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Bibliography on: Brain-Computer Interface

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ESP: PubMed Auto Bibliography 27 Sep 2022 at 01:31 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: 2022-09-24

Nathwani JN, Baucom MR, Salvator A, et al (2022)

Evaluating the Utility of High Sensitivity Troponin in Blunt Cardiac Injury.

The Journal of surgical research, 281:104-111 pii:S0022-4804(22)00535-2 [Epub ahead of print].

INTRODUCTION: Screening for blunt cardiac injury (BCI) includes obtaining a serum troponin level and an electrocardiogram for patients diagnosed with a sternal fracture. Our institution has transitioned to the use of a high sensitivity troponin I (hsTnI). The aim of this study was to determine whether hsTnI is comparable to troponin I (TnI) in identifying clinically significant BCI.

MATERIALS AND METHODS: Trauma patients presenting to a level I trauma center over a 24-mo period with the diagnosis of sternal fracture were screened for BCI. Any initial TnI more than 0.04 ng/mL or hsTnI more than 18 ng/L was considered positive for potential BCI. Clinically significant BCI was defined as a new-bundle branch block, ST wave change, echocardiogram change, or need for cardiac catheterization.

RESULTS: Two hundred sixty five patients with a sternal fracture were identified, 161 underwent screening with TnI and 104 with hsTnI. For TnI, the sensitivity and specificity for detection of clinically significant BCI was 0.80 and 0.79, respectively. For hsTnI, the sensitivity and specificity for detection of clinically significant BCI was 0.71 and 0.69, respectively. A multivariate analysis demonstrated the odds ratio for significant BCI with a positive TnI was 14.4 (95% confidence interval, 3.9-55.8, P < 0.0001) versus an odds ratio of 5.48 (95% confidence interval 1.9-15.7, P = 0.002) in the hsTnI group.

CONCLUSIONS: The sensitivity of hsTnI is comparable to TnI for detection of significant BCI. Additional investigation is needed to determine the necessity and interval for repeat testing and the need for additional diagnostic testing.

RevDate: 2022-09-23

Kapgate D (2022)

Effective 2-D cursor control system using hybrid SSVEP + P300 visual brain computer interface.

Medical & biological engineering & computing [Epub ahead of print].

A cursor control system based on brain-computer interface (BCI) provides efficient computer access. These systems operate without any muscular activity from the user. Conventional BCI-based cursor control systems have several limitations. Therefore, hybrid SSVEP + P300 visual BCI (VBCI)-based cursor control is needed to overcome these limitations. This paper explores the feasibility of using noninvasive hybrid SSVEP + P300 VBCI for cursor control as a universal form of computer access. The proposed cursor control system has a graphical user interface (GUI) design that simultaneously evokes both SSVEP and P300 signals in the human cortex. The performance metrics of the proposed system are compared with conventional SSVEP VBCI and P300 VBCI-based cursor control systems. The proposed hybrid SSVEP + P300 BCI-based cursor control system achieves a maximum accuracy of 97.51% with a 27.15 bit/min information transfer rate (ITR). The results proved that the proposed system performed more efficiently than other systems. The proposed system was tested in a noisy environment and found to be suitable for real-world applications.

RevDate: 2022-09-23

Elston TW, JD Wallis (2022)

Decoding cognition in real-time.

Trends in cognitive sciences pii:S1364-6613(22)00201-7 [Epub ahead of print].

How can we study unobservable cognitive processes that cannot be measured directly? This has been an enduring challenge for cognitive scientists. In this essay we discuss advances in neurotechnology that could allow cognitive processes to be decoded in real-time and the implications that this may have for cognitive science and the treatment of neuropsychiatric disease.

RevDate: 2022-09-23

Shah U, Alzubaidi M, Mohsen F, et al (2022)

The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review.

Sensors (Basel, Switzerland), 22(18): pii:s22186975.

Background: Brain traumas, mental disorders, and vocal abuse can result in permanent or temporary speech impairment, significantly impairing one's quality of life and occasionally resulting in social isolation. Brain-computer interfaces (BCI) can support people who have issues with their speech or who have been paralyzed to communicate with their surroundings via brain signals. Therefore, EEG signal-based BCI has received significant attention in the last two decades for multiple reasons: (i) clinical research has capitulated detailed knowledge of EEG signals, (ii) inexpensive EEG devices, and (iii) its application in medical and social fields. Objective: This study explores the existing literature and summarizes EEG data acquisition, feature extraction, and artificial intelligence (AI) techniques for decoding speech from brain signals. Method: We followed the PRISMA-ScR guidelines to conduct this scoping review. We searched six electronic databases: PubMed, IEEE Xplore, the ACM Digital Library, Scopus, arXiv, and Google Scholar. We carefully selected search terms based on target intervention (i.e., imagined speech and AI) and target data (EEG signals), and some of the search terms were derived from previous reviews. The study selection process was carried out in three phases: study identification, study selection, and data extraction. Two reviewers independently carried out study selection and data extraction. A narrative approach was adopted to synthesize the extracted data. Results: A total of 263 studies were evaluated; however, 34 met the eligibility criteria for inclusion in this review. We found 64-electrode EEG signal devices to be the most widely used in the included studies. The most common signal normalization and feature extractions in the included studies were the bandpass filter and wavelet-based feature extraction. We categorized the studies based on AI techniques, such as machine learning and deep learning. The most prominent ML algorithm was a support vector machine, and the DL algorithm was a convolutional neural network. Conclusions: EEG signal-based BCI is a viable technology that can enable people with severe or temporal voice impairment to communicate to the world directly from their brain. However, the development of BCI technology is still in its infancy.

RevDate: 2022-09-23

Cousens GA, Fotis MM, Bradshaw CM, et al (2022)

Characterization of Retronasal Airflow Patterns during Intraoral Fluid Discrimination Using a Low-Cost, Open-Source Biosensing Platform.

Sensors (Basel, Switzerland), 22(18): pii:s22186817.

Nasal airflow plays a critical role in olfactory processes, and both retronasal and orthonasal olfaction involve sensorimotor processes that facilitate the delivery of volatiles to the olfactory epithelium during odor sampling. Although methods are readily available for monitoring nasal airflow characteristics in laboratory and clinical settings, our understanding of odor sampling behavior would be enhanced by the development of inexpensive wearable technologies. Thus, we developed a method of monitoring nasal air pressure using a lightweight, open-source brain-computer interface (BCI) system and used the system to characterize patterns of retronasal airflow in human participants performing an oral fluid discrimination task. Participants exhibited relatively sustained low-rate retronasal airflow during sampling punctuated by higher-rate pulses often associated with deglutition. Although characteristics of post-deglutitive pulses did not differ across fluid conditions, the cumulative duration, probability, and estimated volume of retronasal airflow were greater during discrimination of perceptually similar solutions. These findings demonstrate the utility of a consumer-grade BCI system in assessing human olfactory behavior. They suggest further that sensorimotor processes regulate retronasal airflow to optimize the delivery of volatiles to the olfactory epithelium and that discrimination of perceptually similar oral fluids may be accomplished by varying the duration of optimal airflow rate.

RevDate: 2022-09-23

Zhang J, Liu D, Chen W, et al (2022)

Deep Convolutional Neural Network for EEG-Based Motor Decoding.

Micromachines, 13(9): pii:mi13091485.

Brain-machine interfaces (BMIs) have been applied as a pattern recognition system for neuromodulation and neurorehabilitation. Decoding brain signals (e.g., EEG) with high accuracy is a prerequisite to building a reliable and practical BMI. This study presents a deep convolutional neural network (CNN) for EEG-based motor decoding. Both upper-limb and lower-limb motor imagery were detected from this end-to-end learning with four datasets. An average classification accuracy of 93.36 ± 1.68% was yielded on the four datasets. We compared the proposed approach with two other models, i.e., multilayer perceptron and the state-of-the-art framework with common spatial patterns and support vector machine. We observed that the performance of the CNN-based framework was significantly better than the other two models. Feature visualization was further conducted to evaluate the discriminative channels employed for the decoding. We showed the feasibility of the proposed architecture to decode motor imagery from raw EEG data without manually designed features. With the advances in the fields of computer vision and speech recognition, deep learning can not only boost the EEG decoding performance but also help us gain more insight from the data, which may further broaden the knowledge of neuroscience for brain mapping.

RevDate: 2022-09-23

Wang M, Zhang Y, Bin J, et al (2022)

Cold Laser Micro-Machining of PDMS as an Encapsulation Layer for Soft Implantable Neural Interface.

Micromachines, 13(9): pii:mi13091484.

PDMS (polydimethylsiloxane) is an important soft biocompatible material, which has various applications such as an implantable neural interface, a microfluidic chip, a wearable brain-computer interface, etc. However, the selective removal of the PDMS encapsulation layer is still a big challenge due to its chemical inertness and soft mechanical properties. Here, we use an excimer laser as a cold micro-machining tool for the precise removal of the PDMS encapsulation layer which can expose the electrode sites in an implantable neural interface. This study investigated and optimized the effect of excimer laser cutting parameters on the electrochemical impedance of a neural electrode by using orthogonal experiment design. Electrochemical impedance at the representative frequencies is discussed, which helps to construct the equivalent circuit model. Furthermore, the parameters of the equivalent circuit model are fitted, which reveals details about the electrochemical property of neural electrode using PDMS as an encapsulation layer. Our experimental findings suggest the promising application of excimer lasers in the micro-machining of implantable neural interface.

RevDate: 2022-09-23

Pap IA, S Oniga (2022)

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence.

International journal of environmental research and public health, 19(18): pii:ijerph191811413.

Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.

RevDate: 2022-09-23

Li Q, Liu Y, Shang Y, et al (2022)

Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition.

Entropy (Basel, Switzerland), 24(9): pii:e24091187.

Recently, emotional electroencephalography (EEG) has been of great importance in brain-computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, stochastic, and susceptible to background noise. Additionally, EEG signal processing network models have the disadvantages of a large number of parameters and long training time. To address the above issues, a novel model is presented in this paper. Initially, a deep sparse autoencoder network (DSAE) was used to remove redundant information from the EEG signal and reconstruct its underlying features. Further, combining a convolutional neural network (CNN) with long short-term memory (LSTM) can extract relevant features from task-related features, mine the correlation between the 32 channels of the EEG signal, and integrate contextual information from these frames. The proposed DSAE + CNN + LSTM (DCRNN) model was experimented with on the public dataset DEAP. The classification accuracies of valence and arousal reached 76.70% and 81.43%, respectively. Meanwhile, we conducted experiments with other comparative methods to further demonstrate the effectiveness of the DCRNN method.

RevDate: 2022-09-23

Muñoz D, Barria P, Cifuentes CA, et al (2022)

EEG Evaluation in a Neuropsychological Intervention Program Based on Virtual Reality in Adults with Parkinson's Disease.

Biosensors, 12(9): pii:bios12090751.

Nowadays, several strategies for treating neuropsychologic function loss in Parkinson's disease (PD) have been proposed, such as physical activity performance and developing games to exercise the mind. However, few studies illustrate the incidence of these therapies in neuronal activity. This work aims to study the feasibility of a virtual reality-based program oriented to the cognitive functions' rehabilitation of PD patients. For this, the study was divided into intervention with the program, acquisition of signals, data processing, and results analysis. The alpha and beta bands' power behavior was determined by evaluating the electroencephalography (EEG) signals obtained during the execution of control tests and games of the "Hand Physics Lab" Software, from which five games related to attention, planning, and sequencing, concentration, and coordination were taken. Results showed the characteristic performance of the cerebral bands during resting states and activity states. In addition, it was determined that the beta band increased its activity in all the cerebral lobes in all the tested games (p-value < 0.05). On the contrary, just one game exhibited an adequate performance of the alpha band activity of the temporal and frontal lobes (p-value < 0.02). Furthermore, the visual attention and the capacity to process and interpret the information given by the surroundings was favored during the execution of trials (p-value < 0.05); thus, the efficacy of the virtual reality program to recover cognitive functions was verified. The study highlights implementing new technologies to rehabilitate people with neurodegenerative diseases.

RevDate: 2022-09-23

Gao S, Yang J, Shen T, et al (2022)

A Parallel Feature Fusion Network Combining GRU and CNN for Motor Imagery EEG Decoding.

Brain sciences, 12(9): pii:brainsci12091233.

In recent years, deep-learning-based motor imagery (MI) electroencephalography (EEG) decoding methods have shown great potential in the field of the brain-computer interface (BCI). The existing literature is relatively mature in decoding methods for two classes of MI tasks. However, with the increase in MI task classes, decoding studies for four classes of MI tasks need to be further explored. In addition, it is difficult to obtain large-scale EEG datasets. When the training data are limited, deep-learning-based decoding models are prone to problems such as overfitting and poor robustness. In this study, we design a data augmentation method for MI-EEG. The original EEG is slid along the time axis and reconstructed to expand the size of the dataset. Second, we combine the gated recurrent unit (GRU) and convolutional neural network (CNN) to construct a parallel-structured feature fusion network to decode four classes of MI tasks. The parallel structure can avoid temporal, frequency and spatial features interfering with each other. Experimenting on the well-known four-class MI dataset BCI Competition IV 2a shows a global average classification accuracy of 80.7% and a kappa value of 0.74. The proposed method improves the robustness of deep learning to decode small-scale EEG datasets and alleviates the overfitting phenomenon caused by insufficient data. The method can be applied to BCI systems with a small amount of daily recorded data.

RevDate: 2022-09-23

Zhao R, Zhang T, Zhou S, et al (2022)

Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm.

Brain sciences, 12(9): pii:brainsci12091159.

Emotion analysis has emerged as one of the most prominent study areas in the field of Brain Computer Interface (BCI) due to the critical role that the human brain plays in the creation of human emotions. In this study, a Multi-objective Immunogenetic Community Division Algorithm Based on Memetic Framework (MFMICD) was suggested to study different emotions from the perspective of brain networks. To improve convergence and accuracy, MFMICD incorporates the unique immunity operator based on the traditional genetic algorithm and combines it with the taboo search algorithm. Based on this approach, we examined how the structure of people's brain networks alters in response to different emotions using the electroencephalographic emotion database. The findings revealed that, in positive emotional states, more brain regions are engaged in emotion dominance, the information exchange between local modules is more frequent, and various emotions cause more varied patterns of brain area interactions than in negative brain states. A brief analysis of the connections between different emotions and brain regions shows that MFMICD is reliable in dividing emotional brain functional networks into communities.

RevDate: 2022-09-23

Liu K, Yu Y, Zeng LL, et al (2022)

Effects of Low Mental Energy from Long Periods of Work on Brain-Computer Interfaces.

Brain sciences, 12(9): pii:brainsci12091152.

