree rotated source-detector pairs fixed on a head surface. Both pairs have a joint sensitivity region located towards the brain. A direct application of the lock-in technique on both signals results in amplifying common frequency components, e.g. related to brain cortex stimulation and attenuating the rest, including all components not related to the stimulation: e.g. pulse, instrumental and biological noise or movement artefacts. This is a self-driven method as no prior assumptions are needed and the noise model is provided by the interfering signals themselves. We show the theory (classical modified Beer-Lambert law and diffuse optical tomography approaches), the algorithm implementation and tests on a finite element mathematical model and in-vivo on healthy volunteers during visual cortex stimulation. The proposed hardware and algorithm complexity suit the entire spectrum of (continuous wave, frequency domain, time-resolved) near-infrared spectroscopy systems featuring real-time, direct, robust and low-noise brain activity registration tool. As such, this can be of special interest in optical brain computer interfaces and high reliability/stability monitors of tissue oxygenation.

RevDate: 2022-05-06

Haider S, Saleem F, Ahmad N, et al (2022)

Translation, Validation, and Psychometric Evaluation of the Diabetes Quality-of-Life Brief Clinical Inventory: The Urdu Version.

Journal of multidisciplinary healthcare, 15:955-966 pii:351330.

Purpose: The study is aimed to examine the psychometric properties of the Urdu version of the Diabetes Quality-of-Life Brief Clinical Inventory.

Methods: We adopted the forward-backward procedure to translate the Diabetes Quality-of-Life Brief Clinical Inventory (DQoL-BCI) into the Urdu language (lingua franca of Pakistan). The intraclass correlation (ICC) confirmed the consistency of retaining the items, and Cronbach's alpha established the test-re-test reliability. The confirmatory factor analysis (principal axis factoring extraction and oblique rotation with Kaiser normalization) validated the DQoL-BCI in Urdu.

Results: A two-time point with an interval of 2 weeks was used, and the Urdu version of DQoL-BCI was piloted accordingly. The 15-item translated version (DQoL-BCI-U) exhibited a satisfactory Cronbach's value of 0.866 (test) at week 1 and 0.850 at week 3 (re-test). Using the one-way random model with single measurements, the ICC for all 15 items exhibited coefficient values of >0.80. The Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's Test of Sphericity revealed relationships of the data and suitability of CFA (0.899, p<0.05). Seven factors explaining the total variance of 69% were extracted. With acceptable communalities, all 15 items of DQoL-BCI-U were retained.

Conclusion: The study concludes that the translated version of DQoL-BCI-U is a valid instrument in regions, where Urdu is a communal language of communication and can examine quality-of-life issues during the typical patient-provider encounter.

RevDate: 2022-05-06

Kim YJ, Yoon S, Cho YH, et al (2020)

Paintable and writable electrodes using black conductive ink on traditional Korean paper (Hanji).

RSC advances, 10(41):24631-24641 pii:d0ra04412a.

We demonstrate black conductive ink (BCI) that is writable and paintable on traditional handmade Korean paper (Hanji) for application as a high performing electrode. By optimal mixing of Ag nanowire (Ag NW) suspension and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)(PEDOT:PSS) solution in standard charcoal-based blank ink, we synthesized BCI suitable for writing and painting on Hanji with a normal paintbrush. Due to the shear stress induced by the paintbrush bristles, the Ag NW and PEDOT:PSS mixture was uniformly coated on the porous cellulose structure of Hanji and showed a low sheet resistance of 11.7 Ohm per square even after repeated brush strokes. Moreover, the brush-painted electrodes on Hanji showed a constant resistance during tests of inner/outer bending and folding due to the outstanding flexibility of the Ag NW and PEDOT:PSS mixture that filled the porous cellulose structure of Hanji. Therefore, the pictures drawn in the BCI on Hanji exhibited a level of flexibility and conductivity sufficiently high to enable the BCI to function as an effective electrode even when the paper substrate is wrinkled or crumpled. The successful operation of the paintable interconnector and heater on Hanji indicates the high potential of the brush-painted electrodes that can be used in various social and cultural fields, including fine art, fashion, interior design, architecture, and heating industry.

RevDate: 2022-05-05

Zhang R, Zeng Y, Tong L, et al (2022)

ERP-WGAN: A Data Augmentation Method for EEG Single-trial Detection.

Journal of neuroscience methods pii:S0165-0270(22)00148-0 [Epub ahead of print].

Brain computer interaction based on EEG presents great potential and becomes the research hotspots. However, the insufficient scale of EEG database limits the BCI system performance, especially the positive and negative sample imbalance caused by oddball paradigm. To alleviate the bottleneck problem of scarce EEG sample, we propose a data augmentation method based on generative adversarial network to improve the performance of EEG signal classification. Taking the characteristics of EEG into account in wasserstein generative adversarial networks (WGAN), the problems of model collapse and poor quality of artificial data were solved by using resting noise, smoothing and random amplitude. The quality of artificial data was comprehensively evaluated from verisimilitude, diversity and accuracy. Compared with the three artificial data methods and two data sampling methods, the proposed ERP-WGAN framework significantly improve the performance of both subject and general classifiers, especially the accuracy of general classifiers trained by less than 5 dimensional features is improved by 20-25%. Moreover, we evaluate the training sets performance with different mixing ratios of artificial and real samples. ERP-WGAN can reduced at least 73% of the real subject data and acquisition cost, which greatly saves the test cycle and research cost.

RevDate: 2022-05-05

Beavers DP, Hsieh KL, Kitzman DW, et al (2022)

Estimating heterogeneity of physical function treatment response to caloric restriction among older adults with obesity.

PloS one, 17(5):e0267779 pii:PONE-D-21-27220.

Clinical trials conventionally test aggregate mean differences and assume homogeneous variances across treatment groups. However, significant response heterogeneity may exist. The purpose of this study was to model treatment response variability using gait speed change among older adults participating in caloric restriction (CR) trials. Eight randomized controlled trials (RCTs) with five- or six-month assessments were pooled, including 749 participants randomized to CR and 594 participants randomized to non-CR (NoCR). Statistical models compared means and variances by CR assignment and exercise assignment or select subgroups, testing for treatment differences and interactions for mean changes and standard deviations. Continuous equivalents of dichotomized variables were also fit. Models used a Bayesian framework, and posterior estimates were presented as means and 95% Bayesian credible intervals (BCI). At baseline, participants were 67.7 (SD = 5.4) years, 69.8% female, and 79.2% white, with a BMI of 33.9 (4.4) kg/m2. CR participants reduced body mass [CR: -7.7 (5.8) kg vs. NoCR: -0.9 (3.5) kg] and increased gait speed [CR: +0.10 (0.16) m/s vs. NoCR: +0.07 (0.15) m/s] more than NoCR participants. There were no treatment differences in gait speed change standard deviations [CR-NoCR: -0.002 m/s (95% BCI: -0.013, 0.009)]. Significant mean interactions between CR and exercise assignment [0.037 m/s (95% BCI: 0.004, 0.070)], BMI [0.034 m/s (95% BCI: 0.003, 0.066)], and IL-6 [0.041 m/s (95% BCI: 0.009, 0.073)] were observed, while variance interactions were observed between CR and exercise assignment [-0.458 m/s (95% BCI: -0.783, -0.138)], age [-0.557 m/s (95% BCI: -0.900, -0.221)], and gait speed [-0.530 m/s (95% BCI: -1.018, -0.062)] subgroups. Caloric restriction plus exercise yielded the greatest gait speed benefit among older adults with obesity. High BMI and IL-6 subgroups also improved gait speed in response to CR. Results provide a novel statistical framework for identifying treatment heterogeneity in RCTs.

