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Bibliography on: Brain-Computer Interface

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ESP: PubMed Auto Bibliography 19 Jan 2022 at 01:38 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: 2022-01-18

Lee DY, Jeong JH, Lee BH, et al (2022)

Motor Imagery Classification Using Inter-Task Transfer Learning via A Channel-Wise Variational Autoencoder-based 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].

Highly sophisticated control based on a brain-computer interface (BCI) requires decoding kinematic information from brain signals. The forearm is a region of the upper limb that is often used in everyday life, but intuitive movements within the same limb have rarely been investigated in previous BCI studies. In this study, we focused on various forearm movement decoding from electroencephalography (EEG) signals using a small number of samples. Ten healthy participants took part in an experiment and performed motor execution (ME) and motor imagery (MI) of the intuitive movement tasks (Dataset I). We propose a convolutional neural network using a channel-wise variational autoencoder (CVNet) based on inter-task transfer learning. We approached that training the reconstructed ME-EEG signals together will also achieve more sufficient classification performance with only a small amount of MI-EEG signals. The proposed CVNet was validated on our own Dataset I and a public dataset, BNCI Horizon 2020 (Dataset II). The classification accuracies of various movements are confirmed to be 0.83 (±0.04) and 0.69 (±0.04) for Dataset I and II, respectively. The results show that the proposed method exhibits performance increases of approximately 0.09~0.27 and 0.08~0.24 compared with the conventional models for Dataset I and II, respectively. The outcomes suggest that the training model for decoding imagined movements can be performed using data from ME and a small number of data samples from MI. Hence, it is presented the feasibility of BCI learning strategies that can sufficiently learn deep learning with a few amount of calibration dataset and time only, with stable performance.

RevDate: 2022-01-17

Ancau DM, Ancau M, M Ancau (2022)

Deep-learning online EEG decoding brain-computer interface using error-related potentials recorded with a consumer-grade headset.

Biomedical physics & engineering express [Epub ahead of print].

Objective:Brain-computer interfaces (BCIs) allow subjects with sensorimotor disability to interact with the environment. Non-invasive BCIs relying on EEG signals such as event-related potentials (ERPs) have been established as a reliable compromise between spatio-temporal resolution and patient impact, but limitations due to portability and versatility preclude their broad application. Here we describe a deep-learning augmented error-related potential (ErrP) discriminating BCI using a consumer-grade portable headset EEG, the Emotiv EPOC+.Approach:We recorded and discriminated ErrPs offline and online from 14 subjects during a visual feedback task.Main results:We achieved online discrimination accuracies of up to 81%, comparable to those obtained with professional 32/64-channel EEG devices via deep-learning using either a generative-adversarial network or an intrinsic-mode function augmentation of the training data and minimalistic computing resources.Significance:Our BCI model has the potential of expanding the spectrum of BCIs to more portable, artificial intelligence-enhanced, efficient interfaces accelerating the routine deployment of these devices outside the controlled environment of a scientific laboratory.

RevDate: 2022-01-17

Li B, Sun H, Shu H, et al (2022)

Applying Neuromorphic Computing Simulation in Band Gap Prediction and Chemical Reaction Classification.

ACS omega, 7(1):168-175.

The rapidly developing artificial intelligence (AI) requires revolutionary computing architectures to break the energy efficiency bottleneck caused by the traditional von Neumann computing architecture. In addition, the emerging brain-machine interface also requires computational circuitry that can conduct large parallel computational tasks with low energy cost and good biocompatibility. Neuromorphic computing, a novel computational architecture emulating human brains, has drawn significant interest for the aforementioned applications due to its low energy cost, capability to parallelly process large-scale data, and biocompatibility. Most efforts in the domain of neuromorphic computing focus on addressing traditional AI problems, such as handwritten digit recognition and file classification. Here, we demonstrate for the first time that current neuromorphic computing techniques can be used to solve key machine learning questions in cheminformatics. We predict the band gaps of small-molecule organic semiconductors and classify chemical reaction types with a simulated neuromorphic circuitry. Our work can potentially guide the design and fabrication of elementary devices and circuitry for neuromorphic computing specialized for chemical purposes.

RevDate: 2022-01-17

Ghosh R, Deb N, Sengupta K, et al (2022)

SAM 40: Dataset of 40 subject EEG recordings to monitor the induced-stress while performing Stroop color-word test, arithmetic task, and mirror image recognition task.

Data in brief, 40:107772 pii:S2352-3409(21)01046-5.

This paper presents a collection of electroencephalogram (EEG) data recorded from 40 subjects (female: 14, male: 26, mean age: 21.5 years). The dataset was recorded from the subjects while performing various tasks such as Stroop color-word test, solving arithmetic questions, identification of symmetric mirror images, and a state of relaxation. The experiment was primarily conducted to monitor the short-term stress elicited in an individual while performing the aforementioned cognitive tasks. The individual tasks were carried out for 25 s and were repeated to record three trials. The EEG was recorded using a 32-channel Emotiv Epoc Flex gel kit. The EEG data were then segmented into non-overlapping epochs of 25 s depending on the various tasks performed by the subjects. The EEG data were further processed to remove the baseline drifts by subtracting the average trend obtained using the Savitzky-Golay filter. Furthermore, the artifacts were also removed from the EEG data by applying wavelet thresholding. The dataset proposed in this paper can aid and support the research activities in the field of brain-computer interface and can also be used in the identification of patterns in the EEG data elicited due to stress.

RevDate: 2022-01-18
CmpDate: 2022-01-18

Pozzi NG, IU Isaias (2022)

Adaptive deep brain stimulation: Retuning Parkinson's disease.

Handbook of clinical neurology, 184:273-284.

A brain-machine interface represents a promising therapeutic avenue for the treatment of many neurologic conditions. Deep brain stimulation (DBS) is an invasive, neuro-modulatory tool that can improve different neurologic disorders by delivering electric stimulation to selected brain areas. DBS is particularly successful in advanced Parkinson's disease (PD), where it allows sustained improvement of motor symptoms. However, this approach is still poorly standardized, with variable clinical outcomes. To achieve an optimal therapeutic effect, novel adaptive DBS (aDBS) systems are being developed. These devices operate by adapting stimulation parameters in response to an input signal that can represent symptoms, motor activity, or other behavioral features. Emerging evidence suggests greater efficacy with fewer adverse effects during aDBS compared with conventional DBS. We address this topic by discussing the basics principles of aDBS, reviewing current evidence, and tackling the many challenges posed by aDBS for PD.

RevDate: 2022-01-18
CmpDate: 2022-01-18

Cui Y, Zhang F, Chen G, et al (2021)

A Stretchable and Transparent Electrode Based on PEGylated Silk Fibroin for In Vivo Dual-Modal Neural-Vascular Activity Probing.