Brain-computer interfaces (BCIs) provide novel hands-free interaction strategies. However, the performance of BCIs is affected by the user's mental energy to some extent. In this study, we aimed to analyze the combined effects of decreased mental energy and lack of sleep on BCI performance and how to reduce these effects. We defined the low-mental-energy (LME) condition as a combined condition of decreased mental energy and lack of sleep. We used a long period of work (>=18 h) to induce the LME condition, and then P300- and SSVEP-based BCI tasks were conducted in LME or normal conditions. Ten subjects were recruited in this study. Each subject participated in the LME- and normal-condition experiments within one week. For the P300-based BCI, we used two decoding algorithms: stepwise linear discriminant (SWLDA) and least square regression (LSR). For the SSVEP-based BCI, we used two decoding algorithms: canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA). Accuracy and information transfer rate (ITR) were used as performance metrics. The experimental results showed that for the P300-based BCI, the average accuracy was reduced by approximately 35% (with a SWLDA classifier) and approximately 40% (with a LSR classifier); the average ITR was reduced by approximately 6 bits/min (with a SWLDA classifier) and approximately 7 bits/min (with an LSR classifier). For the SSVEP-based BCI, the average accuracy was reduced by approximately 40% (with a CCA classifier) and approximately 40% (with a FBCCA classifier); the average ITR was reduced by approximately 20 bits/min (with a CCA classifier) and approximately 19 bits/min (with a FBCCA classifier). Additionally, the amplitude and signal-to-noise ratio of the evoked electroencephalogram signals were lower in the LME condition, while the degree of fatigue and the task load of each subject were higher. Further experiments suggested that increasing stimulus size, flash duration, and flash number could improve BCI performance in LME conditions to some extent. Our experiments showed that the LME condition reduced BCI performance, the effects of LME on BCI did not rely on specific BCI types and specific decoding algorithms, and optimizing BCI parameters (e.g., stimulus size) can reduce these effects.

RevDate: 2022-09-23

Chen X, Liu B, Wang Y, et al (2022)

A Spectrally-dense Encoding Method for Designing a High-speed SSVEP-BCI with 120 Stimuli.

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 practical functionality of a brain-computer interface (BCI) is critically affected by the number of stimuli, especially for steady-state visual evoked potential based BCI (SSVEP-BCI), which shows promise for the implementation of a multi-target system for real-world applications. Joint frequency-phase modulation (JFPM) is an effective and widely used method in modulating SSVEPs. However, the ability of JFPM to implement an SSVEP-BCI system with a large number of stimuli, e.g., over 100 stimuli, remains unclear. To address this issue, a spectrally-dense JPFM (sJFPM) method is proposed to encode a broad array of stimuli, which modulates the low-and medium-frequency SSVEPs with a frequency interval of 0.1 Hz and triples the number of stimuli in conventional SSVEP-BCI to 120. To validate the effectiveness of the proposed 120-target BCI system, an offline experiment and a subsequent online experiment testing 18 healthy subjects in total were conducted. The offline experiment verified the feasibility of using sJFPM in designing an SSVEP-BCI system with 120 stimuli. Furthermore, the online experiment demonstrated that the proposed system achieved an average performance of 92.47±1.83% in online accuracy and 213.23±6.60 bits/min in online information transfer rate (ITR), where more than 75% of the subjects attained the accuracy above 90% and the ITR above 200 bits/min. This present study demonstrates the effectiveness of sJFPM in elevating the number of stimuli to more than 100 and extends our understanding of encoding a large number of stimuli by means of finer frequency division.

RevDate: 2022-09-22

Tao Y, Xu W, Wang G, et al (2022)

Decoding Multi-class EEG Signals of Hand Movement Using Multivariate Empirical Mode Decomposition and Convolutional Neural Network.

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) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients. One specific challenge of such BCI is that the classification accuracy is not high enough for multi-class movements. In this study, by using Multivariate Empirical Mode Decomposition (MEMD) and Convolutional Neural Network (CNN), a novel algorithm (MECN) was proposed to decode EEG signals for four kinds of hand movements. Firstly, the MEMD was used to decompose the movement-related electroencephalogram (EEG) signals to obtain the multivariate intrinsic empirical functions (MIMFs). Then, the optimal MIMFs fusion was performed based on sequential forward selection algorithm. Finally, the selected MIMFs were input to the CNN model for discriminating four kinds of hand movements. The average classification accuracy of thirteen subjects over the six-fold cross-validation reached 81.14% for 2s-data before the movement onset and 81.08% for 2s-data after the movement onset. The MECN method achieved statistically significant improvement on the state-of-the-art methods. The results showed that the algorithm proposed in this study can effectively decode four kinds of hand movements based on EEG signals.

RevDate: 2022-09-23

Anonymous (2022)

Abstracts of Presentations at the Association of Clinical Scientists 143rd Meeting Louisville, KY May 11-14,2022.

Annals of clinical and laboratory science, 52(3):511-525.

RevDate: 2022-09-21

Wen Y, He W, Y Zhang (2022)

A new attention-based 3D densely connected cross-stage-partial network for motor imagery classification in BCI.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: The challenge for motor imagery (MI) in brain-computer interface (BCI) systems is finding a reliable classification model that has high classification accuracy and excellent robustness. Currently, one of the main problems leading to degraded classification performance is the inaccuracy caused by nonstationarities and low signal-to-noise ratio in electroencephalogram (EEG) signals.

APPROACH: This study proposes a novel attention-based 3D densely connected cross-stage-partial network (DCSPNet) model to achieve efficient EEG-based MI classification. This is an end-to-end classification model framework based on the convolutional neural network (CNN) architecture. In this framework, to fully utilize the complementary features in each dimension, the optimal features are extracted adaptively from the EEG signals through the spatial-spectral-temporal (SST) attention mechanism. The 3D DCSPNet is introduced to reduce the gradient loss by segmenting the extracted feature maps to strengthen the network learning capability. Additionally, the design of the densely connected structure increases the robustness of the network.

MAIN RESULTS: The performance of the proposed method was evaluated using the BCI competition IV 2a and the high gamma dataset, achieving an average accuracy of 84.45% and 97.88%, respectively. Our method outperformed most state-of-the-art classification algorithms, demonstrating its effectiveness and strong generalization ability.

SIGNIFICANCE: The experimental results show that our method is promising for improving the performance of MI-BCI. As a general framework based on time-series classification, it can be applied to BCI-related fields.

RevDate: 2022-09-21

Shimizu H, R Srinivasan (2022)

Improving classification and reconstruction of imagined images from EEG signals.

PloS one, 17(9):e0274847 pii:PONE-D-22-15432.

Decoding brain activity related to specific tasks, such as imagining something, is important for brain computer interface (BCI) control. While decoding of brain signals, such as functional magnetic resonance imaging (fMRI) signals and electroencephalography (EEG) signals, during observing visual images and while imagining images has been previously reported, further development of methods for improving training, performance, and interpretation of brain data was the goal of this study. We applied a Sinc-EEGNet to decode brain activity during perception and imagination of visual stimuli, and added an attention module to extract the importance of each electrode or frequency band. We also reconstructed images from brain activity by using a generative adversarial network (GAN). By combining the EEG recorded during a visual task (perception) and an imagination task, we have successfully boosted the accuracy of classifying EEG data in the imagination task and improved the quality of reconstruction by GAN. Our result indicates that the brain activity evoked during the visual task is present in the imagination task and can be used for better classification of the imagined image. By using the attention module, we can derive the spatial weights in each frequency band and contrast spatial or frequency importance between tasks from our model. Imagination tasks are classified by low frequency EEG signals over temporal cortex, while perception tasks are classified by high frequency EEG signals over occipital and frontal cortex. Combining data sets in training results in a balanced model improving classification of the imagination task without significantly changing performance in the visual task. Our approach not only improves performance and interpretability but also potentially reduces the burden on training since we can improve the accuracy of classifying a relatively hard task with high variability (imagination) by combining with the data of the relatively easy task, observing visual images.

RevDate: 2022-09-21

Zhang W, Song A, Zeng H, et al (2022)

The Effects of Bilateral Phase-Dependent Closed-Loop Vibration Stimulation with Motor Imagery Paradigm.

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

Vibration stimulation has been shown to have the potential to improve the activation pattern of unilateral motor imagery (MI) and to promote motor recovery. However, in the widely used left and right hand MI brain-computer interface (BCI) paradigm, the vibration stimuli cannot be directly applied to the imaginary side due to the spontaneity of imagery. In this study, we proposed a method of phase-dependent closed-loop vibration stimulation to be applied on both hands, and explored the effects of different vibration stimuli on the left and right hand MI-BCI. Eighteen healthy subjects were recruited and asked to perform, in sequence, MI tasks under three different conditions of vibratory feedback, which were no vibration stimulus (MI), phase-dependent closed-loop vibration stimulus (PDS), and continuous vibration stimulus (CS). Then the performance of the left and right hand MI-BCI and the patterns of brain oscillation were compared and analyzed under these different stimulation conditions. The results showed that vibration stimulation effectively boosted the activation of the sensorimotor cortex and enhanced the functional connectivity among sensorimotor-related brain regions during MI. The closed-loop stimulation evoked stronger event-related desynchronization patterns on the contralateral side of the imagined hand compared to continuous stimulation. There was a more obvious distinction between left hand task and right hand task. In addition, phase-dependent closed-loop vibration stimulation increased classification accuracy by approximately 7% (paired t-test, p=0.004, n=18) compared to MI alone, while continuous vibration stimulation only increased it by 4% (paired t-test, p=0.067, n=18). This result further demonstrated the effectiveness of the phase-dependent closed-loop vibration stimulation method in improving the overall performance of the MI paradigm and is expected to be further applied in areas such as stroke rehabilitation in the future.

RevDate: 2022-09-20

Liang Z, Wang X, Zhao J, et al (2022)

Comparative study of attention-related features on attention monitoring systems with a single EEG channel.

Journal of neuroscience methods pii:S0165-0270(22)00237-0 [Epub ahead of print].

The easy-to-use attention monitoring systems usually detect the participant's attentional status via processing electroencephalogram (EEG) data recorded from a single FPz channel. But due to the influence of noises and artifacts, the attention-monitoring performance needs to be further improved to suit different individuals and devices. This paper compared the attention-related features extracted using four state-of-the-art methods including delta/beta1 (D/B1), α+β+δ+θ+R, entropy and optimized complex network (OCN). The classification performance was evaluated using receiver operating characteristic (ROC) curves and area under the ROC curves (AUC) on two EEG data acquisition devices, i.e., a BrainAmp device with high precision and a Sichiray device with low cost, respectively. Considering the varied performance on different individuals and devices, this paper proposed a novel Mutual information-based feature fusion (MIFF) method, selecting the optimal combinations of the attention-related features for classification, to enhance the attention detection performance. The experimental results showed that the proposed MIFF method outperformed the state-of-the-art methods regardless of data length on both devices. Especially, the proposed method with data length of 2.5s achieved an average AUC of 0.8505 on the low-cost Sichiray device, which is 56.08% higher than that of D/B1, 27.28% higher than that of α+β+δ+θ+R, 17.42% higher than that of entropy, and 15.48% higher than that of OCN.

RevDate: 2022-09-20

Qu T, Jin J, Xu R, et al (2022)

Riemannian distance based channel selection and feature extraction combining discriminative time-frequency bands and Riemannian tangent space for MI-BCIs.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Motor imagery-based brain computer interfaces (MI-BCIs) have been widely researched because they do not demand external stimuli and have a high degree of maneuverability. In most scenarios, superabundant selected channels, fixed time windows, and frequency bands would certainly affect the performance of MI-BCIs due to the neurophysiological diversities between different individuals. In this study, we tended to effectively use the Riemannian geometry of spatial covariance matrix to extract more robust features and thus enhance the decoding efficiency.

APPROACH: First, we propose a Riemannian distance-based EEG channel selection method (RDCS), which preliminarily reduces the information redundancy in the first stage. Second, we extract discriminative Riemannian Tangent Space features of EEG signals of selected channels from the most discriminant time-frequency bands (DTFRTS) to further enhance decoding accuracy for MI-BCIs. Finally, we trained a support vector machine (SVM) model with a linear kernel to classify our extracted discriminative Riemannian features and evaluated our proposed method using publicly available BCI Competition Ⅳ dataset Ⅰ (DS1) and Competition Ⅲ dataset Ⅲa (DS2).

MAIN RESULTS: The experimental results showed that the average classification accuracy with the selected 10-channel EEG signals of our method is 88.1% and 91.6% in DS1 and DS2 respectively. The average improvements are 24.3% & 27.1% on DS1 and 4.4% & 14.2% on DS2 for 10 & 20 selected channels, respectively.

SIGNIFICANCE: These results showed that our proposed method is a promising candidate for performance improvement of MI-BCIs.

RevDate: 2022-09-20

Matsukawa Y, Naito Y, Ishida S, et al (2022)

Two types of detrusor underactivity in men with nonneurogenic lower urinary tract symptoms.

Neurourology and urodynamics [Epub ahead of print].

AIMS: To clarify the clinical features of men with nonneurogenic detrusor underactivity (DU) by focusing on storage dysfunction (SD).

METHODS: We retrospectively reviewed the clinical and urodynamic data of men with nonneurogenic DU. Patients were divided into two groups according to the presence or absence of SD, such as detrusor overactivity (DO) and reduced bladder compliance (BC). Patient characteristics, lower urinary tract symptoms (LUTS), and urodynamic parameters were compared. DU was defined as bladder contractility index (BCI) ≤ 100 and bladder outlet obstruction index (BOOI) ≤ 40.

RESULTS: Of 212 men with DU, 123 (58.0%) had concomitant SD (SD + DU group), and 89 (42.0%) had only DU (DU-only group). Age, prostate volume, and severity of storage symptoms were significantly higher in the SD + DU group. Particularly, >80% of men in the SD + DU group met the diagnostic criteria for overactive bladder in Japan, which was significantly higher than the 26% of men in the DU-only group. The frequency of urinary urgency incontinence (UUI) was also significantly higher in the SD + DU group (65% vs. 12% in DU-only group). In contrast, voiding symptoms, including straining, were more severe in the DU-only group. Regarding the urodynamic parameters, compared to the DU-only group, bladder capacity was significantly smaller and BOOI and BCI were significantly higher in the SD + DU group. However, there was no significant difference in the maximum flow rate and bladder voiding efficiency.

CONCLUSIONS: Approximately 60% of men with DU had SD, such as DO and/or reduced BC, whereas the remaining 40% had increased bladder capacity without an increase in detrusor pressure during the storage phase. There were significant differences in the storage and voiding symptoms between the groups. It is important to divide patients with DU based on SD to accurately clarify the clinical picture of DU.

RevDate: 2022-09-20

Guan C, Aflalo T, Zhang CY, et al (2022)

Stability of motor representations after paralysis.

eLife, 11: pii:74478 [Epub ahead of print].

Neural plasticity allows us to learn skills and incorporate new experiences. What happens when our lived experiences fundamentally change, such as after a severe injury? To address this question, we analyzed intracortical population activity in the posterior parietal cortex (PPC) of a tetraplegic adult as she controlled a virtual hand through a brain-computer interface (BCI). By attempting to move her fingers, she could accurately drive the corresponding virtual fingers. Neural activity during finger movements exhibited robust representational structure similar to fMRI recordings of able-bodied individuals' motor cortex, which has previously been shown to reflect able-bodied usage patterns. The finger representational structure was consistent throughout multiple sessions, even though the structure contributed to BCI decoding errors. Within individual BCI movements, the representational structure was dynamic, first resembling muscle activation patterns and then resembling the anticipated sensory consequences. Our results reveal that motor representations in PPC reflect able-bodied motor usage patterns even after paralysis, and BCIs can re-engage these representations to restore lost motor functions.

RevDate: 2022-09-20

Vansteensel MJ, Branco MP, Leinders S, et al (2022)

Methodological Recommendations for Studies on the Daily Life Implementation of Implantable Communication-Brain-Computer Interfaces for Individuals With Locked-in Syndrome.

Neurorehabilitation and neural repair [Epub ahead of print].