RevDate: 2022-05-05

Salvatore C, Valeriani D, Piccialli V, et al (2022)

Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics.

RevDate: 2022-05-05

Xu H, Piao L, Liu X, et al (2022)

Ursolic acid-enriched kudingcha extract enhances the antitumor activity of bacteria-mediated cancer immunotherapy.

BMC complementary medicine and therapies, 22(1):123.

BACKGROUND: Bacteria-mediated cancer immunotherapy (BCI) robustly stimulates the immune system and represses angiogenesis, but tumor recurrence and metastasis commonly occur after BCI. The natural product Ilex kudingcha C. J Tseng enriched with ursolic acid has anti-cancer activity and could potentially augment the therapeutic effects of BCI. The objective of the present study was to determine potential additive effects of these modalities.

METHODS: We investigated the anti-cancer activity of KDCE (Kudingcha extract) combined with S.t△ppGpp in the mice colon cancer models.

RESULTS: In the present study, KDCE combined with S.t△ppGpp BCI improved antitumor therapeutic efficacy compared to S.t△ppGpp or KDCE alone. KDCE did not prolong bacterial tumor-colonizing time, but enhanced the antiangiogenic effect of S.t△ppGpp by downregulatingVEGFR2. We speculated that KDCE-induced VEGFR2 downregulation is associated with FAK/MMP9/STAT3 axis but not AKT or ERK.

CONCLUSIONS: Ursolic acid-enriched KDCE enhances the antitumor activity of BCI, which could be mediated by VEGFR2 downregulation and subsequent suppression of angiogenesis. Therefore, combination therapy with S.t△ppGpp and KDCE is a potential cancer therapeutic strategy.

RevDate: 2022-05-04

Sujatha Ravindran A, Malaya C, John I, et al (2022)

Decoding Neural Activity Preceding Balance Loss During Standing with a Lower-limb Exoskeleton using an Interpretable Deep Learning Model.

Journal of neural engineering [Epub ahead of print].

Falls are a leading cause of death in adults 65 and older. Recent efforts to restore lower-limb function in these populations have seen an increase in the use of wearable robotic systems; however, fall prevention measures in these systems require early detection of balance loss to be effective. Prior studies have investigated whether kinematic variables contain information about an impending fall, but few have examined the potential of using electroencephalography (EEG) as a fall-predicting signal and how the brain responds to avoid a fall. To address this, we decoded neural activity in a balance perturbation task while wearing an exoskeleton. We acquired EEG, electromyography (EMG), and center of pressure (COP) data from 7 healthy participants during mechanical perturbations while standing. The timing of the perturbations was randomized in all trials. We found perturbation evoked potentials (PEP) components as early as 75-134 ms after the onset of the external perturbation, which preceded both the peak in EMG (∼ 180 ms) and the COP (∼ 350 ms). A convolutional neural network trained to predict balance perturbations from single-trial EEG had a mean F-score of 75.0 ± 4.3 %. Clustering GradCAM-based model explanations demonstrated that the model utilized components in the PEP and was not driven by artifacts. Additionally, dynamic functional connectivity results agreed with model explanations; the nodal connectivity measured using phase difference derivative was higher in the occipital-parietal region in the early stage of perturbations, before shifting to the parietal, motor, and back to the frontal-parietal channels. Continuous-time decoding of COP trajectories from EEG, using a gated recurrent unit model, achieved a mean Pearson's correlation coefficient of 0.7 ± 0.06. Overall, our findings suggest that EEG signals contain short-latency neural information related to an impending fall, which may be useful for developing brain-machine interface systems for fall prevention in robotic exoskeletons.

RevDate: 2022-05-03

Wang Y, Othayoth R, C Li (2022)

Cockroaches adjust body and appendages to traverse cluttered large obstacles.

The Journal of experimental biology pii:275278 [Epub ahead of print].

To traverse complex terrain, animals often transition between locomotor modes. It is well-known that locomotor transitions can be induced by switching in neural control circuits or driven by a need to minimize metabolic energetic cost. Recent work discovered that locomotor transitions in complex 3-D terrain cluttered with large obstacles can emerge from physical interaction with the environment controlled by the nervous system. For example, to traverse cluttered, stiff grass-like beams, the discoid cockroach often transitions from using a strenuous pitch mode pushing across to using a less strenuous roll mode rolling into and through the gaps, and this transition requires overcoming a potential energy barrier. Previous robotic physical modeling demonstrated that kinetic energy fluctuation of body oscillation from self-propulsion can help overcome the barrier and facilitate this transition. However, the animal was observed to transition even when the barrier still exceeded kinetic energy fluctuation. Here, we further studied whether and how the cockroach makes active adjustments to facilitate this transition to traverse cluttered beams. The animal repeatedly flexed its head and abdomen, reduced hind leg sprawl, and depressed one hind leg and elevated the other during the pitch-to-roll transition, which were absent when running on a flat ground. Using a refined potential energy landscape with additional degrees of freedom to model these adjustments, we found that head flexion did not substantially reduce the transition barrier, whereas leg sprawl reduction did so dramatically. We speculate that head flexion is for sensing the terrain to guide the transition via sensory feedback control.

RevDate: 2022-05-02

Yuan X, Zhang L, Sun Q, et al (2022)

A novel command generation method for SSVEP-based BCI by introducing SSVEP blocking response.

Computers in biology and medicine, 146:105521 pii:S0010-4825(22)00313-4 [Epub ahead of print].

Increasing the number of commands in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) by increasing the number of visual stimuli has been widely studied. This paper proposes a novel BCI paradigm based on SSVEP and SSVEP blocking responses (defined as the disappearance or attenuation of the ongoing SSVEP) to increase the number of BCI commands with limited visual stimuli, in which the duration of SSVEP blocking response can be voluntarily controlled by users. Besides, this paper also proposes a frequency-specific threshold method and a unified threshold method to identify SSVEP blocking response. The paradigm includes a frequency recognition phase and an SSVEP blocking response identification phase. Filter bank canonical correlation analysis is used to detect the stimulation frequency, and the proposed threshold method is used to identify the SSVEP blocking response and calculate the blocking duration. The experimental results show that the two proposed threshold methods can effectively identify the SSVEP blocking response with different blocking duration and alternative stimulation frequencies. When there are Nf stimulation frequencies, the number of commands can be increased to Nf×Nt using the proposed paradigm, where Nt blocking durations correspond to each stimulus. This study demonstrates that the proposed paradigm based on SSVEP and SSVEP blocking responses is effective in increasing the number of BCI commands and has great potential for practical applications.