Advanced materials (Deerfield Beach, Fla.), 33(34):e2100221.

Transparent electrodes that form seamless contact and enable optical interrogation at the electrode-brain interface are potentially of high significance for neuroscience studies. Silk hydrogels can offer an ideal platform for transparent neural interfaces owing to their superior biocompatibility. However, conventional silk hydrogels are too weak and have difficulties integrating with highly conductive and stretchable electronics. Here, a transparent and stretchable hydrogel electrode based on poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) and PEGylated silk protein is reported. PEGylated silk protein with poly(ethylene glycol) diglycidyl ether (PEGDE) improves the Young's modulus to 1.51-10.73 MPa and the stretchability to ≈400% from conventional silk hydrogels (<10 kPa). The PEGylated silk also helps form a robust interface with PEDOT:PSS thin film, making the hydrogel electrode synergistically incorporate superior stretchability (≈260%), stable electrical performance (≈4 months), and a low sheet resistance (≈160 ± 56 Ω sq-1). Finally, the electrode facilitates efficient electrical recording, and stimulation with unobstructed optical interrogation and rat-brain imaging are demonstrated. The highly transparent and stretchable hydrogel electrode offers a practical tool for neuroscience and paves the way for a harmonized tissue-electrode interface.

RevDate: 2022-01-17
CmpDate: 2022-01-17

Lee Y, Shin H, Lee D, et al (2021)

A Lubricated Nonimmunogenic Neural Probe for Acute Insertion Trauma Minimization and Long-Term Signal Recording.

Advanced science (Weinheim, Baden-Wurttemberg, Germany), 8(15):e2100231.

Brain-machine interfaces (BMIs) that link the brain to a machine are promising for the treatment of neurological disorders through the bi-directional translation of neural information over extended periods. However, the longevity of such implanted devices remains limited by the deterioration of their signal sensitivity over time due to acute inflammation from insertion trauma and chronic inflammation caused by the foreign body reaction. To address this challenge, a lubricated surface is fabricated to minimize friction during insertion and avoid immunogenicity during neural signal recording. Reduced friction force leads to 86% less impulse on the brain tissue, and thus immediately increases the number of measured signal electrodes by 102% upon insertion. Furthermore, the signal measurable period increases from 8 to 16 weeks due to the prevention of gliosis. By significantly reducing insertion damage and the foreign body reaction, the lubricated immune-stealthy probe surface (LIPS) can maximize the longevity of implantable BMIs.

RevDate: 2022-01-14

Santamaría-Vázquez E, Martínez-Cagigal V, Pérez-Velasco S, et al (2022)

Robust asynchronous control of ERP-Based brain-Computer interfaces using deep learning.

Computer methods and programs in biomedicine, 215:106623 pii:S0169-2607(22)00008-6 [Epub ahead of print].

BACKGROUND AND OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date are synchronous, requiring the intervention of a supervisor when the user wishes to turn his attention away from the BCI system. In order to bring these BCIs into real-life applications, a robust asynchronous control of the system is required through monitoring of user attention. Despite the great importance of this limitation, which prevents the deployment of these systems outside the laboratory, it is often overlooked in research articles. This study was aimed to propose a novel method to solve this problem, taking advantage of deep learning for the first time in this context to overcome the limitations of previous strategies based on hand-crafted features.

METHODS: The proposed method, based on EEG-Inception, a novel deep convolutional neural network, divides the problem in 2 stages to achieve the asynchronous control: (i) the model detects user's control state, and (ii) decodes the command only if the user is attending to the stimuli. Additionally, we used transfer learning to reduce the calibration time, even exploring a calibration-less approach.

RESULTS: Our method was evaluated with 22 healthy subjects, analyzing the impact of the calibration time and number of stimulation sequences on the system's performance. For the control state detection stage, we report average accuracies above 91% using only 1 sequence of stimulation and 30 calibration trials, reaching a maximum of 96.95% with 15 sequences. Moreover, our calibration-less approach also achieved suitable results, with a maximum accuracy of 89.36%, showing the benefits of transfer learning. As for the overall asynchronous system, which includes both stages, the maximum information transfer rate was 35.54 bpm, a suitable value for high-speed communication.

CONCLUSIONS: The proposed strategy achieved higher performance with less calibration trials and stimulation sequences than former approaches, representing a promising step forward that paves the way for more practical applications of ERP-based spellers.

RevDate: 2022-01-14

Simar C, Petit R, Bozga N, et al (2022)

Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans.

PloS one, 17(1):e0262417 pii:PONE-D-21-04037.

OBJECTIVE: Different visual stimuli are classically used for triggering visual evoked potentials comprising well-defined components linked to the content of the displayed image. These evoked components result from the average of ongoing EEG signals in which additive and oscillatory mechanisms contribute to the component morphology. The evoked related potentials often resulted from a mixed situation (power variation and phase-locking) making basic and clinical interpretations difficult. Besides, the grand average methodology produced artificial constructs that do not reflect individual peculiarities. This motivated new approaches based on single-trial analysis as recently used in the brain-computer interface field.

APPROACH: We hypothesize that EEG signals may include specific information about the visual features of the displayed image and that such distinctive traits can be identified by state-of-the-art classification algorithms based on Riemannian geometry. The same classification algorithms are also applied to the dipole sources estimated by sLORETA.

MAIN RESULTS AND SIGNIFICANCE: We show that our classification pipeline can effectively discriminate between the display of different visual items (Checkerboard versus 3D navigational image) in single EEG trials throughout multiple subjects. The present methodology reaches a single-trial classification accuracy of about 84% and 93% for inter-subject and intra-subject classification respectively using surface EEG. Interestingly, we note that the classification algorithms trained on sLORETA sources estimation fail to generalize among multiple subjects (63%), which may be due to either the average head model used by sLORETA or the subsequent spatial filtering failing to extract discriminative information, but reach an intra-subject classification accuracy of 82%.

RevDate: 2022-01-13

Stirner M, Gurevitch G, Lubianiker N, et al (2022)

An Investigation of Awareness and Metacognition in Neurofeedback with the Amygdala Electrical Fingerprint.

Consciousness and cognition, 98:103264 pii:S1053-8100(21)00190-2 [Epub ahead of print].