Implantable brain-computer interfaces (BCIs) promise to be a viable means to restore communication in individuals with locked-in syndrome (LIS). In 2016, we presented the world-first fully implantable BCI system that uses subdural electrocorticography electrodes to record brain signals and a subcutaneous amplifier to transmit the signals to the outside world, and that enabled an individual with LIS to communicate via a tablet computer by selecting icons in spelling software. For future clinical implementation of implantable communication-BCIs, however, much work is still needed, for example, to validate these systems in daily life settings with more participants, and to improve the speed of communication. We believe the design and execution of future studies on these and other topics may benefit from the experience we have gained. Therefore, based on relevant literature and our own experiences, we here provide an overview of procedures, as well as recommendations, for recruitment, screening, inclusion, imaging, hospital admission, implantation, training, and support of participants with LIS, for studies on daily life implementation of implantable communication-BCIs. With this article, we not only aim to inform the BCI community about important topics of concern, but also hope to contribute to improved methodological standardization of implantable BCI research.

RevDate: 2022-09-21

Pabba K, Widmer RJ, Nguyen V, et al (2022)

Cardiac Contusion Complicated by Heart Failure in a Young Athlete.

JACC. Case reports, 4(17):1124-1128.

Chest trauma is a relatively common injury in athletes. Here, we report a case of a cardiac contusion in a football player that led to hemodynamically significant low-output state. Early invasive management was critical in treatment with imaging playing an important role in diagnosis. (Level of Difficulty: Advanced.).

RevDate: 2022-09-19

Onur Caglar H, Z Duzgun (2022)

Identification of upregulated genes in glioblastoma and glioblastoma cancer stem cells using bioinformatics analysis.

Gene pii:S0378-1119(22)00715-6 [Epub ahead of print].

Glioblastoma (GBM) is the most common malignant brain tumor among adults. Cancer stem cells (CSCs) are known to drive treatment resistance and recurrence. However, a few CSC markers have been identified as therapeutic targets for GBM. This study aimed to show highly coexpressed genes in GBM CSCs and TCGA GBM samples and to identify possible therapeutic targets for GBM. The gene expression profiles of GBM CSCs were obtained from Gene Expression Omnibus database. After the differentially upregulated genes were screened, functional enrichment analyses were performed using DAVID and Reactome databases. For upregulated genes, biological processes were mainly associated with the regulation of transcription. Subsequently, a protein-protein interaction network was constructed for upregulated genes through STRING, in which DUSP6, FGFR3, EGFR, SOX2, NES, and PLP1 were further identified as hub genes via MCC and MNC methods. Expression profiles of hub genes and their association with survival were examined in TCGA GBM dataset using GEPIA2 platform. The expression levels of four hub genes were found to be increased in TCGA GBM samples. Of these, DUSP6 and SOX2 had prognostic value for patients with GBM. Molecular compounds targeting DUSP6 were searched through PubChem database. (E/Z)-BCI and BCI were found to be inhibitors of DUSP6. The molecular docking was performed using Autodock vina 1.02. The compounds showed strong binding capacities by forming various interactions with the ERK2 binding domain of DUSP6. Hence, the current study unravels the potential of (E/Z)-BCI and BCI compounds as possible anti-cancer molecules for GBM treatment.

RevDate: 2022-09-20

Triana-Guzman N, Orjuela-Cañon AD, Jutinico AL, et al (2022)

Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface.

Frontiers in neuroinformatics, 16:961089.

Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.

RevDate: 2022-09-20

Mughal NE, Khan MJ, Khalil K, et al (2022)

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM.

Frontiers in neurorobotics, 16:873239.

The constantly evolving human-machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain-computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.

RevDate: 2022-09-20

Hasslinger J, Meregalli M, S Bölte (2022)

How standardized are "standard protocols"? Variations in protocol and performance evaluation for slow cortical potential neurofeedback: A systematic review.

Frontiers in human neuroscience, 16:887504.

Neurofeedback (NF) aims to alter neural activity by enhancing self-regulation skills. Over the past decade NF has received considerable attention as a potential intervention option for many somatic and mental conditions and ADHD in particular. However, placebo-controlled trials have demonstrated insufficient superiority of NF compared to treatment as usual and sham conditions. It has been argued that the reason for limited NF effects may be attributable to participants' challenges to self-regulate the targeted neural activity. Still, there is support of NF efficacy when only considering so-called "standard protocols," such as Slow Cortical Potential NF training (SCP-NF). This PROSPERO registered systematic review following PRISMA criteria searched literature databases for studies applying SCP-NF protocols. Our review focus concerned the operationalization of self-regulatory success, and protocol-details that could influence the evaluation of self-regulation. Such details included; electrode placement, number of trials, length per trial, proportions of training modalities, handling of artifacts and skill-transfer into daily-life. We identified a total of 63 eligible reports published in the year 2000 or later. SCP-NF protocol-details varied considerably on most variables, except for electrode placement. However, due to the increased availability of commercial systems, there was a trend to more uniform protocol-details. Although, token-systems are popular in SCP-NF for ADHD, only half reported a performance-based component. Also, transfer exercises have become a staple part of SCP-NF. Furthermore, multiple operationalizations of regulatory success were identified, limiting comparability between studies, and perhaps usefulness of so-called transfer-exercises, which purpose is to facilitate the transfer of the self-regulatory skills into every-day life. While studies utilizing SCP as Brain-Computer-Interface mainly focused on the acquisition of successful self-regulation, clinically oriented studies often neglected this. Congruently, rates of successful regulators in clinical studies were mostly low (<50%). The relation between SCP self-regulation and behavior, and how symptoms in different disorders are affected, is complex and not fully understood. Future studies need to report self-regulation based on standardized measures, in order to facilitate both comparability and understanding of the effects on symptoms. When applied as treatment, future SCP-NF studies also need to put greater emphasis on the acquisition of self-regulation (before evaluating symptom outcomes).

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021260087, Identifier: CRD42021260087.

RevDate: 2022-09-16

Premachandran S, Haldavnekar R, Das S, et al (2022)

DEEP Surveillance of Brain Cancer Using Self-Functionalized 3D Nanoprobes for Noninvasive Liquid Biopsy.

ACS nano [Epub ahead of print].

Brain cancers, one of the most fatal malignancies, require accurate diagnosis for guided therapeutic intervention. However, conventional methods for brain cancer prognosis (imaging and tissue biopsy) face challenges due to the complex nature and inaccessible anatomy of the brain. Therefore, deep analysis of brain cancer is necessary to (i) detect the presence of a malignant tumor, (ii) identify primary or secondary origin, and (iii) find where the tumor is housed. In order to provide a diagnostic technique with such exhaustive information here, we attempted a liquid biopsy-based deep surveillance of brain cancer using a very minimal amount of blood serum (5 μL) in real time. We hypothesize that holistic analysis of serum can act as a reliable source for deep brain cancer surveillance. To identify minute amounts of tumor-derived material in circulation, we synthesized an ultrasensitive 3D nanosensor, adopted SERS as a diagnostic methodology, and undertook a DEEP neural network-based brain cancer surveillance. Detection of primary and secondary tumor achieved 100% accuracy. Prediction of intracranial tumor location achieved 96% accuracy. This modality of using patient sera for deep surveillance is a promising noninvasive liquid biopsy tool with the potential to complement current brain cancer diagnostic methodologies.

RevDate: 2022-09-21
CmpDate: 2022-09-20

Rekers S, M Niedeggen (2022)

Intuitive assessment of spatial navigation beyond episodic memory: Feasibility and proof of concept in middle-aged and elderly individuals.

PloS one, 17(9):e0270563.

Deficits in spatial navigation in three-dimensional space are prevalent in various neurological disorders and are a sensitive cognitive marker for prodromal Alzheimer's disease, but are also associated with non-pathological aging. However, standard neuropsychological tests used in clinical settings lack ecological validity to adequately assess spatial navigation. Experimental paradigms, on the other hand, are often too difficult for seniors or patients with cognitive or motor impairments since most require operating a human interface device (HID) or use complex episodic memory tasks. Here, we introduce an intuitive navigation assessment, which is conceptualized using cognitive models of spatial navigation and designed to account for the limited technical experience and diverging impairments of elderly participants and neurological patients. The brief computer paradigm uses videos of hallways filmed with eye tracking glasses, without employing an episodic memory task or requiring participants to operate a HID. Proof of concept data from 34 healthy, middle-aged and elderly participants (56-78 years) provide evidence for the assessment's feasibility and construct validity as a navigation paradigm. Test performance showed normal distribution and was sensitive to age and education, which needs to be considered when investigating the assessment's psychometric properties in larger samples and clinical populations. Correlations of the navigation assessment with other neuropsychological tests confirmed its dependence on visuospatial skills rather than visual episodic memory, with age driving the association with working memory. The novel paradigm is suitable for a differentiated investigation of spatial navigation in elderly individuals and promising for experimental research in clinical settings.

RevDate: 2022-09-20

Zhang W, Wang Z, D Wu (2022)

Multi-Source Decentralized Transfer for Privacy-Preserving BCIs.

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

Transfer learning, which utilizes labeled source domains to facilitate the learning in a target model, is effective in alleviating high intra- and inter-subject variations in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Existing transfer learning approaches usually use the source subjects' EEG data directly, leading to privacy concerns. This paper considers a decentralized privacy-preserving transfer learning scenario: there are multiple source subjects, whose data and computations are kept local, and only the parameters or predictions of their pre-trained models can be accessed for privacy-protection; then, how to perform effective cross-subject transfer for a new subject with unlabeled EEG trials? We propose an offline unsupervised multi-source decentralized transfer (MSDT) approach, which first generates a pre-trained model from each source subject, and then performs decentralized transfer using the source model parameters (in gray-box settings) or predictions (in black-box settings). Experiments on two datasets from two BCI paradigms, motor imagery and affective BCI, demonstrated that MSDT outperformed several existing approaches, which do not consider privacy-protection at all. In other words, MSDT achieved both high privacy-protection and better classification performance.

RevDate: 2022-09-17

Yang J, Zhao W, Liao Y, et al (2022)

Ocular surface disease index questionnaire as a sensitive test for primary screening of chronic ocular graft-versus-host disease.

Annals of translational medicine, 10(16):855.

Background: After allogeneic hematopoietic stem cell transplantation (allo-HSCT), patients are followed up by transplant clinicians. Finding an effective primary screening method that transplant clinicians or patients can master is essential in the early referral of suspected chronic ocular graft-versus-host disease (coGVHD) to an ophthalmologist. This study investigated if the ocular surface disease index (OSDI) questionnaire could be used for coGVHD primary screening.

Methods: This case-controlled, cross-sectional study enrolled 161 allo-HSCT patients. All participants completed an OSDI questionnaire and underwent a silt-lamp examination. Bulbar conjunctival injection (BCI) was assessed using torchlight, while tear volume was measured via the Schirmer test (ST). The receiver operating characteristic curve was used to evaluate the sensitivity, specificity, and cutoff values of OSDI, ST, and BCI grading. Performance comparisons of the 3 tests applied in isolation, parallel, and series were made.

Results: There were 84 patients with and 77 patients without coGVHD. Compared to those without coGVHD, patients with coGVHD had significantly higher median values of OSDI, corneal fluorescein staining, conjunctival injection, conjunctival fibrosis, and meibum quality, but lower ST scores (All P values <0.001). The cutoff values for OSDI, ST, and BCI grade in the diagnosis of coGVHD were 19.4 points, 7 mm, and grade 0, respectively. The sensitivity and specificity of the tests based on the cutoff values were, respectively, 89.3% and 89.6% for OSDI, 91.7% and 59.7% for ST, and 78.6% and 70.1% for BCI. The area under the curve (AUC) value of OSDI was significantly higher than that of ST (0.931 vs. 0.826; P=0.010) and BCI grade (0.931 vs. 0.781; P<0.001). The AUC values of the combinations were lower than that of OSDI alone.

Conclusions: The OSDI questionnaire can be used as a simple screening test for coGVHD as demonstrated by its high sensitivity and specificity in the transplant clinic and patients' self-monitoring. An OSDI greater than 19.4 could be considered an ophthalmology referral criterion.

RevDate: 2022-09-19
CmpDate: 2022-09-19

Zhang W, Yang H, Gao M, et al (2022)

Edaravone Dexborneol Alleviates Cerebral Ischemic Injury via MKP-1-Mediated Inhibition of MAPKs and Activation of Nrf2.

BioMed research international, 2022:4013707.

The edaravone and dexborneol concentrated solution for injection (edaravone-dexborneol) is a medication used clinically to treat neurological impairment induced by ischemic stroke. This study was aimed at investigating the preventive effects and the underlying mechanisms of edaravone-dexborneol on cerebral ischemic injury. A rat four-vessel occlusion (4-VO) model was established, and the neuronal injury and consequent neurological impairment of rats was investigated. Brain tissue malondialdehyde (MDA), myeloperoxidase (MPO), and nitric oxide (NO) levels were determined. The levels of proteins in mitogen-activated protein kinases (MAPKs), nuclear factor erythroid 2-related factor 2 (Nrf2), and nuclear factor-κB (NF-κB) signaling pathways were determined by western immunoblotting. The function of mitogen-activated protein kinase phosphatase 1 (MKP-1) was investigated using both western blot and immunofluorescence methods, and the effect of the MKP-1 inhibitor, (2E)-2-benzylidene-3-(cyclohexylamino)-3H-inden-1-one (BCI), was investigated. The results indicated that edaravone-dexborneol alleviated neurological deficiency symptoms and decreased apoptosis and neuron damage in the hippocampal CA1 area of the ischemic rats. Edaravone-dexborneol increased the MKP-1 level; decreased the phosphorylation of extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinase (JNK), and p38 mitogen-activated protein kinase (p38 MAPK); inhibited NF-κB p65 activation; and boosted Nrf2 activation, all of which were partially reversed by the MKP-1 inhibitor, BCI. The above results indicated that the upregulation of MKP-1 contributed to the protective effects of edaravone-dexborneol against ischemic brain injury. Our findings support the hypothesis that edaravone-dexborneol can alleviate cerebral ischemic injury via the upregulation of MKP-1, which inhibits MAPKs and activates Nrf2.

RevDate: 2022-09-19

El-Qawaqzeh K, Anand T, Richards J, et al (2022)

Predictors of Mortality in Blunt Cardiac Injury: A Nationwide Analysis.

The Journal of surgical research, 281:22-32 pii:S0022-4804(22)00514-5 [Epub ahead of print].

INTRODUCTION: Blunt thoracic injury (BTI) is one of the most common causes of trauma admission in the United States and is uncommonly associated with cardiac injuries. Blunt cardiac injury (BCI) after blunt thoracic trauma is infrequent but carries a substantial risk of morbidity and sudden mortality. Our study aims to identify predictors of concomitant cardiac contusion among BTI patients and the predictors of mortality among patients presenting with BCI on a national level.

MATERIALS AND METHODS: We performed a 1-y (2017) analysis of the American College of Surgeons Trauma Quality Improvement Program. We included all adults (aged ≥ 18 y) with the diagnosis of BTI. We excluded patients who were transferred, had a penetrating mechanism of injury, and who were dead on arrival. Our primary outcomes were the independent predictors of concomitant cardiac contusions among BTI patients and the predictors of mortality among BCI patients. Our secondary outcome measures were in-hospital complications, differences in injury patterns, and injury severity between the survivors and nonsurvivors of BCI.