RevDate: 2022-05-02

AlFarraj A, AlIbrahim M, AlHajjaj H, et al (2022)

Transcutaneous Bone Conduction Implants in Patients With Single-Sided Deafness: Objective and Subjective Evaluation.

Ear, nose, & throat journal [Epub ahead of print].

OBJECTIVES: This study aimed to investigate the audiological outcomes and subjective benefits of transcutaneous bone conduction implants (BCIs) in patients with single-sided deafness (SSD).

METHODS: This retrospective study was conducted on 11 patients with SSD implantations between 2015 and 2018 at a tertiary center. Pure-tone audiometry, speech reception threshold (SRT), and speech-in-noise (SPIN) tests were performed. Preoperative and postoperative performances were compared. Subjective satisfaction level was assessed using validated questionnaires. A PubMed search was conducted to identify the relevant studies published to date.

RESULTS: All patients demonstrated significant audiological improvements compared with their preoperative condition. The mean SRT improved significantly (p = 0.001) from 109 dB to 23 dB after implantation. The mean SPIN score improved significantly after implantation. The questionnaires showed an overall positive benefit of transcutaneous bone conduction devices (BCDs). A literature search revealed 21 articles, of which 14 reported the use of BCIs in patients with SSD. Our results agree with the published evidence showing the overall benefit of BCI in patients with SSD.

CONCLUSIONS: Transcutaneous BCDs could be considered as an alternative treatment option for patients with SSD, it could show good audiological outcomes and high satisfaction levels. Further studies should be conducted on patients with SSD to determine the most appropriate hearing solutions.

RevDate: 2022-05-02

Mouli S, R Palaniappan (2020)

DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset.

HardwareX, 8:e00113 pii:S2468-0672(20)30022-5.

A fully customisable chip-on board (COB) LED design to evoke two brain responses simultaneously (steady state visual evoked potential (SSVEP) and transient evoked potential, P300) is discussed in this paper. Considering different possible modalities in brain-computer interfacing (BCI), SSVEP is widely accepted as it requires a lesser number of electroencephalogram (EEG) electrodes and minimal training time. The aim of this work was to produce a hybrid BCI hardware platform to evoke SSVEP and P300 precisely with reduced fatigue and improved classification performance. The system comprises of four independent radial green visual stimuli controlled individually by a 32-bit microcontroller platform to evoke SSVEP and four red LEDs flashing at random intervals to generate P300 events. The system can also record the P300 event timestamps that can be used in classification, to improve the accuracy and reliability. The hybrid stimulus was tested for real-time classification accuracy by controlling a LEGO robot to move in four directions.

RevDate: 2022-05-02

Kostick-Quenet K, Kalwani L, Koenig B, et al (2022)

Researchers' Ethical Concerns About Using Adaptive Deep Brain Stimulation for Enhancement.

Frontiers in human neuroscience, 16:813922.

The capacity of next-generation closed-loop or adaptive deep brain stimulation devices (aDBS) to read (measure neural activity) and write (stimulate brain regions or circuits) shows great potential to effectively manage movement, seizure, and psychiatric disorders, and also raises the possibility of using aDBS to electively (non-therapeutically) modulate mood, cognition, and prosociality. What separates aDBS from most neurotechnologies (e.g. transcranial stimulation) currently used for enhancement is that aDBS remains an invasive, surgically-implanted technology with a risk-benefit ratio significantly different when applied to diseased versus non-diseased individuals. Despite a large discourse about the ethics of enhancement, no empirical studies yet examine perspectives on enhancement from within the aDBS research community. We interviewed 23 aDBS researchers about their attitudes toward expanding aDBS use for enhancement. A thematic content analysis revealed that researchers share ethical concerns related to (1) safety and security; (2) enhancement as unnecessary, unnatural or aberrant; and (3) fairness, equality, and distributive justice. Most (70%) researchers felt that enhancement applications for DBS will eventually be technically feasible and that attempts to develop such applications for DBS are already happening (particularly for military purposes). However, researchers unanimously (100%) felt that DBS ideally should not be considered for enhancement until researchers better understand brain target localization and functioning. While many researchers acknowledged controversies highlighted by scholars and ethicists, such as potential impacts on personhood, authenticity, autonomy and privacy, their ethical concerns reflect considerations of both gravity and perceived near-term likelihood.

RevDate: 2022-05-02

Wu Y, Chen H, L Guo (2019)

Opportunities and dilemmas of in vitro nano neural electrodes.

RSC advances, 10(1):187-200 pii:c9ra08917a.

Developing electrophysiological platforms to capture electrical activities of neurons and exert modulatory stimuli lays the foundation for many neuroscience-related disciplines, including the neuron-machine interface, neuroprosthesis, and mapping of brain circuitry. Intrinsically more advantageous than genetic and chemical neuronal probes, electrical interfaces directly target the fundamental driving force-transmembrane currents-behind the complicated and diverse neuronal signals, allowing for the discovery of neural computational mechanisms of the most accurate extent. Furthermore, establishing electrical access to neurons is so far the most promising solution to integrate large-scale, high-speed modern electronics with neurons that are highly dynamic and adaptive. Over the evolution of electrode-based electrophysiologies, there has long been a trade-off in terms of precision, invasiveness, and parallel access due to limitations in fabrication techniques and insufficient understanding of membrane-electrode interactions. On the one hand, intracellular platforms based on patch clamps and sharp electrodes suffer from acute cellular damage, fluid diffusion, and labor-intensive micromanipulation, with little room for parallel recordings. On the other hand, conventional extracellular microelectrode arrays cannot detect from subcellular compartments or capture subthreshold membrane potentials because of the large electrode size and poor seal resistance, making it impossible to depict a comprehensive picture of a neuron's electrical activities. Recently, the application of nanotechnology on neuronal electrophysiology has brought about a promising solution to mitigate these conflicts on a single chip. In particular, three dimensional nanostructures of 10-100 nm in diameter are naturally fit to achieve the purpose of precise and localized interrogations. Engineering them into vertical nanoprobes bound on planar substrates resulted in excellent membrane-electrode seals and high-density electrode distribution. There is no doubt that 3D vertical nanoelectrodes have achieved a fundamental milestone in terms of high precision, low invasiveness, and parallel recording at the neuron-electrode interface, albeit with there being substantial engineering issues that remain before the potential of nano neural interfaces can be fully exploited. Within this framework, we review the qualitative breakthroughs and opportunities brought about by 3D vertical nanoelectrodes, and discuss the major limitations of current electrode designs with respect to rational and seamless cell-on-chip systems.

RevDate: 2022-05-02

King JT, John AR, Wang YK, et al (2022)

Brain Connectivity Changes During Bimanual and Rotated Motor Imagery.

IEEE journal of translational engineering in health and medicine, 10:2100408 pii:2100408.