Awareness theory posits that individuals connected to a brain-computer interface can learn to estimate and discriminate their brain states. We used the amygdala Electrical Fingerprint (amyg-EFP) - a functional Magnetic Resonance Imaging-inspired Electroencephalogram surrogate of deep brain activation - to investigate whether participants could accurately estimate their own brain activation. Ten participants completed up to 20 neurofeedback runs and estimated their amygdala-EFP activation (depicted as a thermometer) and confidence in this rating during each trial. We analysed data using multilevel models, predicting the real thermometer position with participant rated position and adjusted for activation during the previous trial. Hypotheses on learning regulation and improvement of estimation were not confirmed. However, participant ratings were significantly associated with the amyg-EFP signal. Higher rating accuracy also predicted higher subjective confidence in the rating. This proof-of-concept study introduces an approach to study awareness with fMRI-informed neurofeedback and provides initial evidence for metacognition in neurofeedback.

RevDate: 2022-01-13

Yi GL, Zhu MZ, Cui HC, et al (2022)

A hippocampus dependent neural circuit loop underlying the generation of auditory mismatch negativity.

Neuropharmacology pii:S0028-3908(22)00006-5 [Epub ahead of print].

Extracting relevant information and transforming it into appropriate behavior, is a fundamental brain function, and requires the coordination between the sensory and cognitive systems, however, the underlying mechanisms of interplay between sensory and cognition systems remain largely unknown. Here, we developed a mouse model for mimicking human auditory mismatch negativity (MMN), a well-characterized translational biomarker for schizophrenia, and an index of early auditory information processing. We found that a subanesthetic dose of ketamine decreased the amplitude of MMN in adult mice. Using pharmacological and chemogenetic approaches, we identified an auditory cortex-entorhinal cortex-hippocampus neural circuit loop that is required for the generation of MMN. In addition, we found that inhibition of dCA1→MEC circuit impaired the auditory related fear discrimination. Moreover, we found that ketamine induced MMN deficiency by inhibition of long-range GABAergic projection from the CA1 region of the dorsal hippocampus to the medial entorhinal cortex. These results provided circuit insights for ketamine effects and early auditory information processing. As the entorhinal cortex is the interface between the neocortex and hippocampus, and the hippocampus is critical for the formation, consolidation, and retrieval of episodic memories and other cognition, our results provide a neural mechanism for the interplay between the sensory and cognition systems.

RevDate: 2022-01-13

Yan W, Xu G, Du Y, et al (2022)

SSVEP-EEG Feature Enhancement Method Using an Image Sharpening Filter.

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

Steady-state visual evoked potential (SSVEP) is widely used in brain computer interface (BCI), medical detection, and neuroscience, so there is significant interest in enhancing SSVEP features via signal processing for better performance. In this study, an image processing method was combined with brain signal analysis and a sharpening filter was used to extract image details and features for the enhancement of SSVEP features. The results demonstrated that sharpening filter could eliminate the SSVEP signal trend term and suppress its low-frequency component. Meanwhile, sharpening filter effectively enhanced the signal-to-noise ratios (SNRs) of the single-channel and multi-channel fused signals. Image sharpening filter also significantly improved the recognition accuracy of canonical correlation analysis (CCA), filter bank canonical correlation analysis (FBCCA), and task-related component analysis (TRCA). The tools developed here effectively enhanced the SSVEP signal features, suggesting that image processing methods can be considered for improved brain signal analysis.

RevDate: 2022-01-13

Liu C, Nguyen MA, Alvarez-Ciara A, et al (2020)

Surface Modifications of an Organic Polymer-Based Microwire Platform for Sustained Release of an Anti-Inflammatory Drug.

ACS applied bio materials, 3(7):4613-4625.

Brain machine interfaces (BMIs), introduced into the daily lives of individuals with injuries or disorders of the nervous system such as spinal cord injury, stroke, or amyotrophic lateral sclerosis, can improve the quality of life. BMIs rely on the capability of microelectrode arrays to monitor the activity of large populations of neurons. However, maintaining a stable, chronic electrode-tissue interface that can record neuronal activity with a high signal-to-noise ratio is a key challenge that has limited the translation of such technologies. An electrode implant injury leads to a chronic foreign body response that is well-characterized and shown to affect the electrode-tissue interface stability. Several strategies have been applied to modulate the immune response, including the application of immunomodulatory drugs applied both systemically and locally. While the use of passive drug release at the site of injury has been exploited to minimize neuroinflammation, this strategy has all but failed as a bolus of anti-inflammatory drugs is released at predetermined times that are often inconsistent with the ongoing innate inflammatory process. Common strategies do not focus on the proper anchorage of soft hydrogel scaffolds on electrode surfaces, which often results in delamination of the porous network from electrodes. In this study, we developed a microwire platform that features a robust yet soft biocompatible hydrogel coating, enabling long-lasting drug release via formation of drug aggregates and dismantlement of hydrophilic biodegradable three-dimensional polymer networks. Facile surface chemistry is developed to functionalize polyimide-coated electrodes with the covalently anchored porous hydrogel network bearing large numbers of highly biodegradable ester groups. Exponential long-lasting drug release is achieved using such hydrogels. We show that the initial state of dexamethasone (Dex) used to formulate the hydrogel precursor solution plays a cardinal role in engineering hydrophilic networks that enable a sustained and long-lasting release of the anti-inflammatory agent. Furthermore, utilization of a high loading ratio that exceeds the solubility of Dex leads to the encapsulation of Dex aggregates that regulate the release of this anti-inflammatory agent. To validate the anti-inflammatory effect of the hydrogel-functionalized Dex-loaded microwires, an in vivo preliminary study was performed in adult male rats (n = 10) for the acute time points of 48 h and 7 days post implant. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to assess the mRNA expression of certain inflammatory-related genes. In general, a decrease in fold-change expression was observed for all genes tested for Dex-loaded wires compared with controls (functionalized but no drug). The engineering of hybrid microwires enables a sustained release of the anti-inflammatory agent over extended periods of time, thus paving the way to fabricate neuroprosthetic devices capable of attenuating the foreign body response.

RevDate: 2022-01-13

Cai Z, Wang L, Guo M, et al (2022)

From Intricacy to Conciseness: A Progressive Transfer Strategy for EEG-Based Cross-Subject Emotion Recognition.

International journal of neural systems [Epub ahead of print].

Emotion plays a significant role in human daily activities, and it can be effectively recognized from EEG signals. However, individual variability limits the generalization of emotion classifiers across subjects. Domain adaptation (DA) is a reliable method to solve the issue. Due to the nonstationarity of EEG, the inferior-quality source domain data bring negative transfer in DA procedures. To solve this problem, an auto-augmentation joint distribution adaptation (AA-JDA) method and a burden-lightened and source-preferred JDA (BLSP-JDA) approach are proposed in this paper. The methods are based on a novel transfer idea, learning the specific knowledge of the target domain from the samples that are appropriate for transfer, which reduces the difficulty of transfer between two domains. On multiple emotion databases, our model shows state-of-the-art performance.