RESULTS: A total of 125,696 patients with BTI were identified, of which 2368 patients had BCI. Mean age was 52 ± 20 y, 67% were male, and median injury severity score was 14 [9-21]. The most common type of cardiac injury was cardiac contusion (43%). Age ≥ 65 y, higher 4-h packed red blood cell requirements, motor vehicle collision mechanism of injury, and concomitant thoracic injuries (hemothorax, flail chest, lung contusion, sternal fracture, diaphragmatic injury, and thoracic aortic injuries) were independently associated with concomitant cardiac contusion among BTI patients (P value < 0.05). Age ≥ 65 y, thoracic aortic injury, diaphragmatic injury, hemothorax, and a history of congestive heart failure were independently associated with mortality in BCI patients (P value < 0.05).

CONCLUSIONS: Predictors of concomitant cardiac contusion among BTI patients and mortality among BCI patients were identified. Guidelines on the management of BCI should incorporate these predictors for timely identification of high-risk patients.

RevDate: 2022-09-21

Shoeibi A, Moridian P, Khodatars M, et al (2022)

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works.

Computers in biology and medicine, 149:106053.

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper.

RevDate: 2022-09-19
CmpDate: 2022-09-19

Keller L, Stelzle D, Schmidt V, et al (2022)

Community-level prevalence of epilepsy and of neurocysticercosis among people with epilepsy in the Balaka district of Malawi: A cross-sectional study.

PLoS neglected tropical diseases, 16(9):e0010675.

BACKGROUND: Epilepsy and neurocysticercosis (NCC) prevalence estimates in sub-Saharan Africa are still scarce but show important variation due to the population studied and different screening and diagnosis strategies used. The aims of this study were to estimate the prevalence of epileptic seizures and epilepsy in the sampled population, and the proportion of NCC among people with epilepsy (PWE) in a large cross-sectional study in a rural district of southern Malawi.

METHODS: We conducted a community-based door-to-door screening study for epileptic seizures in Balaka, Malawi between October and December 2012. Past epileptic seizures were reported through a 15-item questionnaire answered by at least one person per household generating five major criteria. People who screened positive were further examined by a neurologist to establish diagnosis. Patients diagnosed with epilepsy were examined and offered Taenia solium cyst antigen and antibody serological tests, and a CT scan for the diagnosis of NCC.

RESULTS: In total, screening information on 69,595 individuals was obtained for lifetime occurrence of epileptic seizures. 3,100 (4.5%) participants screened positive, of whom 1,913 (62%) could be followed-up and underwent further assessment. Lifetime prevalence was 3.0% (95% Bayesian credible interval [CI] 2.8 to 3.1%) and 1.2% (95%BCI 0.9 to 1.6%) for epileptic seizures and epilepsy, respectively. NCC prevalence among PWE was estimated to be 4.4% (95%BCI 0.8 to 8.5%). A diagnosis of epilepsy was ultimately reached for 455 participants.

CONCLUSION: The results of this large community-based study contribute to the evaluation and understanding of the burden of epilepsy in the population and of NCC among PWE in sub-Saharan Africa.

RevDate: 2022-09-17

Zhang S, Wang S, Liu R, et al (2022)

A bibliometric analysis of research trends of artificial intelligence in the treatment of autistic spectrum disorders.

Frontiers in psychiatry, 13:967074.

Objective: Autism Spectrum Disorder (ASD) is a serious neurodevelopmental disorder that has become the leading cause of disability in children. Artificial intelligence (AI) is a potential solution to this issue. This study objectively analyzes the global research situation of AI in the treatment of ASD from 1995 to 2022, aiming to explore the global research status and frontier trends in this field.

Methods: Web of Science (WoS) and PubMed databese were searched for Literature related to AI on ASD from 1995 to April 2022. CiteSpace, VOSviewer, Pajek and Scimago Graphica were used to analyze the collaboration between countries/institutions/authors, clusters and bursts of keywords, as well as analyses on references.

Results: A total of 448 literature were included, the total number of literature has shown an increasing trend. The most productive country and institution were the USA, and Vanderbilt University. The authors with the greatest contributions were Warren, Zachary, Sakar, Nilanjan and Swanson, Amy. the most prolific and cited journal is Journal of Autism and Developmental Disorders, the highest cited and co-cited articles were Dautenhahn (Socially intelligent robots: dimensions of human-robot interaction 2007) and Scassellati B (Robots for Use in Autism Research 2012). "Artificial Intelligence", "Brain Computer Interface" and "Humanoid Robot" were the hotspots and frontier trends of AI on ASD.

Conclusion: The application of AI in the treatment of ASD has attracted the attention of researchers all over the world. The education, social function and joint attention of children with ASD are the most concerned issues for global researchers. Robots shows gratifying advantages in these issues and have become the most commonly used technology. Wearable devices and brain-computer interface (BCI) were emerging AI technologies in recent years, which is the direction of further exploration. Restoring social function in individuals with ASD is the ultimate aim and driving force of research in the future.

RevDate: 2022-09-19

Savya SP, Li F, Lam S, et al (2022)

In vivo spatiotemporal dynamics of astrocyte reactivity following neural electrode implantation.

Biomaterials, 289:121784 pii:S0142-9612(22)00424-0 [Epub ahead of print].

Brain computer interfaces (BCIs), including penetrating microelectrode arrays, enable both recording and stimulation of neural cells. However, device implantation inevitably causes injury to brain tissue and induces a foreign body response, leading to reduced recording performance and stimulation efficacy. Astrocytes in the healthy brain play multiple roles including regulating energy metabolism, homeostatic balance, transmission of neural signals, and neurovascular coupling. Following an insult to the brain, they are activated and gather around the site of injury. These reactive astrocytes have been regarded as one of the main contributors to the formation of a glial scar which affects the performance of microelectrode arrays. This study investigates the dynamics of astrocytes within the first 2 weeks after implantation of an intracortical microelectrode into the mouse brain using two-photon microscopy. From our observation astrocytes are highly dynamic during this period, exhibiting patterns of process extension, soma migration, morphological activation, and device encapsulation that are spatiotemporally distinct from other glial cells, such as microglia or oligodendrocyte precursor cells. This detailed characterization of astrocyte reactivity will help to better understand the tissue response to intracortical devices and lead to the development of more effective intervention strategies to improve the functional performance of neural interfacing technology.

RevDate: 2022-09-14

Hou Y, Jia S, Lun X, et al (2022)

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.

IEEE transactions on neural networks and learning systems, PP: [Epub ahead of print].

Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning (DL) framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected (FC) softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and groupwise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet dataset), 96.24% and 80.89% (high gamma dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step toward better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain MI. A DL library for EEG task classification including the code for this study is open source at https://github.com/SuperBruceJia/ EEG-DL for scientific research.

RevDate: 2022-09-14

Athanasiou A, Mitsopoulos K, Praftsiotis A, et al (2022)

Neurorehabilitation Through Synergistic Man-Machine Interfaces Promoting Dormant Neuroplasticity in Spinal Cord Injury: Protocol for a Nonrandomized Controlled Trial.

JMIR research protocols, 11(9):e41152 pii:v11i9e41152.

BACKGROUND: Spinal cord injury (SCI) constitutes a major sociomedical problem, impacting approximately 0.32-0.64 million people each year worldwide; particularly, it impacts young individuals, causing long-term, often irreversible disability. While effective rehabilitation of patients with SCI remains a significant challenge, novel neural engineering technologies have emerged to target and promote dormant neuroplasticity in the central nervous system.

OBJECTIVE: This study aims to develop, pilot test, and optimize a platform based on multiple immersive man-machine interfaces offering rich feedback, including (1) visual motor imagery training under high-density electroencephalographic recording, (2) mountable robotic arms controlled with a wireless brain-computer interface (BCI), (3) a body-machine interface (BMI) consisting of wearable robotics jacket and gloves in combination with a serious game (SG) application, and (4) an augmented reality module. The platform will be used to validate a self-paced neurorehabilitation intervention and to study cortical activity in chronic complete and incomplete SCI at the cervical spine.

METHODS: A 3-phase pilot study (clinical trial) was designed to evaluate the NeuroSuitUp platform, including patients with chronic cervical SCI with complete and incomplete injury aged over 14 years and age-/sex-matched healthy participants. Outcome measures include BCI control and performance in the BMI-SG module, as well as improvement of functional independence, while also monitoring neuropsychological parameters such as kinesthetic imagery, motivation, self-esteem, depression and anxiety, mental effort, discomfort, and perception of robotics. Participant enrollment into the main clinical trial is estimated to begin in January 2023 and end by December 2023.

RESULTS: A preliminary analysis of collected data during pilot testing of BMI-SG by healthy participants showed that the platform was easy to use, caused no discomfort, and the robotics were perceived positively by the participants. Analysis of results from the main clinical trial will begin as recruitment progresses and findings from the complete analysis of results are expected in early 2024.

CONCLUSIONS: Chronic SCI is characterized by irreversible disability impacting functional independence. NeuroSuitUp could provide a valuable complementary platform for training in immersive rehabilitation methods to promote dormant neural plasticity.

TRIAL REGISTRATION: ClinicalTrials.gov NCT05465486; https://clinicaltrials.gov/ct2/show/NCT05465486.

PRR1-10.2196/41152.

RevDate: 2022-09-13

Taniguchi A, Yunoki T, Oiwake T, et al (2022)

Association between tear meniscus dimensions and higher-order aberrations in patients with surgically treated lacrimal passage obstruction.

International ophthalmology [Epub ahead of print].

PURPOSE: To analyze the relationship between tear meniscus dimensions and higher-order aberrations (HOAs) in patients with lacrimal passage obstruction using anterior segment optical coherence tomography (AS-OCT).

METHODS: This study was a retrospective observational study of 71 eyes of 49 patients with lacrimal passage obstruction. These patients received sheath-guided dacryoendoscopic probing and bicanalicular intubation (SG-BCI) at Toyama University Hospital between August 2020 and October 2021. Using AS-OCT, tear meniscus height (TMH), tear meniscus area (TMA), and total corneal HOAs values were measured before and after surgery.

RESULTS: Surgical success was achieved in 69 eyes (97.1%). At the final observation, 62 eyes showed lacrimal patency (89.8%). The preoperative TMH, TMA, and HOAs values were 1.55 ± 0.96 mm, 0.11 ± 0.14 mm2, and 0.37 ± 0.27 µm, respectively, and the final postoperative TMH, TMA, and HOAs values were 0.97 ± 0.74 mm (p < 0.0001), 0.06 ± 0.11 mm2 (p = 0.02), and 0.29 ± 0.16 µm (p = 0.001), respectively. The results showed a significant improvement. The changes in HOAs before and after surgery were positively correlated with the changes in TMH (r = 0.3476, p = 0.0241) and TMA (r = 0.3653, p = 0.0174).

CONCLUSION: SG-BCI for lacrimal passage obstruction resulted in a significant decrease in measured HOAs. The decrease in HOAs was correlated with decreases in tear meniscus dimensions.

RevDate: 2022-09-13

Hou S, Fan D, Q Wang (2022)

Regulating absence seizures by tri-phase delay stimulation applied to globus pallidus internal.

Applied mathematics and mechanics, 43(9):1399-1414.

In this paper, a reduced globus pallidus internal (GPI)-corticothalamic (GCT) model is developed, and a tri-phase delay stimulation (TPDS) with sequentially applying three pulses on the GPI representing the inputs from the striatal D 1 neurons, subthalamic nucleus (STN), and globus pallidus external (GPE), respectively, is proposed. The GPI is evidenced to control absence seizures characterized by 2 Hz-4 Hz spike and wave discharge (SWD). Hence, based on the basal ganglia-thalamocortical (BGCT) model, we firstly explore the triple effects of D l-GPI, GPE-GPI, and STN-GPI pathways on seizure patterns. Then, using the GCT model, we apply the TPDS on the GPI to potentially investigate the alternative and improved approach if these pathways to the GPI are blocked. The results show that the striatum D 1, GPE, and STN can indeed jointly and significantly affect seizure patterns. In particular, the TPDS can effectively reproduce the seizure pattern if the D 1-GPI, GPE-GPI, and STN-GPI pathways are cut off. In addition, the seizure abatement can be obtained by well tuning the TPDS stimulation parameters. This implies that the TPDS can play the surrogate role similar to the modulation of basal ganglia, which hopefully can be helpful for the development of the brain-computer interface in the clinical application of epilepsy.

RevDate: 2022-09-13

Pereira JA, Ray A, Rana M, et al (2022)

A real-time fMRI neurofeedback system for the clinical alleviation of depression with a subject-independent classification of brain states: A proof of principle study.

Frontiers in human neuroscience, 16:933559.

Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the "happiness emotional brain state" of the healthy participant. The training (4 training sessions over 2 weeks) was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent (BOLD) activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4-1 Median = 6.563%; Range = 4.10-27.34; Wilcoxon Test (0), 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test (0), 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1-15; Wilcoxon Test (0), 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range -1 to 2; Wilcoxon Test (0), 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range -1 to 2; Wilcoxon Test (0), 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies.

RevDate: 2022-09-12

Wang J, Zhang J, Yu H, et al (2022)

Editorial: Human machine interface-based neuromodulation solutions for neurorehabilitation.

Frontiers in neuroscience, 16:987455.

RevDate: 2022-09-13

Girdler B, Caldbeck W, J Bae (2022)

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review.

Frontiers in systems neuroscience, 16:836778.

Creating flexible and robust brain machine interfaces (BMIs) is currently a popular topic of research that has been explored for decades in medicine, engineering, commercial, and machine-learning communities. In particular, the use of techniques using reinforcement learning (RL) has demonstrated impressive results but is under-represented in the BMI community. To shine more light on this promising relationship, this article aims to provide an exhaustive review of RL's applications to BMIs. Our primary focus in this review is to provide a technical summary of various algorithms used in RL-based BMIs to decode neural intention, without emphasizing preprocessing techniques on the neural signals and reward modeling for RL. We first organize the literature based on the type of RL methods used for neural decoding, and then each algorithm's learning strategy is explained along with its application in BMIs. A comparative analysis highlighting the similarities and uniqueness among neural decoders is provided. Finally, we end this review with a discussion about the current stage of RLBMIs including their limitations and promising directions for future research.

RevDate: 2022-09-11

Shah S, Shaing C, Khatib J, et al (2022)

The Utility of Breast Cancer Index (BCI) Over Clinical Prognostic Tools for Predicting the Need for Extended Endocrine Therapy: A Safety Net Hospital Experience.

Clinical breast cancer pii:S1526-8209(22)00179-3 [Epub ahead of print].

INTRODUCTION: Extended endocrine therapy (EET) benefits select patients with early-stage hormone-receptor positive (HR+) breast cancer (BC) but also incurs side effects and cost. The Clinical Treatment Score at Five Years (CTS5) is a free tool that estimates risks of late relapse in estrogen-receptor positive (ER+) BC using clinicopathologic factors. The Breast Cancer Index (BCI) incorporates 2 genomic assays to estimate late relapse risk and likelihood of benefit from EET. This retrospective study assesses the utility of BCI in selecting EET candidates in a safety net hospital.

MATERIALS AND METHODS: We performed a retrospective chart review on 69 women with early-stage HR+, HER2- BC diagnosed at our institution from December 2009 to February 2016 on whom BCI was submitted. The CTS5 score was also calculated to assess clinical risk of late relapse.

RESULTS: Median age was 53 years. All patients included in our analysis had early ER+ HER2-negative BC. Roughly half of the patients (55%) were postmenopausal and 61% were of Hispanic origin. A total of 34 patients (49%) were deemed high-risk (>5%) for late relapse by CTS5, compared to 42 (61%) by BCI. BCI identified 31 (45%) patients that would benefit from EET and of those, 74%% were advised EET. 16 (47%) clinical high-risk patients were advised against EET due to low benefit predicted by BCI. In the clinical low risk group, 9 (26%) were recommended EET based on high benefit predicted by BCI.