Motor imagery-based brain-computer interface (MI-BCI) currently represents a new trend in rehabilitation. However, individual differences in the responsive frequency bands and a poor understanding of the communication between the ipsilesional motor areas and other regions limit the use of MI-BCI therapy. Objective: Bimanual training has recently attracted attention as it achieves better outcomes as compared to repetitive one-handed training. This study compared the effects of three MI tasks with different visual feedback. Methods: Fourteen healthy subjects performed single hand motor imagery tasks while watching single static hand (traditional MI), single hand with rotation movement (rmMI), and bimanual coordination with a hand pedal exerciser (bcMI). Functional connectivity is estimated by Transfer Entropy (TE) analysis for brain information flow. Results: Brain connectivity of conducting three MI tasks showed that the bcMI demonstrated increased communications from the parietal to the bilateral prefrontal areas and increased contralateral connections between motor-related zones and spatial processing regions. Discussion/Conclusion: The results revealed bimanual coordination operation events increased spatial information and motor planning under the motor imagery task. And the proposed bimanual coordination MI-BCI (bcMI-BCI) can also achieve the effect of traditional motor imagery tasks and promotes more effective connections with different brain regions to better integrate motor-cortex functions for aiding the development of more effective MI-BCI therapy. Clinical and Translational Impact Statement The proposed bcMI-BCI provides more effective connections with different brain areas and integrates motor-cortex functions to promote motor imagery rehabilitation for patients' impairment.

RevDate: 2022-04-30

Reinfeldt S, Eeg-Olofsson M, Fredén Jansson KJ, et al (2022)

Long-term follow-up and review of the Bone Conduction Implant.

Hearing research pii:S0378-5955(22)00072-7 [Epub ahead of print].

Active transcutaneous bone conduction devices are a type of bone conduction device developed to keep the skin intact and provide direct bone conduction stimulation. The Bone Conduction Implant (BCI) is such a device and has been implanted in 16 patients. The objective of this paper is to give a broad overview of the BCI development to the final results of 13 patients at 5-year follow-up. Follow-up of these patients included audiological performance investigations, questionnaires, as well as safety evaluation and objective functionality testing of the device. Among those audiological measurements were sound field warble tone thresholds, speech recognition threshold (SRT), speech recognition score (SRS) and signal to noise ratio threshold (SNR-threshold). The accumulated implant time for all 16 patients was 113 years in February 2022. During this time, no serious adverse events have occurred. The functional improvement for the 13 patients reported in this paper was on average 29.5 dB (average over 0.5, 1, 2 and 4 kHz), while the corresponding effective gain was -12.4 dB. The SRT improvement was 24.5 dB and the SRS improvement was 38.1%, while the aided SNR-threshold was on average -6.4 dB. It was found that the BCI can give effective and safe hearing rehabilitation for patients with conductive and mild-to-moderate mixed hearing loss.

RevDate: 2022-04-30

Niazi IK, Navid MS, Rashid U, et al (2022)

Associative cued asynchronous BCI induces cortical plasticity in stroke patients.

Annals of clinical and translational neurology [Epub ahead of print].

OBJECTIVE: We propose a novel cue-based asynchronous brain-computer interface(BCI) for neuromodulation via the pairing of endogenous motor cortical activity with the activation of somatosensory pathways.

METHODS: The proposed BCI detects the intention to move from single-trial EEG signals in real time, but, contrary to classic asynchronous-BCI systems, the detection occurs only during time intervals when the patient is cued to move. This cue-based asynchronous-BCI was compared with two traditional BCI modes (asynchronous-BCI and offline synchronous-BCI) and a control intervention in chronic stroke patients. The patients performed ankle dorsiflexion movements of the paretic limb in each intervention while their brain signals were recorded. BCI interventions decoded the movement attempt and activated afferent pathways via electrical stimulation. Corticomotor excitability was assessed using motor-evoked potentials in the tibialis-anterior muscle induced by transcranial magnetic stimulation before, immediately after, and 30 min after the intervention.

RESULTS: The proposed cue-based asynchronous-BCI had significantly fewer false positives/min and false positives/true positives (%) as compared to the previously developed asynchronous-BCI. Linear-mixed-models showed that motor-evoked potential amplitudes increased following all BCI modes immediately after the intervention compared to the control condition (p <0.05). The proposed cue-based asynchronous-BCI resulted in the largest relative increase in peak-to-peak motor-evoked potential amplitudes(141% ± 33%) among all interventions and sustained it for 30 min(111% ± 33%).

INTERPRETATION: These findings prove the high performance of a newly proposed cue-based asynchronous-BCI intervention. In this paradigm, individuals receive precise instructions (cue) to promote engagement, while the timing of brain activity is accurately detected to establish a precise association with the delivery of sensory input for plasticity induction.

RevDate: 2022-04-28

Ouyang R, Jin Z, Tang S, et al (2022)

Low-quality Training Data Detection Method of EEG Signals for Motor Imagery BCI System.

Journal of neuroscience methods pii:S0165-0270(22)00134-0 [Epub ahead of print].

BACKGROUND: The design and implementation of high-performance motor imagery-based brain computer interface (MI-BCI) requires high-quality training samples. However, fluctuation in subjects' physiological and mental states as well as artifacts can produce the low-quality motor imagery electroencephalogram (EEG) signal, which will damage the performance of MI-BCI system.

NEW METHOD: In order to select high-quality MI-EEG training data, this paper proposes a low-quality training data detection method combining independent component analysis (ICA) and weak classifier cluster. we also design and implement a new online BCI system based on motor imagery to verify the online processing performance of the proposed method.

RESULT: In order to verify the effectiveness of the proposed method, we conducted offline experiments on the public dataset called BCI Competition IV Data Set 2b. Furthermore, in order to verify the processing performance of the online system, we designed 60 groups of online experiments on 12 subjects. The online experimental results show that the twelve subjects can complete the system task efficiently (the best experiment is 135.6 seconds with 9 trials of subject S1).

CONCLUSION: This paper demonstrated that the proposed low-quality training data detection method can effectively screen out low-quality training samples, so as to improve the performance of the MI-BCI system.

RevDate: 2022-04-28

Yan W, Wu Y, Du C, et al (2022)

Cross-subject Spatial Filter Transfer Method for SSVEP-EEG Feature Recognition.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Steady-state visual evoked potential (SSVEP) is an important control method of the brain-computer interface (BCI) system. The development of an efficient SSVEP feature decoding algorithm is the core issue in SSVEP-BCI. It has been proposed to use user training data to reduce the spontaneous electroencephalogram (EEG) activity interference on SSVEP response, thereby improving the feature recognition accuracy of the SSVEP signal. Nevertheless, the tedious data collection process increases the mental fatigue of the user and severely affects the applicability of the BCI system.

APPROACH: A cross-subject spatial filter transfer (CSSFT) method that transfer the existing user data model to the new user test data without collecting any training data from the new user is proposed.

MAIN RESULTS: Experimental results demonstrate that the transfer model increases the distinction of the feature discriminant coefficient between the gaze following target and the non-gaze following target and accurately identifies the wrong target in the fundamental algorithm model. The public datasets show that the CSSFT method significantly increases the recognition performance of canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA). Additionally, when the data used to calculate the transfer model contains one data block only, the CSSFT method retains its effective feature recognition capabilities.