RevDate: 2022-01-13

Cai Y, Zhong Z, He C, et al (2019)

Homogeneously Synthesized Hydroxybutyl Chitosans in Alkali/Urea Aqueous Solutions as Potential Wound Dressings.

ACS applied bio materials, 2(10):4291-4302.

Wound healing is a clinical challenge, and nontoxic, nonadherent wound dressings that promote healing are urgently needed. Herein, hydroxybutyl chitosans (HBCSs) with the degree of substitution (DS) from 0.41 to 1.38 were synthesized in alkali/urea aqueous solutions, from which sponge-like dressings were prepared by freeze-drying. The pore size of the sponges was in the range of 14.8-18.4 μm, and the porosity was about 98-99%. The compressive strength of the sponges decreased with increasing DS of HBCS. Cytocompatibility studies with normal human dermal fibroblast (NHDF) cells demonstrated that HBCSs were nontoxic and could even promote the growth of fibroblasts. Further tests revealed that HBCS-3 (DS = 0.85) and HBCS-5 (DS = 1.38) exhibited better hemocompatibility and a low blood-clotting index (BCI). Therefore, these two samples were selected as model dressings for in vivo wound-healing assessment in rats. The experiments suggested that HBCS-3 significantly shortened the wound recovery period compared with HBCS-5, chitosan, and gauze by facilitating epithelialization, collagen deposition, and neovascularization and activating the immune system. The results highlighted the potential of HBCSs as efficient dressings for promoting wound healing.

RevDate: 2022-01-13

Schroeder KE, Perkins SM, Wang Q, et al (2022)

Cortical Control of Virtual Self-Motion Using Task-Specific Subspaces.

The Journal of neuroscience : the official journal of the Society for Neuroscience, 42(2):220-239.

Brain-machine interfaces (BMIs) for reaching have enjoyed continued performance improvements, yet there remains significant need for BMIs that control other movement classes. Recent scientific findings suggest that the intrinsic covariance structure of neural activity depends strongly on movement class, potentially necessitating different decode algorithms across classes. To address this possibility, we developed a self-motion BMI based on cortical activity as monkeys cycled a hand-held pedal to progress along a virtual track. Unlike during reaching, we found no high-variance dimensions that directly correlated with to-be-decoded variables. This was due to no neurons having consistent correlations between their responses and kinematic variables. Yet we could decode a single variable-self-motion-by nonlinearly leveraging structure that spanned multiple high-variance neural dimensions. Resulting online BMI-control success rates approached those during manual control. These findings make two broad points regarding how to build decode algorithms that harmonize with the empirical structure of neural activity in motor cortex. First, even when decoding from the same cortical region (e.g., arm-related motor cortex), different movement classes may need to employ very different strategies. Although correlations between neural activity and hand velocity are prominent during reaching tasks, they are not a fundamental property of motor cortex and cannot be counted on to be present in general. Second, although one generally desires a low-dimensional readout, it can be beneficial to leverage a multidimensional high-variance subspace. Fully embracing this approach requires highly nonlinear approaches tailored to the task at hand, but can produce near-native levels of performance.SIGNIFICANCE STATEMENT Many brain-machine interface decoders have been constructed for controlling movements normally performed with the arm. Yet it is unclear how these will function beyond the reach-like scenarios where they were developed. Existing decoders implicitly assume that neural covariance structure, and correlations with to-be-decoded kinematic variables, will be largely preserved across tasks. We find that the correlation between neural activity and hand kinematics, a feature typically exploited when decoding reach-like movements, is essentially absent during another task performed with the arm: cycling through a virtual environment. Nevertheless, the use of a different strategy, one focused on leveraging the highest-variance neural signals, supported high performance real-time brain-machine interface control.

RevDate: 2022-01-12

Riglietti G, Avatefipour A, P Trucco (2021)

The impact of business continuity management on the components of supply chain resilience: A quantitative analysis.

Journal of business continuity & emergency planning, 15(2):182-195.

This study investigates the mitigating influence of business continuity management (BCM) with respect to supply chain disruptions. Using a dataset from the 2017 BCI Supply Chain Resilience Report, the authors conduct partial least square-based structural equation modelling with reflective constructs for both exogenous and endogenous variables. The results demonstrate that BCM reduces vulnerability and mitigates the impact of supply chain disruptions on operational performance. The study highlights BCM's contribution to such important components of supply chain resilience as visibility, collaboration and agility. In addition to demonstrating the impact of BCM on supply chain resilience, the paper explains the role of top management in the BCM process, and provides a list of measures that organisations can take to protect themselves from external threats. This is the first study to use statistical analysis to provide empirical validation in this field, while employing a clear definition of BCM in line with international best practices.

RevDate: 2022-01-11

Zhang S, Chen X, Wang Y, et al (2022)

Visual field inhomogeneous in brain-computer interfaces based on rapid serial visual presentation.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Visual attention is not homogeneous across the visual field, while how to mine the effective EEG characteristics that are sensitive to the inhomogeneous of visual attention and further explore applications such as the performance of brain-computer interface (BCI) are still distressing explorative scientists.

APPROACH: Images were encoded into a rapid serial visual presentation (RSVP) paradigm, and were presented in three visuospatial patterns (central, left/right, upper/lower) at the stimulation frequencies of 10Hz, 15Hz and 20Hz. The comparisons among different visual fields were conducted in the dimensions of subjective behavioral and EEG characteristics. Furthermore, the effective features (e.g. SSVEP, N2pc and P300) that sensitive to visual-field asymmetry were also explored.

RESULTS: The visual fields had significant influences on the performance of RSVP target detection, in which the performance of central was better than that of peripheral visual field, the performance of horizontal meridian was better than that of vertical meridian, the performance of left visual field was better than that of right visual field, and the performance of upper visual field was better than that of lower visual field. Furthermore, stimuli of different visual fields had significant effects on the spatial distributions of EEG, in which N2pc and P300 showed left-right asymmetry in occipital and frontal regions, respectively. In addition, the evidences of SSVEP characteristics indicated that there was obvious overlap of visual fields on the horizontal meridian, but not on the vertical meridian.

SIGNIFICANCE: The conclusions of this study provide insights into the relationship between visual field inhomogeneous and EEG characteristics. In addition, this study has the potential to achieve precise positioning of the target's spatial orientation in RSVP-BCIs.

RevDate: 2022-01-11

He C, Ke M, Zhong Z, et al (2021)

Effect of the Degree of Acetylation of Chitin Nonwoven Fabrics for Promoting Wound Healing.

ACS applied bio materials, 4(2):1833-1842.