CONCLUSION: BCI is reasonable to consider in early-stage HR+ BC and offered clinically relevant information over clinical pathologic information alone.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Kim MY, Park JY, Leigh JH, et al (2022)

Exploring user perspectives on a robotic arm with brain-machine interface: A qualitative focus group study.

Medicine, 101(36):e30508.

Brain-machine Interface (BMI) is a system that translates neuronal data into an output variable to control external devices such as a robotic arm. A robotic arm can be used as an assistive living device for individuals with tetraplegia. To reflect users' needs in the development process of the BMI robotic arm, our team followed an interactive approach to system development, human-centered design, and Human Activity Assistive Technology model. This study aims to explore the perspectives of people with tetraplegia about activities they want to participate in, their opinions, and the usability of the BMI robotic arm. Eight people with tetraplegia participated in a focus group interview in a semistructured interview format. A general inductive analysis method was used to analyze the qualitative data. The 3 overarching themes that emerged from this analysis were: 1) activities, 2) acceptance, and 3) usability. Activities that the users wanted to do using the robotic arm were categorized into the following 5 activity domains: activities of daily living (ADL), instrumental ADL, health management, education, and leisure. Participants provided their opinions on the needs and acceptance of the BMI technology. Participants answered usability and expected standards of the BMI robotic arm within 7 categories such as accuracy, setup, cost, etc. Participants with tetraplegia have a strong interest in the robotic arm and BMI technology to restore their mobility and independence. Creating BMI features appropriate to users' needs, such as safety and high accuracy, will be the key to acceptance. These findings from the perspectives of potential users should be taken into account when developing the BMI robotic arm.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Koorathota S, Khan Z, Lapborisuth P, et al (2022)

Multimodal Neurophysiological Transformer for Emotion Recognition.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:3563-3567.

Understanding neural function often requires multiple modalities of data, including electrophysiogical data, imaging techniques, and demographic surveys. In this paper, we introduce a novel neurophysiological model to tackle major challenges in modeling multimodal data. First, we avoid non-alignment issues between raw signals and extracted, frequency-domain features by addressing the issue of variable sampling rates. Second, we encode modalities through "cross-attention" with other modalities. Lastly, we utilize properties of our parent transformer architecture to model long-range dependencies between segments across modalities and assess intermediary weights to better understand how source signals affect prediction. We apply our Multimodal Neurophysiological Transformer (MNT) to predict valence and arousal in an existing open-source dataset. Experiments on non-aligned multimodal time-series show that our model performs similarly and, in some cases, outperforms existing methods in classification tasks. In addition, qualitative analysis suggests that MNT is able to model neural influences on autonomic activity in predicting arousal. Our architecture has the potential to be fine-tuned to a variety of downstream tasks, including for BCI systems.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Lee KW, Lee DH, Kim SJ, et al (2022)

Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:1977-1980.

Speech impairments due to cerebral lesions and degenerative disorders can be devastating. For humans with severe speech deficits, imagined speech in the brain-computer interface has been a promising hope for reconstructing the neural signals of speech production. However, studies in the EEG-based imagined speech domain still have some limitations due to high variability in spatial and temporal information and low signal-to-noise ratio. In this paper, we investigated the neural signals for two groups of native speakers with two tasks with different languages, English and Chinese. Our assumption was that English, a non-tonal and phonogram-based language, would have spectral differences in neural computation compared to Chinese, a tonal and ideogram-based language. The results showed the significant difference in the relative power spectral density between English and Chinese in specific frequency band groups. Also, the spatial evaluation of Chinese native speakers in the theta band was distinctive during the imagination task. Hence, this paper would suggest the key spectral and spatial information of word imagination with specialized language while decoding the neural signals of speech. Clinical Relevance- Imagined speech-related studies lead to the development of assistive communication technology especially for patients with speech disorders such as aphasia due to brain damage. This study suggests significant spectral features by analyzing cross-language differences of EEG-based imagined speech using two widely used languages.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Liao G, Wang S, Wei Z, et al (2022)

Online classifier of AMICA model to evaluate state anxiety while standing in virtual reality.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:381-384.

Changes in emotional state, such as anxiety, have a significant impact on behavior and mental health. However, the detection of anxiety in individuals requires trained specialists to administer specialized assessments, which often take a significant amount of time and resources. Thus, there is a significant need for objective and real-time anxiety detection methods to aid clinical practice. Recent advances in Adaptive Mixture Independent Component Analysis (AMICA) have demonstrated the ability to detect changes in emotional states using electroencephalographic (EEG) data. However, given that several hours may be need to identify the different models, alternative methods must be sought for future brain-computer-interface applications. This study examines the feasibility of a machine learning classifier using frequency domain features of EEG data to classify individual 500 ms samples of EEG data into different cortical states, as established by multi-model AMICA labels. Using a random forest classifier with 12 input features from EEG data to predict cortical states yielded a 75% accuracy in binary classification. Based on these findings, this work may provide a foundation for real-time anxiety state detection and classification.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Musellim S, Han DK, Jeong JH, et al (2022)

Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:711-714.

Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitates a calibration technique to obtain subject/session-specific data in order to tune the model each time the system is utilized. This issue is acknowledged as a key hindrance to BCI, and a new strategy based on domain generalization has recently evolved to address it. In light of this, we've concentrated on developing an EEG classification framework that can be applied directly to data from unknown domains (i.e. subjects), using only data acquired from separate subjects previously. For this purpose, in this paper, we proposed a framework that employs the open-set recognition technique as an auxiliary task to learn subject-specific style features from the source dataset while helping the shared feature extractor with mapping the features of the unseen target dataset as a new unseen domain. Our aim is to impose cross-instance style in-variance in the same domain and reduce the open space risk on the potential unseen subject in order to improve the generalization ability of the shared feature extractor. Our experiments showed that using the domain information as an auxiliary network increases the generalization performance. Clinical relevance-This study suggests a strategy to improve the performance of the subject-independent BCI systems. Our framework can help to reduce the need for further calibration and can be utilized for a range of mental state monitoring tasks (e.g. neurofeedback, identification of epileptic seizures, and sleep disorders).

RevDate: 2022-09-13
CmpDate: 2022-09-13

Uyanik C, Khan MA, Brunner IC, et al (2022)

Machine Learning for Motor Imagery Wrist Dorsiflexion Prediction in Brain-Computer Interface Assisted Stroke Rehabilitation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:715-719.

Stroke is a life-changing event that can affect the survivors' physical, cognitive and emotional state. Stroke care focuses on helping the survivors to regain their strength; recover as much functionality as possible and return to independent living through rehabilitation therapies. Automated training protocols have been reported to improve the efficiency of the rehabilitation process. These protocols also decrease the dependency of the process on a professional trainer. Brain-Computer Interface (BCI) based systems are examples of such systems where they make use of the motor imagery (MI) based electroencephalogram (EEG) signals to drive the rehabilitation protocols. In this paper, we have proposed the use of well-known machine learning (ML) algorithms, such as, the decision tree (DT), Naive Bayesian (NB), linear discriminant analysis (LDA), support vector machine (SVM), ensemble learning classifier (ELC), and artificial neural network (ANN) for MI wrist dorsiflexion prediction in a BCI assisted stroke rehabilitation study conducted on eleven stroke survivors with either the left or right paresis. The doubling sub-band selection filter bank common spatial pattern (DSBS-FBCSP) has been proposed as feature extractor and it is observed that the ANN based classifier produces the best results.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Ayoobi N, EB Sadeghian (2022)

A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:218-221.

A calibration procedure is required in motor imagery-based brain-computer interface (MI-BCI) to tune the system for new users. This procedure is time-consuming and prevents naive users from using the system immediately. Developing a subject-independent MI-BCI system to reduce the calibration phase is still challenging due to the subject-dependent characteristics of the MI signals. Many algorithms based on machine learning and deep learning have been developed to extract high-level features from the MI signals to improve the subject-to-subject generalization of a BCI system. However, these methods are based on supervised learning and extract features useful for discriminating various MI signals. Hence, these approaches cannot find the common underlying patterns in the MI signals and their generalization level is limited. This paper proposes a subject-independent MI-BCI based on a supervised autoencoder (SAE) to circumvent the calibration phase. The suggested framework is validated on dataset 2a from BCI competition IV. The simulation results show that our SISAE model outperforms the conventional and widely used BCI algorithms, common spatial and filter bank common spatial patterns, in terms of the mean Kappa value, in eight out of nine subjects.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Song Z, Zhang X, Y Wang (2022)

Cluster Kernel Reinforcement Learning-based Kalman Filter for Three-Lever Discrimination Task in Brain-Machine Interface.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:690-693.

Brain-Machine Interface (BMI) translates paralyzed people's neural activity into control commands of the prosthesis so that their lost motor functions could be restored. The neural activities represent brain states that change continuously over time which brings the challenge to the online decoder. Reinforcement Learning (RL) has the advantage to construct the dynamic neural-kinematic mapping during the interaction. However, existing RL decoders output discrete actions as a classification problem and cannot provide continuous estimation. Previous work has combined Kalman Filter (KF) with RL for BMI, which achieves a continuous motor state estimation. However, this method adopts a neural network structure, which might get stuck in local optimum and cannot provide an efficient online update for the neural-kinematic mapping. In this paper, we propose a Cluster Kernel Reinforcement Learning-based Kalman Filter (CKRL-based KF) to avoid the local optimum problem for online neural-kinematic updating. The neural patterns are projected into Reproducing Kernel Hilbert Space (RKHS), which builds a universal approximation to guarantee the global optimum. We compare our proposed algorithm with the existing method on rat data collected during a brain control three-lever discrimination task. Our preliminary results show that the proposed method has a higher trial accuracy with lower variance across data segments, which shows its potential to improve the performance for online BMI control. Clinical Relevance- This paper provides a more stable decoding method for adaptive and continuous neural decoding. It is promising for clinical applications in BMI.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Ferrero L, Quiles V, Ortiz M, et al (2022)

Assessing user experience with BMI-assisted exoskeleton in patients with spinal cord injury.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:4064-4067.

Spinal Cord Injury (SCI) refers to damage to the spinal cord that can affect different body functionalities. Recovery after SCI depends on multiple factors, being the rehabilitation therapy one of them. New approaches based on robot-assisted training offer the possibility to make training sessions longer and with a reproducible pattern of movements. The control of these robotic devices by means of Brain-Machine Interfaces (BMIs) based on Motor Imagery (MI) favors the patient cognitive engagement during the rehabilitation, promoting mechanisms of neuroplasticity. This research evaluates the acceptance and feedback received from patients with incomplete SCI about the usage of a MI-based BMI with a lower-limb exoskeleton. Clinical Relevance- Patients experienced satisfaction when using the exoskeleton and levels of mental and physical workload were withing reasonable limits. In addition results from the BMI were promising for the inclusion of this type of systems in rehabilitation programs.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Wu X, RHM Chan (2022)

Does Meta-Learning Improve EEG Motor Imagery Classification?.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:4048-4051.

Deep learning has been applied to enhance the performance of EEG-based brain-computer interface applications. However, the cross-subject variations in EEG signals cause domain shifts and negatively affect the model performance and generalization. Meta-learning algorithms have shown fast new domain adaption in various fields, which may help solve the domain shift problems in EEG. Reptile, with satisfactory performance and low computational costs, stands out from other existing meta-learning algorithms. We integrated Reptile with a deep neural network as Reptile-EEG for the EEG motor imagery tasks, and compared Reptile-EEG with other state-of-the-art models in three motor imagery BCI benchmark datasets. Results show that Reptile-EEGdoes not outperform simple training of deep neural networks in motor imagery BCI tasks.

RevDate: 2022-09-13
CmpDate: 2022-09-13

See BA, JT Francis (2022)

High Classification Accuracy of Touch Locations from S1 LFPs Using CNNs and Fastai.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:342-345.

The primary somatosensory cortex (S1) is a region often targeted for input via somatosensory neuroprosthesis as tactile and proprioception are represented in S1. How this information is represented is an ongoing area of research. Neural signals are high-dimensional, making accurate models for decoding a significant challenge. Artificial neural networks (ANNs) have proven efficient at classification tasks in multiple fields. Moreover, ANNs allow for transfer learning, which exploits feature extraction trained on a large and more general dataset than may be available for a particular problem. In this work, convolutional neural networks (CNN), used for image recognition, were fine-tuned with somatosensory cortical recordings during experiments with naturalistic touch stimuli. We created a highly accurate (correct) classifier for cutaneous stimulation locations as part of a somatosensory neuroprosthesis pipeline. Here we present the classifier results. Clinical Relevance- Our work provides a method for classifying cortical activity in brain-machine interface applications, specifically towards somatosensory neuroprosthetics.

RevDate: 2022-09-13
CmpDate: 2022-09-13

de Seta V, Colamarino E, Cincotti F, et al (2022)

Cortico-Muscular Coupling Allows to Discriminate Different Types of Hand Movements.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:2324-2327.

Cortico-muscular coupling (CMC) could be used as potential input of a novel hybrid Brain-Computer Interface (hBCI) for motor re-learning after stroke. Here, we aim of addressing the design of a hBCI able to classify different movement tasks taking into account the interplay between the cerebral and residual or recovered muscular activity involved in a given movement. Hence, we compared the performances of four classification methods based on CMC features to evaluate their ability in discriminating finger extension from grasping movements executed by 17 healthy subjects. We also explored how the variation in the dimensionality of the feature domain would influence the different classifier performances. Results showed that, regardless of the model, few CMC features (up to 10) allow for a successful classification of two different movements type. Moreover, support vector machine classifier with linear kernel showed the best trade-off between performances and system usability (few electrodes). Thus, these results suggest that a hBCI based on brain-muscular interplay holds the potential to enable more informed neural plasticity and functional motor recovery after stroke. Furthermore, this CMC-based BCI could also allow for a more "natural control" (l.e., that resembling physiological control) of prosthetic devices.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Alexandre H, B Stephane (2022)

Blinking characterization for each eye from EEG analysis using wavelets.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:4274-4277.

Eye blinks can be used to perform monitoring tasks such as drowsiness detection, attention measurement or other biological measurement mainly using video data. With the developement of brain computer interfaces (BCI) eye movements and blinks could be used to perform control tasks such as pointer activation or communications. This work aims to prove that it is possible to characterize eye blinks for each eye separately using only electroencephalography (EEG) signal acquired through non invasive portable device and dry electroencephalography.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Tan J, Shen X, Zhang X, et al (2022)

Estimating Reward Function from Medial Prefrontal Cortex Cortical Activity using Inverse Reinforcement Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:3346-3349.

Reinforcement learning (RL)-based brain-machine interfaces (BMIs) learn the mapping from neural signals to subjects' intention using a reward signal. External rewards (water or food) or internal rewards extracted from neural activity are leveraged to update the parameters of decoders in the existing RL-based BMI framework. However, for complex tasks, the design of external reward could be difficult, which may not fully reflect the subject's own evaluation internally. It is important to obtain an internal reward model from neural activity to access subject's internal evaluation when the subject is performing the task through trial and error. In this paper, we propose to use an inverse reinforcement learning (IRL) method to estimate the internal reward function interpreted from the brain to assist the update of the decoders. Specifically, the inverse Q-learning (IQL) algorithm is applied to extract internal reward information from real data collected from medial prefrontal cortex (mPFC) when a rat was learning a two-lever-press discrimination task. Such an internal reward information is validated by checking whether it can guide the training of the RL decoder to complete movement task. Compared with the RL decoder trained with the external reward, our approach achieves a similar decoding performance. This preliminary result validates the effectiveness of using IRL to obtain the internal reward model. It reveals the potential of estimating internal reward model to improve the design of autonomous learning BMIs.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Carretero A, A Araujo (2022)

Analysis of Simple Algorithms for Motion Detection in Wearable Devices.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:2410-2415.