SIGNIFICANCE: The proposed method requires no tedious data calibration process, provides an effective technical solution for the transfer of the cross-subject model, and has potential application value for promoting the application of the BCI system.

RevDate: 2022-04-28

Aurna NF, Yousuf MA, Taher KA, et al (2022)

A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models.

Computers in biology and medicine, 146:105539 pii:S0010-4825(22)00331-6 [Epub ahead of print].

The brain tumor is one of the deadliest cancerous diseases and its severity has turned it to the leading cause of cancer related mortality. The treatment procedure of the brain tumor depends on the type, location and size of the tumor. Relying solely on human inspection for precise categorization can lead to inevitably dangerous situation. This manual diagnosis process can be improved and accelerated through an automated Computer Aided Diagnosis (CADx) system. In this article, a novel approach using two-stage feature ensemble of deep Convolutional Neural Networks (CNN) is proposed for precise and automatic classification of brain tumors. Three unique Magnetic Resonance Imaging (MRI) datasets and a dataset merging all the unique datasets are considered. The datasets contain three types of brain tumor (meningioma, glioma, pituitary) and normal brain images. From five pre-trained models and a proposed CNN model, the best models are chosen and concatenated in two stages for feature extraction. The best classifier is also chosen among five different classifiers based on accuracy. From the extracted features, most substantial features are selected using Principal Component Analysis (PCA) and fed into the classifier. The robustness of the proposed two stage ensemble model is analyzed using several performance metrics and three different experiments. Through the prominent performance, the proposed model is able to outperform other existing models attaining an average accuracy of 99.13% by optimization of the developed algorithms. Here, the individual accuracy for Dataset 1, Dataset 2, Dataset 3, and Merged Dataset is 99.67%, 98.16%, 99.76%, and 98.96% respectively. Finally a User Interface (UI) is created using the proposed model for real time validation.

RevDate: 2022-04-29

Maÿe A, Rauterberg R, AK Engel (2022)

Instant classification for the spatially-coded BCI.

PloS one, 17(4):e0267548 pii:PONE-D-21-13493.

The spatially-coded SSVEP BCI exploits changes in the topography of the steady-state visual evoked response to visual flicker stimulation in the extrafoveal field of view. In contrast to frequency-coded SSVEP BCIs, the operator does not gaze into any flickering lights; therefore, this paradigm can reduce visual fatigue. Other advantages include high classification accuracies and a simplified stimulation setup. Previous studies of the paradigm used stimulation intervals of a fixed duration. For frequency-coded SSVEP BCIs, it has been shown that dynamically adjusting the trial duration can increase the system's information transfer rate (ITR). We therefore investigated whether a similar increase could be achieved for spatially-coded BCIs by applying dynamic stopping methods. To this end we introduced a new stopping criterion which combines the likelihood of the classification result and its stability across larger data windows. Whereas the BCI achieved an average ITR of 28.4±6.4 bits/min with fixed intervals, dynamic intervals increased the performance to 81.1±44.4 bits/min. Users were able to maintain performance up to 60 minutes of continuous operation. We suggest that the dynamic response time might have worked as a kind of temporal feedback which allowed operators to optimize their brain signals and compensate fatigue.

RevDate: 2022-04-29
CmpDate: 2022-04-29

Cai X, J Pan (2022)

Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals.

Journal of healthcare engineering, 2022:6894392.

This study proposes a brain-computer interface (BCI)- and Internet of Things (IoT)-based smart ward collaborative system using hybrid signals. The system is divided into hybrid asynchronous electroencephalography (EEG)-, electrooculography (EOG)- and gyro-based BCI control system and an IoT monitoring and management system. The hybrid BCI control system proposes a GUI paradigm with cursor movement. The user uses the gyro to control the cursor area selection and uses blink-related EOG to control the cursor click. Meanwhile, the attention-related EEG signals are classified based on a support-vector machine (SVM) to make the final judgment. The judgment of the cursor area and the judgment of the attention state are reduced, thereby reducing the false operation rate in the hybrid BCI system. The accuracy in the hybrid BCI control system was 96.65 ± 1.44%, and the false operation rate and command response time were 0.89 ± 0.42 events/min and 2.65 ± 0.48 s, respectively. These results show the application potential of the hybrid BCI control system in daily tasks. In addition, we develop an architecture to connect intelligent things in a smart ward based on narrowband Internet of Things (NB-IoT) technology. The results demonstrate that our system provides superior communication transmission quality.

RevDate: 2022-04-28

Chen Z, Ye N, Teng C, et al (2022)

Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review.

Frontiers in neuroscience, 16:856808.

In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural-functional coupling of glioma. Additionally, the brain-computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.

RevDate: 2022-04-28

Thompson EM, Patel V, Rajeeve V, et al (2022)

The cytotoxic action of BCI is not dependent on its stated DUSP1 or DUSP6 targets in neuroblastoma cells.

FEBS open bio [Epub ahead of print].

Neuroblastoma (NB) is a heterogenous cancer of the sympathetic nervous system which accounts for 7-10% of paediatric malignancies worldwide. Due to the lack of targetable molecular aberrations in NB, most treatment options remain relatively non-specific. Here, we investigated the therapeutic potential of BCI, an inhibitor of DUSP1 and DUSP6, in cultured NB cells. BCI was cytotoxic in a range of NB cell lines and induced a short-lived activation of the AKT and stress-inducible MAP kinases, although ERK phosphorylation was unaffected. Furthermore, a phosphoproteomic screen identified significant upregulation of JNK signalling components and a suppression in mTOR and R6K signalling. To assess the specificity of BCI, CRISPR-Cas9 was employed to introduce insertions and deletions in the DUSP1 and DUSP6 genes. Surprisingly, BCI remained fully cytotoxic in NB cells with complete loss of DUSP6 and partial depletion of DUSP1, suggesting that BCI exerts cytotoxicity in NB cells through a complex mechanism that is unrelated to these phosphatases. Overall, these data highlight the risk of using inhibitors such as BCI as a supposedly specific DUSP1/6 inhibitor, without understanding its full range of targets in cancer cells.

RevDate: 2022-04-27

Zhang R, Xu Z, Zhang L, et al (2022)

The effect of stimulus number on the recognition accuracy and information transfer rate of SSVEP-BCI in augmented reality.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: The biggest advantage of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) lies in its large command set and high information transfer rate (ITR). Almost all current SSVEP-BCIs use a computer screen (CS) to present flickering visual stimuli, which limits its flexible use in actual scenes. Augmented reality (AR) technology provides the ability to superimpose visual stimuli on the real world, and it considerably expands the application scenarios of SSVEP-BCI. However, whether the advantages of SSVEP-BCI can be maintained when moving the visual stimuli to AR glasses is not known. This study investigated the effects of the stimulus number for SSVEP-BCI in an AR context.

APPROACH: We designed SSVEP flickering stimulation interfaces with four different numbers of stimulus targets and put them in AR glasses and a CS to display. Three common recognition algorithms were used to analyze the influence of the stimulus number and stimulation time on the recognition accuracy and ITR of AR-SSVEP and CS-SSVEP.