Chitin and chitosan have been extensively used as wound dressings because of their special functions to promote wound healing. However, there was little focus on the effects of the degree of acetylation (DA) on wound healing. In this work, the regenerated chitin nonwoven fabrics with DA values of 90, 71, 60, and 42% were prepared, and the morphology and physical performances of the fabrics were characterized. Moreover, the effects of DA of the chitin nonwoven fabrics on wound recovery were studied with a full-thickness skin defect model in rats. In vitro experiments indicated that the chitin nonwoven fabrics exhibited good biocompatibility and blood compatibility and a low blood-clotting index (BCI). In vivo experiments revealed that the chitin nonwoven fabrics could accelerate wound healing more effectively than gauze by promoting re-epithelialization and collagen deposition as well as by stimulating neovascularization. The results of the wound healing process showed that DA of the chitin nonwoven fabrics had a profound effect on promoting wound healing. Notably, the regenerated chitin nonwoven fabrics with 71% DA significantly improved the wound healing compared to the commercial wound dressing Algoplaque film. Therefore, the regenerated chitin nonwoven fabrics are promising candidates for wound healing.

RevDate: 2022-01-11

Proix T, Delgado Saa J, Christen A, et al (2022)

Imagined speech can be decoded from low- and cross-frequency intracranial EEG features.

Nature communications, 13(1):48.

Reconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence difficult to decode by learning algorithms. We obtained three electrocorticography datasets from 13 patients, with electrodes implanted for epilepsy evaluation, who performed overt and imagined speech production tasks. Based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, phonetic, and vocalic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings show that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding.

RevDate: 2022-01-11

Abuín-Porras V, Martinez-Perez C, Romero-Morales C, et al (2021)

Citation Network Study on the Use of New Technologies in Neurorehabilitation.

International journal of environmental research and public health, 19(1): pii:ijerph19010026.

New technologies in neurorehabilitation is a wide concept that intends to find solutions for individual and collective needs through technical systems. Analysis through citation networks is used to search scientific literature related to a specific topic. On the one hand, the main countries, institutions, and authors researching this topic have been identified, as well as their evolution over time. On the other hand, the links between the authors, the countries, and the topics under research have been analyzed. The publications analysis was performed through the Web of Science database using the search terms "new technolog*," "neurorehabilitation," "physical therapy*," and "occupational therapy*." The selected interval of publication was from 1992 to December 2020. The results were analyzed using CitNetExplorer software. After a Web of Science search, a total of 454 publications and 135 citation networks were found, 1992 being the first year of publication. An exponential increase was detected from the year 2009. The largest number was detected in 2020. The main areas are rehabilitation and neurosciences and neurology. The most cited article was from Perry et al. in 2007, with a citation index of 460. The analysis of the top 20 most cited articles shows that most approach the use of robotic devices and brain-computer interface systems. In conclusion, the main theme was found to be the use of robotic devices to address neuromuscular rehabilitation goals and brain-computer interfaces and their applications in neurorehabilitation.

RevDate: 2022-01-11

Palumbo A, Ielpo N, B Calabrese (2022)

An FPGA-Embedded Brain-Computer Interface System to Support Individual Autonomy in Locked-In Individuals.

Sensors (Basel, Switzerland), 22(1): pii:s22010318.

Brain-computer interfaces (BCI) can detect specific EEG patterns and translate them into control signals for external devices by providing people suffering from severe motor disabilities with an alternative/additional channel to communicate and interact with the outer world. Many EEG-based BCIs rely on the P300 event-related potentials, mainly because they require training times for the user relatively short and provide higher selection speed. This paper proposes a P300-based portable embedded BCI system realized through an embedded hardware platform based on FPGA (field-programmable gate array), ensuring flexibility, reliability, and high-performance features. The system acquires EEG data during user visual stimulation and processes them in a real-time way to correctly detect and recognize the EEG features. The BCI system is designed to allow to user to perform communication and domotic controls.

RevDate: 2022-01-11

McDermott EJ, Raggam P, Kirsch S, et al (2021)

Artifacts in EEG-Based BCI Therapies: Friend or Foe?.

Sensors (Basel, Switzerland), 22(1): pii:s22010096.

EEG-based brain-computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

RevDate: 2022-01-11

Montegut C, Correard F, Nouguerède E, et al (2021)

Prognostic Value of the B12/CRP Index in Older Systemically Treatable Cancer Patients.

Cancers, 14(1): pii:cancers14010169.

BACKGROUND: While comprehensive geriatric assessment (CGA) in older patients treated for cancer assesses several related domains, it does not include standardized biological tests. The present study aimed to: (1) assess the prognosis value of the B12/CRP index (BCI) in a population of systemically treatable older patients with cancer and (2) analyze the association between BCI value and pre-existing geriatric frailty.

METHOD: We conducted a retrospective observational study between January 2016 and June 2020 at Marseille University Hospital. All consecutive cancer patients aged 70 years and over before initiating systemic therapy were included.

RESULTS: Of the 863 patients included, 60.5% were men and 42.5% had metastatic stage cancer. Mean age was 81 years. The low-BCI group (≤10,000) had a significantly longer survival time than the mid-BCI (10,000 < BCI ≤ 40,000) and high-BCI (BCI > 40,000) groups (HR = 0.327, CI95% [0.26-0.42], p-value = 0.0001). Mid- and high-BCI (BCI > 40,000) values were associated with impaired functional status and malnutrition.

CONCLUSION: A BCI > 10,000 would appear to be a good biological prognostic factor for poor survival times and pre-existing geriatric impairment in older cancer patients before they initiate systemic treatment.

RevDate: 2022-01-10

Liu Y, Wang Z, Huang S, et al (2022)

EEG characteristic investigation of the sixth-finger motor imagery and optimal channel selection for classification.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Supernumerary Robotic Limbs (SRL) are body augmentation robotic devices by adding extra limbs or fingers to the human body different from the traditional wearable robotic devices such as prosthesis and exoskeleton. We proposed a novel MI (Motor imagery)-based BCI paradigm based on the sixth-finger which imagines controlling the extra finger movements. The goal of this work is to investigate the EEG characteristics and the application potential of MI-based BCI systems based on the new imagination paradigm (the sixth finger MI).

APPROACH: 14 subjects participated in the experiment involving the sixth finger MI tasks and rest state. Event-related spectral perturbation (ERSP) was adopted to analyse EEG spatial features and key-channel time-frequency features. Common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM). A genetic algorithm (GA) was used to select combinations of EEG channels that maximized classification accuracy and verified EEG patterns based on the sixth finger MI. And we conducted a longitudinal 4-week EEG control experiment based on the new paradigm.

MAIN RESULTS: ERD (event-related desynchronization) was found in the supplementary motor area (SMA) and primary motor area (M1) with a faint contralateral dominance. Unlike traditional MI based on the human hand, ERD was also found in frontal lobe. GA results showed that the distribution of the optimal 8-channel is similar to EEG topographical distributions, nearing parietal and frontal lobe. And the classification accuracy based on the optimal 8-channel (the highest accuracy of 80% and mean accuracy of 70%) was significantly better than that based on the random 8-channel (p<0.01).