Brain Computer Interfaces are used to obtain relevant information from the electroencephalogram (EEG) with a concrete objective. The evoked potentials related to movement are much demanded nowadays, in particular the ones associated to imagery movement. The objective of this work is to develop simple algorithms to imagery motion detection that can be included in a non-invasive wearable that everybody can use in a comfortable way for new services and applications. A wearable implies low resources, which is the most important requirement that the algorithms have. A public database with 105 subjects doing an upper-limb imagery movement is used. We have developed two algorithms (FBA and BLA) based on three characteristics of the signal (correlation, wavelet energy per segment and wavelet energy per electrode). They are tested for different number of electrodes and frequency bands. The best performance is found for 6 electrodes. The beta band is not the only band who achieves good performances. In fact, in this study the range between 25 Hz - 30 Hz has obtained the best performance using 6 electrodes. The conclusions show that these simple algorithms not fit well with the wearable requirements. However, it shows the need of adaptive algorithms to bypass the differences between subjects. Also, it affirms that more electrodes not lead to a better information, as well as, less electrodes not lead to a worse information. The same goes for frequency, where not only the beta band have the information required that fits our needs.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Mongiardini E, Colamarino E, Toppi J, et al (2022)

Low Frequency Brain Oscillations during the execution and imagination of simple hand movements for Brain-Computer Interface applications.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:226-229.

Low Frequency Brain Oscillations (LFOs) are brief periods of oscillatory activity in delta and lower theta band that appear at motor cortical areas before and around movement onset. It has been shown that LFO power decreases in post-stroke patients and re-emerges with motor functional recovery. To date, LFOs have not yet been explored during the motor execution (ME) and imagination (MI) of simple hand movements, often used in BCI-supported motor rehabilitation protocols post-stroke. This study aims at analyzing the LFOs during the ME and MI of the finger extension task in a sample of 10 healthy subjects and 2 stroke patients in subacute phase. The results showed that LFO power peaks occur in the preparatory phase of both ME and MI tasks on the sensorimotor channels in healthy subjects and their alterations in stroke patients. Clinical Relevance- Results suggest that LFOs could be explored as biomarker of the motor function recovery in rehabilitative protocols based on the movement imagination.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Micheli A, Consoli D, Merlini A, et al (2022)

Brain-Computer Interfaces: Investigating the Transition from Visually Evoked to Purely Imagined Steady-State Potentials.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:222-225.

Brain-Computer Interfaces (BCIs) based on Steady State Visually Evoked Potentials (SSVEPs) have proven effective and provide significant accuracy and information-transfer rates. This family of strategies, however, requires external devices that provide the frequency stimuli required by the technique. This limits the scenarios in which they can be applied, especially when compared to other BCI approaches. In this work, we have investigated the possibility of obtaining frequency responses in the EEG output based on the pure visual imagination of SSVEP-eliciting stimuli. Our results show that not only that EEG signals present frequency-specific peaks related to the frequency the user is focusing on, but also that promising classification accuracy can be achieved, paving the way for a robust and reliable visual imagery BCI modality. Clinical relevance-Brain computer interfaces play a fundamental role in enhancing the quality of life of patients with severe motor impairments. Strategies based on purely imagined stimuli, like the one presented here, are particularly impacting, especially in the most severe cases.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Thapa BR, Tangarife DR, J Bae (2022)

Kernel Temporal Differences for EEG-based Reinforcement Learning Brain Machine Interfaces.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:3327-3333.

Kernel temporal differences (KTD) (λ) algorithm integrated in Q-learning (Q-KTD) has shown its applicability and feasibility for reinforcement learning brain machine interfaces (RLBMIs). RLBMI with its unique learning strategy based on trial-error allows continuous learning and adaptation in BMIs. Q-KTD has shown good performance in both open and closed-loop experiments for finding a proper mapping from neural intention to control commands of an external device. However, previous studies have been limited to intracortical BMIs where monkey's firing rates from primary motor cortex were used as inputs to the neural decoder. This study provides the first attempt to investigate Q-KTD algorithm's applicability in EEG-based RLBMIs. Two different publicly available EEG data sets are considered, we refer to them as Data set A and Data set B. EEG motor imagery tasks are integrated in a single step center-out reaching task, and we observe the open-loop RLBMI experiments reach 100% average success rates after sufficient learning experience. Data set A converges after approximately 20 epochs for raw features and Data set B shows convergence after approximately 40 epochs for both raw and Fourier transform features. Although there still exist challenges to overcome in EEG-based RLBMI using Q-KTD, including increasing the learning speed, and optimization of a continuously growing number of kernel units, the results encourage further investigation of Q-KTD in closed-loop RLBMIs using EEG. Clinical Relevance- This study supports feasibility of noninvasive EEG-based RLBMI implementations and addresses benefits and challenges of RLBMI using EEG.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Vaghei Y, Park EJ, S Arzanpour (2022)

Decoding Brain Signals to Classify Gait Direction Anticipation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:309-312.

The use of brain-computer interface (BCI) technology has emerged as a promising rehabilitation approach for patients with motor function and motor-related disorders. BCIs provide an augmentative communication platform for controlling advanced assistive robots such as a lower-limb exoskeleton. Brain recordings collected by an electroencephalography (EEG) system have been employed in the BCI platform to command the exoskeleton. To date, the literature on this topic is limited to the prediction of gait intention and gait variations from EEG signals. This study, however, aims to predict the anticipated gait direction using a stream of EEG signals collected from the brain cortex. Three healthy participants (age range: 29-31, 2 female) were recruited. While wearing the EEG device, the participants were instructed to initiate gait movement toward the direction of the arrow triggers (pointing forward, backward, left, or right) being shown on a screen with a blank white background. Collected EEG data was then epoched around the trigger timepoints. These epochs were then converted to the time-frequency domain using event- related synchronization (ERS) and event-related desynchronization (ERD) methods. Finally, the classification pipeline was constructed using logistic regression (LR), support vector machine (SVM), and convolutional neural network (CNN). A ten-fold cross-validation scheme was used to evaluate the classification performance. The results revealed that the CNN classifier outperforms the other two classifiers with an accuracy of 0.75. Clinical Relevance - The outcome of this study has the potential to be ultimately used for interactive navigation of the lower-limb exoskeletons during robotic rehabilitation therapy and enhance neurodegeneration and neuroplasticity in a wide range of individuals with lower-limb motor function disabilities.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Rimbert S, F Lotte (2022)

ERD modulations during motor imageries relate to users' traits and BCI performances.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:203-207.

Improving user performances is one of the major issues for Motor Imagery (MI) - based BCI control. MI-BCIs exploit the modulation of sensorimotor rhythms (SMR) over the motor and sensorimotor cortices to discriminate several mental states and enable user interaction. Such modulations are known as Event-Related Desynchronization (ERD) and Synchronization (ERS), coming from the mu (7-13 Hz) and beta (15-30 Hz) frequency bands. This kind of BCI opens up promising fields, particularly to control assistive technologies, for sport training or even for post-stroke motor rehabilitation. However, MI - BCIs remain barely used outside laboratories, notably due to their lack of robustness and usability (15 to 30% of users seem unable to gain control of an MI-BCI). One way to increase user performance would be to better understand the relationships between user traits and ERD/ERS modulations underlying BCI performance. Therefore, in this article we analyzed how cerebral motor patterns underlying MI tasks (i.e., ERDs and ERSs) are modulated depending (i) on nature of the task (i.e., right-hand MI and left-hand MI), (ii) the session during which the task was performed (i.e., calibration or user training) and (iii) on the characteristics of the user (e.g., age, gender, manual activity, personality traits) on a large MI-BCI data base of N=75 participants. One of the originality of this study is to combine the investigation of human factors related to the user's traits and the neurophysiological ERD modulations during the MI task. Our study revealed for the first time an association between ERD and self-control from the 16PF5 questionnaire.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Sweet T, DE Thompson (2022)

Applying Big Transfer-based classifiers to the DEAP dataset.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:406-409.

Affective brain-computer interfaces are a fast-growing area of research. Accurate estimation of emotional states from physiological signals is of great interest to the fields of psychology and human-computer interaction. The DEAP dataset is one of the most popular datasets for emotional classification. In this study we generated heat maps from spectral data within the neurological signals found in the DEAP dataset. To account for the class imbalance within this dataset, we then discarded images belonging to the larger class. We used these images to fine-tune several Big Transfer neural networks for binary classification of arousal, valence, and dominance affective states. Our best classifier was able to achieve greater than 98% accuracy and 990% balanced accuracy in all three classification tasks. We also investigated the effects of this balancing method on our classifiers.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Ajra Z, Xu B, Dray G, et al (2022)

Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:52-55.

In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefit from BCIs as a means of communication and for the restoration of motor functions. Electroencephalography (EEG) is one of most used for evaluating the neuronal activity. In many computer vision applications, deep neural networks (DNN) show significant advantages. Towards to ultimate usage of DNN, we present here a shallow neural network that uses mainly two convolutional neural network (CNN) layers, with relatively few parameters and fast to learn spectral-temporal features from EEG. We compared this models to three other neural network models with different depths applied to a mental arithmetic task using eye-closed state adapted for patients suffering from motor disorders and a decline in visual functions. Experimental results showed that the shallow CNN model outperformed all the other models and achieved the highest classification accuracy of 90.68%. It's also more robust to deal with cross-subject classification issues: only 3% standard deviation of accuracy instead of 15.6% from conventional method.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Pan C, Liu H, Zheng D, et al (2022)

Neural Entrainment to Rhythms of Imagined Syllables.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:4040-4043.

Imagined speech based brain-computer interface (BCI) is of great interest due to its efficiency and user-friendliness for patients with speech impairment. The aim of this work was to study whether different rhythms of imagined syllables could elicit corresponding frequency components on EEG amplitude spectra. Seventeen participants were recruited to take part in the experiments, and performed a control task and four imagery tasks with the presence of periodic pure tones while their EEG signals were recorded. The four imagery tasks included imagining the syllable' /a/' every time, every two times, and every three times the periodic pure tones occurred, and imagined twice every three times the periodic pure tones occurred. The experimental results analyzed by Fourier transform indicated that neural entrainment to rhythmic speech imagery can be notably reflected on the EEG amplitude spectra. Clinical Relevance- This work manifested that different rhythms of imagined syllables could be identified from EEG amplitude spectra, which may be beneficial to the development of imagined speech based BCIs.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Ayoobi N, EB Sadeghian (2022)

Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:4817-4820.

Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG) signals can enhance the performance of the brain-computer interface (BCI) system and reduce the computational burden required for processing lengthy MI-EEG signals. In this paper, we propose an unsupervised method based on the self-attention mechanism to detect the salient intervals of MI-EEG signals automatically. Our suggested method can be used as a preprocessing step within any BCI algorithm to enhance its performance. The effectiveness of the suggested method is evaluated on the most widely used BCI algorithm, the common spatial pattern (CSP) algorithm, using dataset 2a from BCI competition IV. The results indicate that the proposed method can effectively prune MI-EEG signals and significantly enhance the performance of the CSP algorithm in terms of classification accuracy.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Floreani ED, Kelly D, Rowley D, et al (2022)

Iterative Development of a Software to Facilitate Independent Home Use of BCI Technologies for Children with Quadriplegic Cerebral Palsy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:3361-3364.

Brain-computer interfaces (BCIs) are emerging as a new solution for children with severe disabilities to interact with the world. However, BCI technologies have yet to reach end-users in their daily lives due to significant translational gaps. To address these gaps, we applied user-centered design principles to establish a home BCI program for children with quadriplegic cerebral palsy. This work describes the technical development of the software we designed to facilitate BCI use at home. Children and their families were involved at each design stage to evaluate and provide feedback. Since deployment, seven families have successfully used the system independently at home and continue to use BCI at home to further enable participation and independence for their children. Clinical relevance- The design and successful implementation of user-centered software for home use will both inform on the feasibility of BCI as a long-term access solution for children with neurological disabilities as well as decrease barriers of accessibility and availability of BCI technologies for end-users.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Earley EJ, Mastinu E, M Ortiz-Catalan (2022)

Cross-Channel Impedance Measurement for Monitoring Implanted Electrodes.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:4880-4883.

Implanted electrodes, such as those used for cochlear implants, brain-computer interfaces, and prosthetic limbs, rely on particular electrical conditions for optimal operation. Measurements of electrical impedance can be a diagnostic tool to monitor implanted electrodes for changing conditions arising from glial scarring, encapsulation, and shorted or broken wires. Such measurements provide information about the electrical impedance between a single electrode and its electrical reference, but offer no insights into the overall network of impedances between electrodes. Other solutions generally rely on geometrical assumptions of the arrangement of the electrodes and may not generalize to other electrode networks. Here, we propose a linear algebra-based approach, Cross-Channel Impedance Measurement (CCIM), for measuring a network of impedances between electrodes which all share a common electrical reference. This is accomplished by measuring the voltage response from all electrodes to a known current applied between each electrode and the shared reference, and is agnostic to the number and arrangement of electrodes. The approach is validated using a simulated 8-electrode network, demonstrating direct impedance measurements between electrodes and the reference with 96.6% ±0.2% accuracy, and cross-channel impedance measurements with 93.3% ±0.6% accuracy in a typical system. Subsequent analyses on randomized systems demonstrate the sensitivity of the model to impedance range and measurement noise. Clinical Relevance- CCIM provides a system-agnostic diagnostic test for implanted electrode networks, which may aid in the longitudinal tracking of electrode performance and early identification of electronics failures.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Li C, Sheng Y, Wang H, et al (2022)

EEG Emotion Recognition Based on Self-attention Dynamic Graph Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:292-296.

In recent years, due to the fundamental role played by the central nervous system in emotion expression, electroencephalogram (EEG) signals have emerged as the most robust signals for use in emotion recognition and inference. Current emotion recognition methods mainly employ deep learning technology to learn the spatial or temporal representation of each channel, then obtain complementary information from different EEG channels by adopting a multi-modal fusion strategy. However, emotional expression is usually accompanied by the dynamic spatio-temporal evolution of functional connections in the brain. Therefore, the effective learning of more robust long-term dynamic representations for the brain's functional connection networks is a key to improving the EEG-based emotion recognition system. To address these issues, we propose a brain network representation learning method that employs self-attention dynamic graph neural networks to obtain the spatial structure information and temporal evolution characteristics of brain networks. Experimental results on the AMIGOS dataset show that the proposed method is superior to the state-of-the-art methods.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Teversham J, Wong SS, Hsieh B, et al (2022)

Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:208-213.

This study details the development of a novel, approx. £20 electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer a financially and operationally accessible device that can be deployed on a mass scale to facilitate education and public engagement in the domain of EEG sensing and neurotechnologies. Real-time decoding of steady-state visual evoked potentials (SSVEPs) is achieved using variations of the widely-used canonical correlation analysis (CCA) algorithm: multi-set CCA and generalised CCA. All BCI functionality is executed on board an inexpensive ESP32 microcontroller. SSVEP decoding accuracy of 95.56 ± 3.74% with an ITR of 102 bits/min was achieved with modest calibration.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Brandt TM, Sweet T, DE Thompson (2022)

BCI Accuracy Using Classifier-Based Latency Estimation and the Optimal Interstimulus Interval.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:4097-4100.