MAIN RESULTS: The amplitude spectrum and signal-to-noise ratio of AR-SSVEP were not significantly different from CS-SSVEP at the fundamental frequency but were significantly lower than CS-SSVEP at the second harmonic. SSVEP recognition accuracy decreased as the stimulus number increased in AR-SSVEP but not in CS-SSVEP. When the stimulus number increased, the maximum ITR of CS-SSVEP also increased, but not for AR-SSVEP. When the stimulus number was 25, the maximum ITR (142.05 bits/min) was reached at 400 ms. The importance of stimulation time in SSVEP was confirmed. When the stimulation time became longer, the recognition accuracy of both AR-SSVEP and CS-SSVEP increased. The peak value was reached at 3 s. The ITR increased first and then slowly decreased after reaching the peak value.

SIGNIFICANCE: Our study indicates that the conclusions based on CS-SSVEP cannot be simply applied to AR-SSVEP, and it is not advisable to set too many stimulus targets in the AR display device.

RevDate: 2022-04-28
CmpDate: 2022-04-28

van Velthoven EAM, van Stuijvenberg OC, Haselager DRE, et al (2022)

Ethical implications of visual neuroprostheses-a systematic review.

Journal of neural engineering, 19(2):.

Objective. The aim of this review was to systematically identify the ethical implications of visual neuroprostheses.Approach. A systematic search was performed in both PubMed and Embase using a search string that combined synonyms for visual neuroprostheses, brain-computer interfaces (BCIs), cochlear implants (CIs), and ethics. We chose to include literature on BCIs and CIs, because of their ethically relavant similarities and functional parallels with visual neuroprostheses.Main results. We included 84 articles in total. Six focused specifically on visual prostheses. The other articles focused more broadly on neurotechnologies, on BCIs or CIs. We identified 169 ethical implications that have been categorized under seven main themes: (a) benefits for health and well-being; (b) harm and risk; (c) autonomy; (d) societal effects; (e) clinical research; (f) regulation and governance; and (g) involvement of experts, patients and the public.Significance. The development and clinical use of visual neuroprostheses is accompanied by ethical issues that should be considered early in the technological development process. Though there is ample literature on the ethical implications of other types of neuroprostheses, such as motor neuroprostheses and CIs, there is a significant gap in the literature regarding the ethical implications of visual neuroprostheses. Our findings can serve as a starting point for further research and normative analysis.

RevDate: 2022-04-29

Jaipuria J, Karimi AM, Singh A, et al (2021)

Pitcher pot neourethral modification of ileal orthotopic neobladder achieves satisfactory long-term functional and quality of life outcomes with low clean intermittent self-catheterization rate.

BJUI compass, 2(4):292-299.

Objective: To describe a decade of our experience with a neo-urethral modification of ileal orthotopic neobladder (pitcher pot ONB). Multiple investigators have reported similar modifications. However, long-term longitudinal functional and quality of life (QOL) outcomes are lacking.

Methods: Prospectively maintained hospital registry for 238 ONB patients comprising a mix of open and robotic surgery cohorts from 2007 to 2017, and minimum of 2 years of follow-up was retrospectively queried. QOL was evaluated using Bladder Cancer Index (BCI). Longitudinal trends of QOL domain parameters were analysed. List of perioperative variables that have a biologically plausible association with continence, potency, and post-operative BCI QOL sexual, urinary, and bowel domain scores was drawn. Variables included surgery type, Body Mass Index (BMI), T and N stage, neurovascular bundle (NVB) sparing, age, and related pre-operative BCI QOL domain score. Prognostic associations were analysed using multivariable Cox proportional hazard models and multilevel mixed-effects modeling.

Results: The study comprised 80 and 158 patients who underwent open and robotic sandwich technique cohorts, respectively. Open surgery was associated with significantly higher "any" complication (40% vs 27%, P-value .050) and "major" complication rate (15% vs 11%, P-value .048). All patients developed a bladder capacity >400 cc with negligible post-void residual urine, and all but one patient achieved spontaneous voiding by the end of study period (<1% clean intermittent self-catheterization [CISC] rate). By 15 months, QOL for all three domains had recovered to reach a plateau. About 45% of patients achieved potency, and the median time to achieve day and night time continence was 9 and 12 months respectively. Lower age and NVBs spared during surgery were found to be significantly associated with the earlier achievement of potency, day and night time continence, as well as better urinary and sexual summary QOL scores.

Conclusions: Pitcher pot neobladder achieves satisfactory long-term functional and QOL outcomes with negligible CISC rate. Results were superior with incremental nerves spared during surgery.

RevDate: 2022-04-28
CmpDate: 2022-04-28

Jalilpour S, G Müller-Putz (2022)

Toward passive BCI: asynchronous decoding of neural responses to direction- and angle-specific perturbations during a simulated cockpit scenario.

Scientific reports, 12(1):6802.

Neuroimaging studies have provided proof that loss of balance evokes specific neural transient wave complexes in electroencephalography (EEG), called perturbation evoked potentials (PEPs). Online decoding of balance perturbations from ongoing EEG signals can establish the possibility of implementing passive brain-computer interfaces (pBCIs) as a part of aviation/driving assistant systems. In this study, we investigated the feasibility of identifying the existence and expression of perturbations in four different conditions by using EEG signals. Fifteen healthy participants experienced four various postural changes while they sat in a glider cockpit. Sudden perturbations were exposed by a robot connected to a glider and moved to the right and left directions with tilting angles of 5 and 10 degrees. Perturbations occurred in an oddball paradigm in which participants were not aware of the time and expression of the perturbations. We employed a hierarchical approach to separate the perturbation and rest, and then discriminate the expression of perturbations. The performance of the BCI system was evaluated by using classification accuracy and F1 score. Asynchronously, we achieved average accuracies of 89.83 and 73.64% and average F1 scores of 0.93 and 0.60 for binary and multiclass classification, respectively. These results manifest the practicality of pBCI for the detection of balance disturbances in a realistic situation.

RevDate: 2022-04-25

Han JJ (2022)

A man in a completely locked-in state produces intelligible sentences using a brain-computer interface.

Patients with amyotrophic lateral sclerosis may enter into a completely locked-in state without any capability for communication using neuromuscular output. Using an auditory-guided neurofeedback-based strategy with implantable sensors in the motor cortex, scientists were able to help a patient in this state produce intelligible sentences.

RevDate: 2022-04-29

Wang Y, Yang Z, Ji H, et al (2022)

Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System.

Frontiers in psychology, 13:833007.

The brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals' features based on transfer learning and ensemble learning theory. In detail, we used the Independent Component Analysis (ICA) algorithm for the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern (CSP) algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study's findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface.

RevDate: 2022-04-29

Liu L, Jin M, Zhang L, et al (2022)

Brain-Computer Interface-Robot Training Enhances Upper Extremity Performance and Changes the Cortical Activation in Stroke Patients: A Functional Near-Infrared Spectroscopy Study.

Frontiers in neuroscience, 16:809657.