SIGNIFICANCE: This work provided a new paradigm for MI-based MI system and verified its feasibility, widened the control bandwidth of the BCI system.

RevDate: 2022-01-10

Moon J, Chau T, S Orlandi (2022)

A comparison and classification of oscillatory characteristics in speech perception and covert speech.

Brain research pii:S0006-8993(22)00002-6 [Epub ahead of print].

Covert speech, the mental imagery of speaking, has been studied increasingly to understand and decode thoughts in the context of brain-computer interfaces. In studies of speech comprehension, neural oscillations are thought to play a key role in the temporal encoding of speech. However, little is known about the role of oscillations in covert speech. In this study, we investigated the oscillatory involvements in covert speech and speech perception. Data were collected from 10 participants with 64 channel EEG. Participants heard the words, 'blue' and 'orange', and subsequently mentally rehearsed them. First, continuous wavelet transform was performed on epoched signals and subsequently two-tailed t-tests between two classes were conducted to determine statistical differences in frequency and time (t-CWT). Features were also extracted using t-CWT and subsequently classified using a support vector machine. θ and γ phase amplitude coupling (PAC) was also assessed within and between tasks. All binary classifications produced accuracies significantly greater (80-90%) than chance level, supporting the use of t-CWT in determining relative oscillatory involvements. While the perception task dynamically invoked all frequencies with more prominent θ and α activity, the covert task favoured higher frequencies with significantly higher γ activity than perception. Moreover, the perception condition produced significant θ-γ PAC, corroborating a reported linkage between syllabic and phonemic sampling. Although this coupling was found to be suppressed in the covert condition, we found significant cross-task coupling between perception θ and covert speech γ. Covert speech processing appears to be largely associated with higher frequencies of EEG. Importantly, the significant cross-task coupling between speech perception and covert speech, in the absence of within-task covert speech PAC, supports the notion that the γ- and θ-bands subserve, respectively, shared and unique encoding processes across tasks.

RevDate: 2022-01-10

Mathur V, Talapatra S, Kar S, et al (2021)

In Vivo Partial Restoration of Neural Activity across Severed Mouse Spinal Cord Bridged with Ultralong Carbon Nanotubes.

ACS applied bio materials, 4(5):4071-4078.

Electrically bridging severed nerves in vivo has transformative healthcare implications, but current materials are inadequate. Carbon nanotubes (CNTs) are promising, with low impedance, high charge injection capacity, high flexibility, are chemically inert, and can electrically couple to neurons. Ultralong CNTs are unexplored for neural applications. Using only ultralong CNTs in saline, without neuroregeneration or rehabilitation, we partially restored neural activity across a severed mouse spinal cord, recovering 23.8% of the intact amplitude, while preserving signal shape. Neural signals are preferentially facilitated over artifact signals by a factor of ×5.2, suggesting ultralong CNTs are a promising material for neural-scaffolding and neural-electronics applications.

RevDate: 2022-01-10

Li X, Song Y, Xiao G, et al (2021)

PDMS-Parylene Hybrid, Flexible Micro-ECoG Electrode Array for Spatiotemporal Mapping of Epileptic Electrophysiological Activity from Multicortical Brain Regions.

ACS applied bio materials, 4(11):8013-8022.

Epilepsy detection and focus location are urgent issues that need to be solved in epilepsy research. A cortex conformable and fine spatial accuracy electrocorticogram (ECoG) sensor array, especially for real-time detection of multicortical functional regions and delineating epileptic focus remains a challenge. Here, we fabricated a polydimethylsiloxane (PDMS)-parylene hybrid, flexible micro-ECoG electrode array. The multiwalled carbon nanotubes (MWCNTs)/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) nanocomposite-modified electrode interface significantly improved the sensing performance with low impedance (20.68 ± 6.65 kΩ), stable phase offset, and high sensitivity. The electrophysiological activities of multicortical brain regions (somatosensory cortex, parietal association cortex, and visual cortex) were simultaneously monitored during normal and epileptic statuses. The epileptic ECoG activities spread spatiotemporally from the starting point toward the adjacent cortex. Significant variations of the waveform, power, and frequency band were observed. The ECoG potential (123 ± 23 μV) at normal status was prominently up to 417 ± 87 μV at the spike wave stage. Besides, the power for epileptic activity (11.049 ± 4.513 μW) was 10 times higher than that (1.092 ± 0.369 μW) for normal activity. In addition, the theta frequency band was found to be a characteristic frequency band of epileptic signals. These joint analysis results of multicortical regions indicated that the active micron-scale region on the parietal association cortex was more likely to be the epileptogenic focus. Cortical mapping with high spatial detail provides the accurate delineation of lesions. The flexible micro-ECoG electrode array is a powerful tool for constructing a spatiotemporal map of the cortex. It provides a technical platform for epileptic focus location, biomedical diagnosis, and brain-computer interaction.

RevDate: 2022-01-10

Ziadeh H, Gulyas D, Nielsen LD, et al (2021)

"Mine Works Better": Examining the Influence of Embodiment in Virtual Reality on the Sense of Agency During a Binary Motor Imagery Task With a Brain-Computer Interface.

Frontiers in psychology, 12:806424.

Motor imagery-based brain-computer interfaces (MI-BCI) have been proposed as a means for stroke rehabilitation, which combined with virtual reality allows for introducing game-based interactions into rehabilitation. However, the control of the MI-BCI may be difficult to obtain and users may face poor performance which frustrates them and potentially affects their motivation to use the technology. Decreases in motivation could be reduced by increasing the users' sense of agency over the system. The aim of this study was to understand whether embodiment (ownership) of a hand depicted in virtual reality can enhance the sense of agency to reduce frustration in an MI-BCI task. Twenty-two healthy participants participated in a within-subject study where their sense of agency was compared in two different embodiment experiences: 1) avatar hand (with body), or 2) abstract blocks. Both representations closed with a similar motion for spatial congruency and popped a balloon as a result. The hand/blocks were controlled through an online MI-BCI. Each condition consisted of 30 trials of MI-activation of the avatar hand/blocks. After each condition a questionnaire probed the participants' sense of agency, ownership, and frustration. Afterwards, a semi-structured interview was performed where the participants elaborated on their ratings. Both conditions supported similar levels of MI-BCI performance. A significant correlation between ownership and agency was observed (r = 0.47, p = 0.001). As intended, the avatar hand yielded much higher ownership than the blocks. When controlling for performance, ownership increased sense of agency. In conclusion, designers of BCI-based rehabilitation applications can draw on anthropomorphic avatars for the visual mapping of the trained limb to improve ownership. While not While not reducing frustration ownership can improve perceived agency given sufficient BCI performance. In future studies the findings should be validated in stroke patients since they may perceive agency and ownership differently than able-bodied users.