PURPOSE: Detection of event-related potentials (ERPs) in brain-computer interfaces (BCIs) allow for communication by individuals with neuromuscular disorders. Enhancing BCI accuracy may be possible through the exploration of the optimal interstimulus interval (ISI). Our objective is to investigate the relationship between BCI accuracy and the optimal ISI value for an individual.

APPROACH: Using the previously developed classifier-based latency estimation (CBLE) [1], we investigated the relationship between the interstimulus interval (ISI) and P3 Speller BCI accuracy. Participants underwent two consecutive sessions in one day. The first session had a default ISI value of 120ms. An optimal ISI value calculated from the first session was used in the second.

RESULTS: Ten subjects participated in the study. Of the ten, half received an optimal ISI value of 120ms and half 160ms. Accuracy differences after implementing the adjusted ISI ranged from -26.1 percent to 4.35 percent. Suggestions for additional experimental design adjustments are highlighted under the discussion portion of this manuscript.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Soroush PZ, Herff C, Ries S, et al (2022)

Contributions of Stereotactic EEG Electrodes in Grey and White Matter to Speech Activity Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:4789-4792.

Recent studies have shown it is possible to decode and synthesize speech directly using brain activity recorded from implanted electrodes. While this activity has been extensively examined using electrocorticographic (ECoG) recordings from cortical surface grey matter, stereotactic electroen-cephalography (sEEG) provides comparatively broader coverage and access to deeper brain structures including both grey and white matter. The present study examines the relative and joint contributions of grey and white matter electrodes for speech activity detection in a brain-computer interface.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Bethge D, Hallgarten P, Ozdenizci O, et al (2022)

Exploiting Multiple EEG Data Domains with Adversarial Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:3154-3158.

Electroencephalography (EEG) is shown to be a valuable data source for evaluating subjects' mental states. However, the interpretation of multi-modal EEG signals is challenging, as they suffer from poor signal-to-noise-ratio, are highly subject-dependent, and are bound to the equipment and experimental setup used, (i.e. domain). This leads to machine learning models often suffer from poor generalization ability, where they perform significantly worse on real-world data than on the exploited training data. Recent research heavily focuses on cross-subject and cross-session transfer learning frameworks to reduce domain calibration efforts for EEG signals. We argue that multi-source learning via learning domain-invariant representations from multiple data-sources is a viable alternative, as the available data from different EEG data-source domains (e.g., subjects, sessions, experimental setups) grow massively. We propose an adversarial inference approach to learn data-source invariant representations in this context, enabling multi-source learning for EEG-based brain- computer interfaces. We unify EEG recordings from different source domains (i.e., emotion recognition datasets SEED, SEED-IV, DEAP, DREAMER), and demonstrate the feasibility of our invariant representation learning approach in suppressing data- source-relevant information leakage by 35% while still achieving stable EEG-based emotion classification performance.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Yadav T, Tellez OM, JT Francis (2022)

Reward-dependent Graded Suppression of Sensorimotor Beta-band Local Field Potentials During an Arm Reaching Task in NHP.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:3123-3126.

A better understanding of reward signaling in the sensorimotor cortices can aid in developing Reinforcement Learning-based Brain-Computer Interfaces (RLBCI) for restoration of movement functions with fewer implants. Brain-computer interfaces (BCIs) using local field potentials (LFPs) have recently achieved performance comparable to spike-BCIs [1]. With superior stability over time, LFPs may be the preferred signal for BCIs. We show that sensorimotor LFPs can provide reward level information (R1 - R3) like spikes[2]. We used a cued reward-level reaching task in which reward information was temporally dissociated from movement information. This allowed the study of reward- and movement-related modulations in LFPs. We recorded simultaneously from contralateral primary -somatosensory (S1), -motor (M1), and the dorsal premotor (PMd) cortices in a female Macaca Mulatta. We found that all three cortices' average beta band (14-30 Hz) amplitude showed robust modulation with reward levels during the cue presentation period. Such modulation was consistently observed after controlling for cue color, differences in behavioral variables, and electromyogram (EMG) activity. Statistical amplitude analysis showed that reward level could be extracted from the simple LFP feature of beta band amplitude, even before a reaching target appeared, and no specific reach plan could be developed. Clinical Relevance - The availability of reward-related signals in the sensorimotor cortical (S1, M1,and PMd) LFPs' prior to movement planning opens new avenues to build RLBCIs with fewer implants recording fewer sites among different cortices Reward and motivational representations derived from LFPs compared to spikes allow the development of long-term clinical applications given LFP's stability and ease of recording over long periods.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Jeong JH, Kim KT, Kim DJ, et al (2022)

Subject-Transfer Decoding using the Convolutional Neural Network for Motor Imagery-based Brain-Computer Interface.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:48-51.

Various pattern-recognition or machine learning-based methods have recently been developed to improve the accuracy of the motor imagery (MI)-based brain-computer interface (BCI). However, more research is needed to reduce the training time to apply it to the real-world environment. In this study, we propose a subject-transfer decoding method based on a convolutional neural network (CNN) which is robust even with a small number of training trials. The proposed CNN was pre-trained with other subjects' MI data and then fine-tuned to the target subject's training MI data. We evaluated the proposed method using the BCI competition IV data2a, which had the 4-class MIs. Consequently, on the same test dataset, with changing the number of training trials, the proposed method showed better accuracy than the self-training method, which used only the target subject's data for training, as averaged 86.54±7.78% (288 trials), 85.76 ±8.00% (240 trials), 84.65±8.11% (192 trials), and 83.29 ±8.25% (144 trials), respectively, which was 4.94% (288 trials), 6.10% (240 trials), 9.03% (192 trials), and 12.31% (144 trials)-point higher than the self-training method. Consequently, the proposed method was shown to be effective in maintaining classification accuracy even with the reduced training trials.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Mu J, Liu PC, Grayden DB, et al (2022)

Does Real-Time Feedback Improve User Performance in SSVEP-based Brain-Computer Interfaces?.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:694-697.

Offline and online experiments are both widely used in SSVEP-based BCI research and development for different purposes. One of the major differences between offline and online experiments is the existence of real-time feedback to the user while they are using the interface. However, the role of feedback in SSVEP-based BCIs has not yet been well studied. This work focuses on understanding the effect of feedback in SSVEP-based BCIs and if there exists any relationship between offline and online BCI performance. An experiment was designed to compare directly the accuracies of the BCI with and without feedback for participants. Results showed that feedback can improve performance in a complex task, but no clear improvement was observed in a simple task.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Hong J, Shamsi F, L Najafizadeh (2022)

A Deep Learning Framework Based on Dynamic Channel Selection for Early Classification of Left and Right Hand Motor Imagery Tasks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:3550-3553.

Ideal brain-computer interfaces (BCIs) need to be efficient and accurate, demanding for classifiers that can work across subjects while providing high classification accu-racy results from recordings with short duration. To address this problem, we present a new deep learning framework for discriminating motor imagery (MI) tasks from electroen-cephalography (EEG) signals. The framework consists of a 1D convolutional neural network-long short-term memory (CNN-LSTM), combined with a dynamic channel selection approach based on Davies-Bouldin index (DBI). Using data from BCI competition IV-IIa data, the proposed framework reports an average classification accuracy of 70.17% and 76.18% when using only 800 ms and 1500 ms of the EEG data after the task onset, respectively. The proposed framework dynamically balances individual differences, achieves comparable or better performance compared to existing work, while using short duration of EEG.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Zhang J, K Li (2022)

A Pruned Deep Learning Approach for Classification of Motor Imagery Electroencephalography Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:4072-4075.

The Deep Learning (DL) approach has been gaining much popularity in recent years in the development of electroencephalogram (EEG) based Motor Imagery (MI) Brain-Computer Interface (BCI) systems, aiming to improve the performance of existing stroke rehabilitation strategies. A complex deep neural network structure has lots of neurons with thousands of parameters to optimize, and a great deal of data is often required to train the network and the training process can take an extremely long time. High training costs and high model complexity not only have negative impacts on the performance of the BCI system but also on its applicability to meet the real-time requirement to support the rehabilitation exercises of patients. To tackle the challenge, a contribution-based neuron selection method is proposed in this paper. A Convolutional Neural Network (CNN) based motor imagery classification framework is implemented, and a neuron pruning approach is developed and applied. The temporal and spatial features of EEG signals are captured by the CNN layers, and then the fast recursive algorithm (FRA) is applied to prune redundant parameters in the fully connected layers which reduces the computation cost of the CNN model without affecting its performance. The experimental results show that the proposed method can achieve up to 50% model size reduction and 67.09% computation savings.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Berezutskaya J, Ambrogioni L, Ramsey NF, et al (2022)

Towards Naturalistic Speech Decoding from Intracranial Brain Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:3100-3104.

Speech decoding from brain activity can enable development of brain-computer interfaces (BCIs) to restore naturalistic communication in paralyzed patients. Previous work has focused on development of decoding models from isolated speech data with a clean background and multiple repetitions of the material. In this study, we describe a novel approach to speech decoding that relies on a generative adversarial neural network (GAN) to reconstruct speech from brain data recorded during a naturalistic speech listening task (watching a movie). We compared the GAN-based approach, where reconstruction was done from the compressed latent representation of sound decoded from the brain, with several baseline models that reconstructed sound spectrogram directly. We show that the novel approach provides more accurate reconstructions compared to the baselines. These results underscore the potential of GAN models for speech decoding in naturalistic noisy environments and further advancing of BCIs for naturalistic communication. Clinical Relevance - This study presents a novel speech decoding paradigm that combines advances in deep learning, speech synthesis and neural engineering, and has the potential to advance the field of BCI for severely paralyzed individuals.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Premchand B, Toe KK, Wang C, et al (2022)

Information sparseness in cortical microelectrode channels while decoding movement direction using an artificial neural network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:3534-3537.

Implanted microelectrode arrays can directly pick up electrode signals from the primary motor cortex (M1) during movement, and brain-machine interfaces (BMIs) can decode these signals to predict the directions of contemporaneous movements. However, it is not well known how much each individual input is responsible for the overall performance of a BMI decoder. In this paper, we seek to quantify how much each channel contributes to an artificial neural network (ANN)-based decoder, by measuring how much the removal of each individual channel degrades the accuracy of the output. If information on movement direction was equally distributed among channels, then the removal of one would have a minimal effect on decoder accuracy. On the other hand, if that information was distributed sparsely, then the removal of specific information-rich channels would significantly lower decoder accuracy. We found that for most channels, their removal did not significantly affect decoder performance. However, for a subset of channels (16 out of 61), removing them significantly reduced the decoder accuracy. This suggests that information is not uniformly distributed among the recording channels. We propose examining these channels further to optimize BMIs more effectively, as well as understand how M1 functions at the neuronal level.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Li M, Chen S, Liu X, et al (2022)

Modeling Neural Connectivity in a Point-Process Analogue of Kalman Filter.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:768-771.

A neural encoding model describes how single neuron tunes to external stimuli as well as its connectivity with other neurons. The connectivity illustrates the neuronal interaction within populations in response to the shared latent brain states. Understanding these interactions is crucial to computationally predict the neural activity, which elucidates the neural encoding mechanism A computational analysis on the neural connectivity also facilitates developing more point process decoding model to interpret movement state from neural spike observations for brain machine interfaces (BMI). Most of the previous point process models only consider single neural tuning property and assumes conditional independence among multiple neurons. The connectivity among neurons is not considered in such a Bayesian approach to derive the state. In this work, we propose a point-process analogue of Kalman Filter to model the neural connectivity in a closed-form Bayesian filter. Neural connectivity corrects the posterior of the state given the multi-dimension observation, and a Gaussian distribution is used to approximate the updated posterior distribution. We validate the proposed method on simulation data and compared with traditional point process filtering with conditional independent assumption. The result shows that our method models the neural connectivity information and the single neuronal tuning property at the same time and achieves a better performance of the state decoding. Clinical Relevance - This paper proposes a closed-form derivation of a point process filter based on Gaussian approximations. It can model both single neuronal tuning property and the neural connectivity, which is potential to understanding the neural connectivity computationally.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Kumar JNA, JT Francis (2022)

Improved Grip Force Prediction Using a Loss Function that Penalizes Reward Related Neural Information.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:2336-2339.

Neural activity in the sensorimotor cortices has been previously shown to correlate with kinematics, kinetics, and non-sensorimotor variables, such as reward. In this work, we compare the grip force offline Brain Machine Interface (BMI) prediction performance, of a simple artificial neural network (ANN), under two loss functions: the standard mean squared error (MSE) and a modified reward penalized mean squared error (RP_MSE), which penalizes for correlation between reward and grip force. Our results show that the ANN performs significantly better under the RP_MSE loss function in three brain regions: dorsal premotor cortex (PMd), primary motor cortex (M1) and the primary somatosensory cortex (S1) by approximately 6%.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Favero P, Berezutskaya J, Ramsey NF, et al (2022)

Mapping Acoustics to Articulatory Gestures in Dutch: Relating Speech Gestures, Acoustics and Neural Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:802-806.

Completely locked-in patients suffer from paralysis affecting every muscle in their body, reducing their communication means to brain-computer interfaces (BCIs). State-of-the-art BCIs have a slow spelling rate, which inevitably places a burden on patients' quality of life. Novel techniques address this problem by following a bio-mimetic approach, which consists of decoding sensory-motor cortex (SMC) activity that underlies the movements of the vocal tract's articulators. As recording articulatory data in combination with neural recordings is often unfeasible, the goal of this study was to develop an acoustic-to-articulatory inversion (AAI) model, i.e. an algorithm that generates articulatory data (speech gestures) from acoustics. A fully convolutional neural network was trained to solve the AAI mapping, and was tested on an unseen acoustic set, recorded simultaneously with neural data. Representational similarity analysis was then used to assess the relationship between predicted gestures and neural responses. The network's predictions and targets were significantly correlated. Moreover, SMC neural activity was correlated to the vocal tract gestural dynamics. The present AAI model has the potential to further our understanding of the relationship between neural, gestural and acoustic signals and lay the foundations for the development of a bio-mimetic speech BCI. Clinical Relevance- This study investigates the relationship between articulatory gestures during speech and the underlying neural activity. The topic is central for development of brain-computer interfaces for severely paralysed individuals.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Lin Y, Shu IW, Hsu SH, et al (2022)

Novel EEG-Based Neurofeedback System Targeting Frontal Gamma Activity of Schizophrenia Patients to Improve Working Memory.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:4031-4035.

Patients with schizophrenia (SCZ) exhibit working memory (WM) deficits that are associated with deficient dorsal-lateral prefrontal cortical activity, including decreased frontal gamma power. We thus hypothesized that training SCZ patients to increase frontal gamma activity would improve their WM performance. We administered electroencephalographic (EEG) neurofeedback (NFB) to 31 participants with SCZ for 12 weeks (24 sessions), which provides real-time visual and auditory feedback related to frontal gamma activity. The EEG-NFB training significantly improved EEG markers of optimal working memory, e.g., frontal P3 amplitude and gamma power. Based on these promising results, we developed a novel, EEGLAB/MATLAB-based brain-computer interface (BCI) that delivers F3-F4 gamma coherence NFB with a dynamic threshold to SCZ patients randomized in a double-blind, placebo-controlled clinical trial. The BCI significantly increased F3-F4 gamma coherence after 12 weeks (24 sessions) of training, according to data from the first 12 subjects (n=6 /group) who completed gamma- or placebo-NFB training.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Chen J, Wang D, Hu B, et al (2022)

MCFHNet: Multi-Channel Fusion Hybrid Network for Efficient EEG-fNIRS Multi-modal Motor Imagery Decoding.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:4821-4825.