Introduction: We evaluated the efficacy of brain-computer interface (BCI) training to explore the hypothesized beneficial effects of physiotherapy alone in chronic stroke patients with moderate or severe paresis. We also focused on the neuroplastic changes in the primary motor cortex (M1) after BCI training.

Methods: In this study, 18 hospitalized chronic stroke patients with moderate or severe motor deficits participated. Patients were operated on for 20 sessions and followed up after 1 month. Functional assessments were performed at five points, namely, pre1-, pre2-, mid-, post-training, and 1-month follow-up. Wolf Motor Function Test (WMFT) was used as the primary outcome measure, while Fugl-Meyer Assessment (FMA), its wrist and hand (FMA-WH) sub-score and its shoulder and elbow (FMA-SE) sub-score served as secondary outcome measures. Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS) at baseline and after 20 sessions of BCI training. Pearson correlation analysis was used to evaluate functional connectivity (FC) across time points.

Results: Compared to the baseline, better functional outcome was observed after BCI training and 1-month follow-up, including a significantly higher probability of achieving a clinically relevant increase in the WMFT full score (ΔWMFT score = 12.39 points, F = 30.28, and P < 0.001), WMFT completion time (ΔWMFT time = 248.39 s, F = 16.83, and P < 0.001), and FMA full score (ΔFMA-UE = 12.72 points, F = 106.07, and P < 0.001), FMA-WH sub-score (ΔFMA-WH = 5.6 points, F = 35.53, and P < 0.001), and FMA-SE sub-score (ΔFMA-SE = 8.06 points, F = 22.38, and P < 0.001). Compared to the baseline, after BCI training the FC between the ipsilateral M1 and the contralateral M1 was increased (P < 0.05), which was the same as the FC between the ipsilateral M1 and the ipsilateral frontal lobe, and the FC between the contralateral M1 and the contralateral frontal lobe was also increased (P < 0.05).

Conclusion: The findings demonstrate that BCI-based rehabilitation could be an effective intervention for the motor performance of patients after stroke with moderate or severe upper limb paresis and represents a potential strategy in stroke neurorehabilitation. Our results suggest that FC between ipsilesional M1 and frontal cortex might be enhanced after BCI training.

Clinical Trial Registration: www.chictr.org.cn, identifier: ChiCTR2100046301.

RevDate: 2022-04-29

Zhou Q, Cheng R, Yao L, et al (2022)

Neurofeedback Training of Alpha Relative Power Improves the Performance of Motor Imagery Brain-Computer Interface.

Frontiers in human neuroscience, 16:831995.

Significant variation in performance in motor imagery (MI) tasks impedes their wide adoption for brain-computer interface (BCI) applications. Previous researchers have found that resting-state alpha-band power is positively correlated with MI-BCI performance. In this study, we designed a neurofeedback training (NFT) protocol based on the up-regulation of the alpha band relative power (RP) to investigate its effect on MI-BCI performance. The principal finding of this study is that alpha NFT could successfully help subjects increase alpha-rhythm power and improve their MI-BCI performance. An individual difference was also found in this study in that subjects who increased alpha power more had a better performance improvement. Additionally, the functional connectivity (FC) of the frontal-parietal (FP) network was found to be enhanced after alpha NFT. However, the enhancement failed to reach a significant level after multiple comparisons correction. These findings contribute to a better understanding of the neurophysiological mechanism of cognitive control through alpha regulation.

RevDate: 2022-04-25

Belkacem AN, Falk TH, Yanagisawa T, et al (2022)

Editorial: Cognitive and Motor Control Based on Brain-Computer Interfaces for Improving the Health and Well-Being in Older Age.

Frontiers in human neuroscience, 16:881922.

RevDate: 2022-04-29
CmpDate: 2022-04-26

Pan J, Yang F, Qiu L, et al (2022)

Fusion of EEG-Based Activation, Spatial, and Connection Patterns for Fear Emotion Recognition.

Computational intelligence and neuroscience, 2022:3854513.

At present, emotion recognition based on electroencephalograms (EEGs) has attracted much more attention. Current studies of affective brain-computer interfaces (BCIs) focus on the recognition of happiness and sadness using brain activation patterns. Fear recognition involving brain activities in different spatial distributions and different brain functional networks has been scarcely investigated. In this study, we propose a multifeature fusion method combining energy activation, spatial distribution, and brain functional connection network (BFCN) features for fear emotion recognition. The affective brain pattern was identified by not only the power activation features of differential entropy (DE) but also the spatial distribution features of the common spatial pattern (CSP) and the EEG phase synchronization features of phase lock value (PLV). A total of 15 healthy subjects took part in the experiment, and the average accuracy rate was 85.00% ± 8.13%. The experimental results showed that the fear emotions of subjects were fully stimulated and effectively identified. The proposed fusion method on fear recognition was thus validated and is of great significance to the development of effective emotional BCI systems.

RevDate: 2022-04-29
CmpDate: 2022-04-26

Huang Z, Cheng L, Y Liu (2022)

Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training.

Computational intelligence and neuroscience, 2022:6752067.

Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affects all aspects of our lives all the time. Accurate identification of human emotional states and further application in artificial intelligence can better improve and assist human life. Therefore, the research on emotion recognition has attracted the attention of many scholars in the field of artificial intelligence in recent years. Brain electrical signal conversion becomes critical, and it needs a brain electrical signal processing method to extract the effective signal to realize the human-computer interaction However, nonstationary nonlinear characteristics of EEG signals bring great challenge in characteristic signal extraction. At present, although there are many feature extraction methods, none of them can reflect the global feature of the signal. The following solutions are used to solve the above problems: (1) this paper proposed an ICA and sample entropy algorithm-based framework for feature extraction of EEG signals, which has not been applied for EEG and (2) simulation signals were used to verify the feasibility of this method, and experiments were carried out on two real-world data sets, to show the advantages of the new algorithm in feature extraction of EEG signals.

RevDate: 2022-04-29

Li L, Zhang Y, Huang L, et al (2022)

Robot Assisted Treatment of Hand Functional Rehabilitation Based on Visual Motor Imagination.

Frontiers in aging neuroscience, 14:870871.

This pilot study implements a hybrid brain computer interface paradigm based on motor imagery (MI) and steady-state visual evoked potential (SSVEP), in order to explore the neural mechanism and clinical effect of MI-SSVEP intervention paradigm on upper limb functional rehabilitation. In this study, EEG data of 12 healthy participants were collected, and the activation regions of MI-SSVEP paradigm were identified by power spectral density (PSD). By analyzing the inter trial phase consistency (ITPC) of characteristic regions and the causal relationship of brain network, the motor cognitive process including high-level somatosensory joint cortex in the intervention process of MI-SSVEP was studied. Subsequently, this study verified the clinical effect of MI-SSVEP intervention paradigm for 61 stroke patients. The results show that the robot assisted therapy using MI-SSVEP intervention paradigm can more effectively improve the rehabilitation effect of patients.

RevDate: 2022-04-24

Gehrke L, Lopes P, Klug M, et al (2022)

Neural Sources of Prediction Errors Detect Unrealistic VR Interactions.