RevDate: 2022-01-10

Chang Y, He C, Tsai BY, et al (2021)

Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings.

Frontiers in human neuroscience, 15:785562.

Mental state changes induced by stimuli under experimental settings or by daily events in real life affect task performance and are entwined with physical and mental health. In this study, we developed a physiological state indicator with five parameters that reflect the subject's real-time physiological states based on online EEG signal processing. These five parameters are attention, fatigue, stress, and the brain activity shifts of the left and right hemispheres. We designed a target detection experiment modified by a cognitive attention network test for validating the effectiveness of the proposed indicator, as such conditions would better approximate a real chaotic environment. Results demonstrated that attention levels while performing the target detection task were significantly higher than during rest periods, but also exhibited a decay over time. In contrast, the fatigue level increased gradually and plateaued by the third rest period. Similar to attention levels, the stress level decreased as the experiment proceeded. These parameters are therefore shown to be highly correlated to different stages of the experiment, suggesting their usage as primary factors in passive brain-computer interfaces (BCI). In addition, the left and right brain activity indexes reveal the EEG neural modulations of the corresponding hemispheres, which set a feasible reference of activation for an active BCI control system, such as one executing motor imagery tasks. The proposed indicator is applicable to potential passive and active BCI applications for monitoring the subject's physiological state change in real-time, along with providing a means of evaluating the associated signal quality to enhance the BCI performance.

RevDate: 2022-01-10
CmpDate: 2022-01-10

Mihelj E, Bächinger M, Kikkert S, et al (2021)

Mental individuation of imagined finger movements can be achieved using TMS-based neurofeedback.

NeuroImage, 242:118463.

Neurofeedback (NF) in combination with motor imagery (MI) can be used for training individuals to volitionally modulate sensorimotor activity without producing overt movements. However, until now, NF methods were of limited utility for mentally training specific hand and finger actions. Here we employed a novel transcranial magnetic stimulation (TMS) based protocol to probe and detect MI-induced motor activity patterns in the primary motor cortex (M1) with the aim to reinforce selective facilitation of single finger representations. We showed that TMS-NF training but not MI training with uninformative feedback enabled participants to selectively upregulate corticomotor excitability of one finger, while simultaneously downregulating excitability of other finger representations within the same hand. Successful finger individuation during MI was accompanied by strong desynchronization of sensorimotor brain rhythms, particularly in the beta band, as measured by electroencephalography. Additionally, informative TMS-NF promoted more dissociable EEG activation patterns underlying single finger MI, when compared to MI of the control group where no such feedback was provided. Our findings suggest that selective TMS-NF is a new approach for acquiring the ability of finger individuation even if no overt movements are performed. This might offer new treatment modality for rehabilitation after stroke or spinal cord injury.

RevDate: 2022-01-10
CmpDate: 2022-01-10

Song X, Chen X, Guo J, et al (2022)

Living Rat SSVEP Mapping With Acoustoelectric Brain Imaging.

IEEE transactions on bio-medical engineering, 69(1):75-82.

OBJECTIVE: Acoustoelectric Brain Imaging (ABI) is a potential method for mapping brain electrical activity with high spatial resolution (millimeter). To resolve the key issue for eventual realization of ABI, testing that recorded acoustoelectric (AE) signal can be used to decode intrinsic brain electrical activity, the experiment of living rat SSVEP measurement with ABI is implemented.

METHOD: A 1-MHz ultrasound transducer is focused on the visual cortex of anesthetized rat. With visual stimulus, the electroencephalogram and AE signal are simultaneously recorded with Pt electrode. Besides, with FUS transducer scanning at the visual cortex, corresponding AE signals at different spatial positions are decoded and imaged.

RESULTS: Consistent with the directly measured SSVEP, decoded AE signal presents a clear event-related spectral perturbation (ERSP). And, decoded AE signal is of high amplitude response at the base and harmonics of the visual stimulus frequency. What's more, for timing signal, a significant positive amplitude correlation is observed between decoded AE signal and simultaneously measured SSVEP. In addition, the mean SNRs of SSVEP and decoded AE signal are both significantly higher than that of background EEG. Finally, with one fixed recording electrode, the active area with an inner diameter of 1mm is located within the 4 mm×4 mm measurement region.

CONCLUSION: Experimental results demonstrate that the millimeter-level spatial resolution SSVEP measurement of living rat is achieved through ABI for the first time.

SIGNIFICANCE: This study confirms that ABI should shed light on high spatiotemporal resolution neuroimaging.

RevDate: 2022-01-08

Perrault JR, Page-Karjian A, Morgan AN, et al (2022)

Morphometrics and blood analytes of leatherback sea turtle hatchlings (Dermochelys coriacea) from Florida: reference intervals, temporal trends with clutch deposition date, and body size correlations.

Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology [Epub ahead of print].

The northwest Atlantic leatherback sea turtle (Dermochelys coriacea) population is exhibiting decreasing trends along numerous nesting beaches. Since population health and viability are inherently linked, it is important to establish species- and life-stage class-specific blood analyte reference intervals (RIs) so that effects of future disturbances on organismal health can be better understood. For hatchling leatherbacks, the objectives of this study were to (1) establish RIs for morphometrics and blood analytes; (2) evaluate correlations between hatchling morphometrics, blood analytes, and hatching success; and (3) determine temporal trends in hatchling morphometrics and blood analytes across nesting season. Blood samples were collected from 176 naturally emerging leatherback hatchlings from 18 clutches. Reference intervals were established for morphometrics and blood analytes. Negative relationships were noted between hatchling mass and packed cell volume, total white blood cells, heterophils, lymphocytes, and total protein and between body condition index (BCI) and immature red blood cells (RBC), RBC polychromasia and anisocytosis, and total protein. Clutch deposition date showed positive relationships with lymphocytes and total protein, and negative relationships with hatchling mass and BCI. Hatching success was positively correlated with mass, and negatively with total protein and glucose, suggesting that nutritional provisions in eggs, incubation time, and/or metabolic rates could change later in the season and affect survivorship. These various observed correlations provide evidence for increased physiological stress (e.g., inflammation, subclinical dehydration) in hatchlings emerging later in nesting season, presumably due to increased nest temperatures or other environmental factors (e.g., moisture/rainfall). Data reported herein provide morphometric and blood analyte data for leatherback hatchlings and will allow for future investigations into spatiotemporal trends and responses to various stressors.