Motor Imagery-based Brain Computer Interface (MI-BCI) is a typical active BCI with a main focus on motor intention identification. Hybrid motor imagery (MI) decoding methods that based on multi-modal fusion of Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), especially deep learning-based methods, become popular in recent MI-BCI studies. However, the fusion strategy and network design in deep learning-based methods are complex. To solve this problem, we proposed the multi-channel fusion method (MCF) to simplify current fusion methods, and we designed a multi-channel fusion hybrid network (MCFHNet) based on MCF. MCFHNet combines depthwise convolutional layers, channel attention mechanism, and Bidirectional Long Short Term Memory (Bi-LSTM) layers, which enables strong capability of feature extraction in spatial and temporal domain. The comparison between MCFHNet and representative deep learning-based methods was performed on an open EEG-fNIRS dataset. We found the proposed method can yield superior performance (mean accuracy of 99.641 % in 5-fold cross validation of an intra-subject experiment). This work provides a new option for multi-modal MI decoding, which can be applied in the rehabilitation field based on hybrid BCI systems.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Schmitz C, Sweet T, DE Thompson (2022)

The Effects of Word Priming on Emotion Classification from Neurological Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:410-413.

Affective states play an important role in human behavior and decision-making. In recent years, several affective brain-computer interface (aBCI) studies have focused on developing an emotion classifier based on elicited emotions within the user. However, it is difficult to achieve consistency in elicited emotions across populations, which can lead to dataset imbalances. The experimental design presented in this paper seeks to avoid consistency issues by asking the participant to classify the emotion portrayed in images of facial expressions, rather than their own emotions. Priming is also a common technique used in psychology studies that is known to influence emotional perception. To improve participant accuracy, we investigated matching and mis-matched word priming for the facial expression images. Electro-encephalogram (EEG) data were used to generate images fed into a classifier based on the Big Transfer model, BiT-M R101x1. The primed images resulted in higher classification accuracy overall. Further, by building different classifier models for both mis-matched primed images and matching primed images, we were able to achieve classification accuracies above 90%. We also provided the classifier with the true labels of the photographs instead of the labels generated by the participants and achieved similar results. The experimental paradigm of measuring brain activity during the emotional classification of another individual provides consistently high, balanced classification accuracies.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Yamamoto MS, Lotte F, Yger F, et al (2022)

Class-distinctiveness-based frequency band selection on the Riemannian manifold for oscillatory activity-based BCIs: preliminary results.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:3690-3693.

Considering user-specific settings is known to enhance Brain-Computer Interface (BCI) performances. In particular, the optimal frequency band for oscillatory activity classification is highly user-dependent and many frequency band selection methods have been developed in the past two decades. However, it is not well studied whether those conventional methods can be efficiently applied to the Riemannian BCIs, a recent family of BCI systems that utilize the non-Euclidean nature of the data unlike conventional BCI pipelines. In this paper, we proposed a novel frequency band selection method working on the Riemannian manifold. The frequency band is selected considering the class distinctiveness as quantified based on the inter-class distance and the intra-class variance on the manifold. An advantage of this method is that the frequency bandwidth can be adjusted for each individual without intensive optimization steps. In a comparative experiment using a public dataset of motor imagery-based BCI, our method showed a substantial improvement in average accuracy over both a fixed broad frequency band and a popular conventional frequency band selection method. In particular, our method substantially improved performances for subjects with initially low accuracies. This preliminary result suggests the importance of developing new user-specific setting algorithms considering the manifold properties, rather than directly applying methods developed prior to the rise of the Riemannian BCIs.

RevDate: 2022-09-13
CmpDate: 2022-09-13

Fang T, Song Z, Mu W, et al (2022)

Comparison of MI-EEG Decoding in Source to Sensor Domain.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022:3586-3589.

Brain-computer interface (BCI) system based on sensorimotor rhythm (SMR) is a more natural brain-computer interaction system. In this paper, we propose a new multi-task motor imagery EEG (MI-EEG) classification framework. Unlike traditional EEG decoding algorithms, we perform the decoding task in the source domain rather than the sensor domain. In the proposed algorithm, we first build a conduction model of the signal using the public ICBM152 head model and the boundary element method (BEM). The sensor domain EEG was then mapped to the selected cortex region using standardized low-resolution electromagnetic tomography (sLORETA) technology, which benefit to address volume conduction effects problem. Finally, the source domain features are extracted and classified by combining FBCSP and simple LDA. The results show that the classification-decoding algorithm performed in the source domain can well solve the classification task of MI-EEG. In addition, we found that the source imaging method can significantly increase the number of available EEG channels, which can be expanded at least double. The preliminary results of this study encourage the implementation of EEG decoding algorithms in the source domain. Clinical Relevance- This confirms that better results can be obtained by performing MI-EEG decoding in the source domain than in the sensor domain.

RevDate: 2022-09-13
CmpDate: 2022-09-12

Tao L, Cao T, Wang Q, et al (2022)

Distribution Adaptation and Classification Framework Based on Multiple Kernel Learning for Motor Imagery BCI Illiteracy.

Sensors (Basel, Switzerland), 22(17):.

A brain-computer interface (BCI) translates a user's thoughts such as motor imagery (MI) into the control of external devices. However, some people, who are defined as BCI illiteracy, cannot control BCI effectively. The main characteristics of BCI illiterate subjects are low classification rates and poor repeatability. To address the problem of MI-BCI illiteracy, we propose a distribution adaptation method based on multi-kernel learning to make the distribution of features between the source domain and target domain become even closer to each other, while the divisibility of categories is maximized. Inspired by the kernel trick, we adopted a multiple-kernel-based extreme learning machine to train the labeled source-domain data to find a new high-dimensional subspace that maximizes data divisibility, and then use multiple-kernel-based maximum mean discrepancy to conduct distribution adaptation to eliminate the difference in feature distribution between domains in the new subspace. In light of the high dimension of features of MI-BCI illiteracy, random forest, which can effectively handle high-dimensional features without additional cross-validation, was employed as a classifier. The proposed method was validated on an open dataset. The experimental results show that that the method we proposed suits MI-BCI illiteracy and can reduce the inter-domain differences, resulting in a reduction in the performance degradation of both cross-subjects and cross-sessions.

RevDate: 2022-09-13
CmpDate: 2022-09-12

Aydin M, Carpenelli AL, Lucia S, et al (2022)

The Dominance of Anticipatory Prefrontal Activity in Uncued Sensory-Motor Tasks.

Sensors (Basel, Switzerland), 22(17):.

Anticipatory event-related potentials (ERPs) precede upcoming events such as stimuli or actions. These ERPs are usually obtained in cued sensory-motor tasks employing a warning stimulus that precedes a probe stimulus as in the contingent negative variation (CNV) paradigms. The CNV wave has been widely studied, from clinical to brain-computer interface (BCI) applications, and has been shown to emerge in medial frontoparietal areas, localized in the cingulate and supplementary motor areas. Several dated studies also suggest the existence of a prefrontal CNV, although this component was not confirmed by later studies due to the contamination of ocular artifacts. Another lesser-known anticipatory ERP is the prefrontal negativity (pN) that precedes the uncued probe stimuli in discriminative response tasks and has been localized in the inferior frontal gyrus. This study aimed to characterize the pN by comparing it with the CNV in cued and uncued tasks and test if the pN could be associated with event preparation, temporal preparation, or both. To achieve these aims, high-density electroencephalographic recording and advanced ERP analysis controlling for ocular activity were obtained in 25 volunteers who performed 4 different visuomotor tasks. Our results showed that the pN amplitude was largest in the condition requiring both time and event preparation, medium in the condition requiring event preparation only, and smallest in the condition requiring temporal preparation only. We concluded that the prefrontal CNV could be associated with the pN, and this activity emerges in complex tasks requiring the anticipation of both the category and timing of the upcoming stimulus. The proposed method can be useful in BCI studies investigating the endogenous neural signatures triggered by different sensorimotor paradigms.

RevDate: 2022-09-20
CmpDate: 2022-09-12

Lee JH, Choi ME, An H, et al (2022)

BCI-215, a Dual-Specificity Phosphatase Inhibitor, Reduces UVB-Induced Pigmentation in Human Skin by Activating Mitogen-Activated Protein Kinase Pathways.

Molecules (Basel, Switzerland), 27(17):.

BACKGROUND: The dysregulation of melanin production causes skin-disfiguring ultraviolet (UV)-associated hyperpigmented spots. Previously, we found that the activation of c-Jun N-terminal kinase (JNK), a mitogen-activated protein kinase (MAPK), inhibited melanogenesis.

METHODS: We selected BCI-215 as it may modify MAPK expression via a known function of a dual-specificity phosphatase (DUSP) 1/6 inhibitor. B16F10 melanoma cells, Mel-ab cells, human melanocytes, and a coculture were used to assess the anti-melanogenic activity of BCI-215. The molecular mechanisms were deciphered by assaying the melanin content and cellular tyrosinase activity via immunoblotting and RT-PCR.

RESULTS: BCI-215 was found to suppress basal and cAMP-stimulated melanin production and cellular tyrosinase activity in vitro through the downregulation of microphthalmia-associated transcription factor (MITF) protein and its downstream enzymes. The reduction in MITF expression caused by BCI-215 was found to be due to all three types of MAPK activation, including extracellular signal-regulated kinase (ERK), JNK, and p38. The degree of activation was greater in ERK. A phosphorylation of the β-catenin pathway was also demonstrated. The melanin index, expression of MITF, and downstream enzymes were well-reduced in UVB-irradiated ex vivo human skin by BCI-215.

CONCLUSIONS: As BCI-215 potently inhibits UV-stimulated melanogenesis, small molecules of DUSP-related signaling modulators may provide therapeutic benefits against pigmentation disorders.

RevDate: 2022-09-19
CmpDate: 2022-09-08

Korik A, McCreadie K, McShane N, et al (2022)

Competing at the Cybathlon championship for people with disabilities: long-term motor imagery brain-computer interface training of a cybathlete who has tetraplegia.

Journal of neuroengineering and rehabilitation, 19(1):95.

BACKGROUND: The brain-computer interface (BCI) race at the Cybathlon championship, for people with disabilities, challenges teams (BCI researchers, developers and pilots with spinal cord injury) to control an avatar on a virtual racetrack without movement. Here we describe the training regime and results of the Ulster University BCI Team pilot who has tetraplegia and was trained to use an electroencephalography (EEG)-based BCI intermittently over 10 years, to compete in three Cybathlon events.

METHODS: A multi-class, multiple binary classifier framework was used to decode three kinesthetically imagined movements (motor imagery of left arm, right arm, and feet), and relaxed state. Three game paradigms were used for training i.e., NeuroSensi, Triad, and Cybathlon Race: BrainDriver. An evaluation of the pilot's performance is presented for two Cybathlon competition training periods-spanning 20 sessions over 5 weeks prior to the 2019 competition, and 25 sessions over 5 weeks in the run up to the 2020 competition.

RESULTS: Having participated in BCI training in 2009 and competed in Cybathlon 2016, the experienced pilot achieved high two-class accuracy on all class pairs when training began in 2019 (decoding accuracy > 90%, resulting in efficient NeuroSensi and Triad game control). The BrainDriver performance (i.e., Cybathlon race completion time) improved significantly during the training period, leading up to the competition day, ranging from 274-156 s (255 ± 24 s to 191 ± 14 s mean ± std), over 17 days (10 sessions) in 2019, and from 230-168 s (214 ± 14 s to 181 ± 4 s), over 18 days (13 sessions) in 2020. However, on both competition occasions, towards the race date, the performance deteriorated significantly.

CONCLUSIONS: The training regime and framework applied were highly effective in achieving competitive race completion times. The BCI framework did not cope with significant deviation in electroencephalography (EEG) observed in the sessions occurring shortly before and during the race day. Changes in cognitive state as a result of stress, arousal level, and fatigue, associated with the competition challenge and performance pressure, were likely contributing factors to the non-stationary effects that resulted in the BCI and pilot achieving suboptimal performance on race day. Trial registration not registered.

RevDate: 2022-09-07
CmpDate: 2022-09-07

Tazoe T, Y Nishimura (2022)

[Artificial Neural Connection Restores Voluntary Motor Function After Stroke and Spinal Cord Injury].

Brain and nerve = Shinkei kenkyu no shinpo, 74(9):1111-1116.

Artificial neural connection (ANC) establishes an artificial neural pathway to connect distant neuromuscular substrates using a computer interface. Our series of studies have demonstrated three events achieved by ANC. 1)ANC compensated the voluntary motor pathways damaged by stroke and spinal cord injury (SCI). 2)ANC provided a novel function for the neuron to input its information into the ANC. 3)ANC induced plastic changes in the existing neural connectivity between the neural substrates connected by ANC. In the preset literature, we expound the neurophysiological mechanisms underlying the three events and introduce theoretical neural backgrounds in restoring and recovering impaired voluntary locomotor function after stroke and SCI.

RevDate: 2022-09-06

Liang J, Song Y, Belkacem AN, et al (2022)

Prediction of balance function for stroke based on EEG and fNIRS features during ankle dorsiflexion.

Frontiers in neuroscience, 16:968928.

Balance rehabilitation is exceedingly crucial during stroke rehabilitation and is highly related to the stroke patients' secondary injuries (caused by falling). Stroke patients focus on walking ability rehabilitation during the early stage. Ankle dorsiflexion can activate the brain areas of stroke patients, similar to walking. The combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) was a new method, providing more beneficial information. We extracted the event-related desynchronization (ERD), oxygenated hemoglobin (HBO), and Phase Synchronization Index (PSI) features during ankle dorsiflexion from EEG and fNIRS. Moreover, we established a linear regression model to predict Berg Balance Scale (BBS) values and used an eightfold cross validation to test the model. The results showed that ERD, HBO, PSI, and age were critical biomarkers in predicting BBS. ERD and HBO during ankle dorsiflexion and age were promising biomarkers for stroke motor recovery.

RevDate: 2022-09-06

Pan C, Yu H, Fei X, et al (2022)

Temporal-spatial dynamic functional connectivity analysis in schizophrenia classification.

Frontiers in neuroscience, 16:965937.

With the development of resting-state functional magnetic resonance imaging (rs-fMRI) technology, the functional connectivity network (FCN) which reflects the statistical similarity of temporal activity between brain regions has shown promising results for the identification of neuropsychiatric disorders. Alteration in FCN is believed to have the potential to locate biomarkers for classifying or predicting schizophrenia (SZ) from healthy control. However, the traditional FCN analysis with stationary assumption, i.e., static functional connectivity network (SFCN) at the time only measures the simple functional connectivity among brain regions, ignoring the dynamic changes of functional connectivity and the high-order dynamic interactions. In this article, the dynamic functional connectivity network (DFCN) is constructed to delineate the characteristic of connectivity variation across time. A high-order functional connectivity network (HFCN) designed based on DFCN, could characterize more complex spatial interactions across multiple brain regions with the potential to reflect complex functional segregation and integration. Specifically, the temporal variability and the high-order network topology features, which characterize the brain FCNs from region and connectivity aspects, are extracted from DFCN and HFCN, respectively. Experiment results on SZ identification prove that our method is more effective (i.e., obtaining a significantly higher classification accuracy, 81.82%) than other competing methods. Post hoc inspection of the informative features in the individualized classification task further could serve as the potential biomarkers for identifying associated aberrant connectivity in SZ.

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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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