Journal of neural engineering [Epub ahead of print].

Objective Neural interfaces hold significant promise to implicitly track user experience. Their application in VR/AR simulations is especially favorable as it allows user assessment without breaking the immersive experience. In VR, designing immersion is one key challenge. Subjective questionnaires are the established metrics to assess the effectiveness of immersive VR simulations. However, administering such questionnaires requires breaking the immersive experience they are supposed to assess. Approach We present a complimentary metric based on a ERPs. For the metric to be robust, the neural signal employed must be reliable. Hence, it is beneficial to target the neural signal's cortical origin directly, efficiently separating signal from noise. To test this new complementary metric, we designed a reach-to-tap paradigm in VR to probe EEG and movement adaptation to visuo-haptic glitches. Our working hypothesis was, that these glitches, or violations of the predicted action outcome, may indicate a disrupted user experience. Main Results Using prediction error negativity features, we classified VR glitches with ~77\% accuracy. We localized the EEG sources driving the classification and found midline cingulate EEG sources and a distributed network of parieto-occipital EEG sources to enable the classification success. Significance Prediction error signatures from these sources reflect violations of user's predictions during interaction with AR/VR, promising a robust and targeted marker for adaptive user interfaces.

RevDate: 2022-04-29
CmpDate: 2022-04-26

Butt AM, Alsaffar H, Alshareef M, et al (2022)

AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements.

Sensors (Basel, Switzerland), 22(8):.

Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in tandem with a brain-computer interface (BCI) device to predict brain signals corresponding to sitting, standing and walking postures of a person. Posture classification was attained with an accuracy range between 87-93% from FBG and BCI signals using machine learning models such as K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and naïve Bayes (NB). These models were used to identify electrodes responding to sitting, standing and walking activities of four users from a 16 channel BCI device. Six electrode positions based on the 10-20 system for electroencephalography (EEG) were identified as the most sensitive to plantar activities and found to be consistent with clinical investigations of the sensorimotor cortex during foot movement. A prediction of brain EEG corresponding to given FBG data with lowest mean square error (MSE) values (0.065-0.109) was made with the selection of a long-short term memory (LSTM) machine learning model when compared to the recurrent neural network (RNN) and gated recurrent unit (GRU) models.

RevDate: 2022-04-29
CmpDate: 2022-04-26

Algarni M, Saeed F, Al-Hadhrami T, et al (2022)

Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM).

Sensors (Basel, Switzerland), 22(8):.

Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (EEG) signals that can be used by the Brain-Computer Interface (BCI), to provide better human-machine interactions. Several studies have been conducted in the field of emotion recognition. However, one of the most important issues facing the emotion recognition process, using EEG signals, is the accuracy of recognition. This paper proposes a deep learning-based approach for emotion recognition through EEG signals, which includes data selection, feature extraction, feature selection and classification phases. This research serves the medical field, as the emotion recognition model helps diagnose psychological and behavioral disorders. The research contributes to improving the performance of the emotion recognition model to obtain more accurate results, which, in turn, aids in making the correct medical decisions. A standard pre-processed Database of Emotion Analysis using Physiological signaling (DEAP) was used in this work. The statistical features, wavelet features, and Hurst exponent were extracted from the dataset. The feature selection task was implemented through the Binary Gray Wolf Optimizer. At the classification stage, the stacked bi-directional Long Short-Term Memory (Bi-LSTM) Model was used to recognize human emotions. In this paper, emotions are classified into three main classes: arousal, valence and liking. The proposed approach achieved high accuracy compared to the methods used in past studies, with an average accuracy of 99.45%, 96.87% and 99.68% of valence, arousal, and liking, respectively, which is considered a high performance for the emotion recognition model.

RevDate: 2022-04-29
CmpDate: 2022-04-26

Phadikar S, Sinha N, Ghosh R, et al (2022)

Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter.

Sensors (Basel, Switzerland), 22(8):.

Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain-computer interface (BCI) system as well as in various medical diagnoses. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm. At first, the artifact EEG signal is identified through a pre-trained classifier. Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. Finally, the artifact-free EEG is reconstructed from corrected wavelet coefficients through inverse WPD. To optimize the filter parameters, two meta-heuristic algorithms are used in this paper for the first time. The proposed system is first validated on simulated EEG data and then tested on real EEG data. The proposed approach achieved average mutual information (MI) as 2.9684 ± 0.7045 on real EEG data. The result reveals that the proposed system outperforms recently developed denoising techniques with higher average MI, which indicates that the proposed approach is better in terms of quality of reconstruction and is fully automatic.

RevDate: 2022-04-29

Afzali M, JS Boateng (2022)

Composite Fish Collagen-Hyaluronate Based Lyophilized Scaffolds Modified with Sodium Alginate for Potential Treatment of Chronic Wounds.

Polymers, 14(8):.

Chronic wounds are characterized by both decreased collagen deposition and increased collagen breakdown. It is reasonable to hypothesize that exogenous collagen can potentially promote wound healing by reducing degradation enzymes in the wound environment and disrupting the cycle of chronicity. Therefore, this study aimed to develop an optimal combination of fish collagen (FCOL), sodium alginate (SA), and hyaluronic acid (HA) loaded with bovine serum albumin (BSA) as a model protein fabricated as lyophilized scaffolds. The effects of sodium alginate (SA#) with higher mannuronic acid (M) were compared to sodium alginate (SA*) with higher guluronic acid (G). The SA* with higher G resulted in elegant scaffolds with hardness ranging from 3.74 N-4.29 N that were able to withstand the external force due to the glycosidic bonds in guluronic acid. Furthermore, the high G content also had a significant effect on the pore size, pore shape, and porosity. The water absorption (WA) ranged from 380-1382 (%) and equilibrium water content (EWC) 79-94 (%) after 24 h incubation at 37 °C. The SA* did not affect the water vapor transmission rate (WVTR) but incorporating BSA significantly increased the WVTR making these wound dressing scaffolds capable of absorbing about 50% exudate from a heavily exuding chronic wound. The protein released from the composite systems was best explained by the Korsmeyer-Peppas model with regression R2 values ranging from 0.896 to 0.971 and slope or n < 0.5 indicating that the BSA release mechanism was governed by quasi-Fickian diffusion. Cell viability assay showed that the scaffolds did not inhibit the proliferation of human dermal fibroblasts and human epidermal keratinocytes, and are therefore biocompatible. In vitro blood analysis using human whole blood confirmed that the BSA-loaded SA*:FCOL:HA scaffolds reduced the blood clotting index (BCI) by up to 20% compared to a commercially available sponge for chronic wounds. These features confirm that SA*:FCOL:HA scaffolds could be applied as a multifunctional wound dressing.

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Rajesh Rao has written the perfect introduction to the exciting world of brain-computer interfaces. The book is remarkably comprehensive — not only including full descriptions of classic and current experiments but also covering essential background concepts, from the brain to Bayes and back. Brain-Computer Interfacing will be welcomed by a wide range of intelligent readers interested in understanding the first steps toward the symbiotic merger of brains and computers. Eberhard E. Fetz, UW

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