RevDate: 2022-01-08

Endris BS, Dinant GJ, Gebreyesus SH, et al (2022)

Risk factors of anemia among preschool children in Ethiopia: a Bayesian geo-statistical model.

BMC nutrition, 8(1):2.

BACKGROUND: The etiology and risk factors of anemia are multifactorial and varies across context. Due to the geospatial clustering of anemia, identifying risk factors for anemia should account for the geographic variability. Failure to adjust for spatial dependence whilst identifying risk factors of anemia could give spurious association. We aimed to identify risk factors of anemia using a Bayesian geo-statistical model.

METHODS: We analyzed the Ethiopian Demographic and Health Survey (EDHS) 2016 data. The sample was selected using a stratified, two- stage cluster sampling design. In this survey, 9268 children had undergone anemia testing. Hemoglobin level was measured using a HemoCue photometer and the results were recorded onsite. Based on the World Health Organization's cut-off points, a child was considered anaemic if their altitude adjusted haemoglobin (Hb) level was less than 11 g/dL. Risk factors for anemia were identified using a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data. Posterior means and 95% credible interval (BCI) were used to report our findings. We used a statistically significant level at 0.05.

RESULT: The 9267 children in our study were between 6 and 59 months old. Fifty two percent (52%) of children were males. Thirteen percent (13%) of children were from the highest wealth quintile whereas 23% from the lowest wealth quintile. Most of them lived in rural areas (90%). The overall prevalence of anemia among preschool children was 57% (95% CI: 54.4-59.4). We found that child stunting (OR = 1.26, 95% BCI (1.14-1.39), wasting (OR = 1.35, 95% BCI (1.15-1.57), maternal anemia (OR = 1.61, 95% BCI (1.44-1.79), mothers having two under five children (OR = 1.2, 95% BCI (1.08-1.33) were risk factors associated with anemia among preschool children. Children from wealthy households had lower risk of anemia (AOR = 0.73, 95% BCI (0.62-0.85).

CONCLUSION: Using the Bayesian geospatial statistical modeling, we were able to account for spatial dependent structure in the data, which minimize spurious association. Childhood Malnutrition, maternal anemia, increased fertility, and poor wealth status were risk factors of anemia among preschool children in Ethiopia. The existing anaemia control programs such as IFA supplementation during pregnancy should be strengthened to halt intergenerational effect of anaemia. Furthermore, routine childhood anaemia screening and intervention program should be part of the Primary health care in Ethiopia.

RevDate: 2022-01-07

Zheng L, Pei W, Gao X, et al (2022)

A high-performance brain switch based on code-modulated visual evoked potentials.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Asynchronous brain-computer interfaces (BCIs) are more practical and natural compared to synchronous BCIs. A brain switch is a standard asynchronous BCI, which can automatically detect the specified change of the brain and discriminate between the control state and the idle state. The current brain switches still face challenges on relatively long reaction time (RT) and high false positive rate (FPR).

APPROACH: In this paper, an online electroencephalography-based brain switch is designed to realize a fast reaction and keep long idle time (IDLE) without false positives (FPs) using code-modulated visual evoked potentials (c-VEPs). Two stimulation paradigms were designed and compared in the experiments: multi-code concatenate modulation (concatenation mode) and single-code periodic modulation (periodic mode). Using a task-related component analysis-based detection algorithm, EEG data can be decoded into a series of code indices. Brain states can be detected by a template matching approach with a sliding window on the output series.

MAIN RESULTS: The online experiments achieved an average RT of 1.49 seconds when the average IDLE for each FP was 68.57 minutes (1.46e-2 FP/min) or an average RT of 1.67 seconds without FPs.

SIGNIFICANCE: This study provides a practical c-VEP based brain switch system with both fast reaction and low FPR during idle state, which can be used in various BCI applications.

RevDate: 2022-01-07

Wang X, Wang M, Sheng H, et al (2021)

Subdural neural interfaces for long-term electrical recording, optical microscopy and magnetic resonance imaging.

Biomaterials, 281:121352 pii:S0142-9612(21)00708-0 [Epub ahead of print].

Though commonly used, metal electrodes are incompatible with brain tissues, often leading to injury and failure to achieve long-term implantation. Here we report a subdural neural interface of hydrogel functioning as an ionic conductor, and elastomer as a dielectric. We demonstrate that it incurs a far less glial reaction and less cerebrovascular destruction than a metal electrode. Using a cat model, the hydrogel electrode was able to record electrical signals comparably in quality to a metal electrode. The hydrogel-elastomer neural interface also readily facilitated multimodal functions. Both the hydrogel and elastomer are transparent, enabling in vivo optical microscopy. For imaging, cerebral vessels and calcium signals were imaged using two-photon microscopy. The new electrode is compatible with magnetic resonance imaging and does not cause artifact images. Such a new multimodal neural interface could represent immediate opportunity for use in broad areas of application in neuroscience research and clinical neurology.

RevDate: 2022-01-07

Nafees M, Ullah S, I Ahmed (2022)

Modulation of drought adversities in Vicia faba by the application of plant growth promoting rhizobacteria and biochar.

Microscopy research and technique [Epub ahead of print].

Drought is the greatest threat to world food security, seen as the catalyst for the great famines of the past. Given that the world's water supply is limited, it is likely that future demand of food for increasing population will further exacerbate the drought effects. Therefore, the present study was aimed to investigate the effect of biochar and plant growth promoting rhizobacteria (PGPR) Sphingobacterium pakistanensis (NCCP246) and Cellulomonas pakistanensis (NCCP11) on agronomic and physiological attributes of Vicia faba two varieties Desi (V1) and Pulista (V2) under induced drought stress. The seeds were sown in earthen pots filled with 3 kg sand and soil (1:2), and biochar (0 and 5% w/w) in triplicate arranged in complete randomized design. Analysis of biochar possessed 0.49 g cm-3 bulk density, 9.6 pH; 5.4 cmol kg-1 cation exchange capacity, 3.64% organic carbon and EC 6.7 ds/m. Agronomic attributes including seed LAI, LAR, SVI, %PHSI and RWC were improved by 30.4-180.4%, 14.37-47.20%, 37.64-50.91%, 18.21-30.80, and 35.82-54.34% in both varieties by the co-application of biochar and PGPR. Stomatal physiology and epidermal vigor was successfully improved by the application of PGPR and biochar as analyzed by scanning electron microscopy (SEM). Photosynthetic pigments, flavonoids, phenols, proline and glycine betaine were amplified by 58.33-173.8%, 50.59-130.33%, 46.58-86.62%, 46.66-109.30%, 35.74-56.10%, and 21.96-77.22% in both varieties by the co-application of biochar and PGPR. So, the present work concluded that, combined application of biochar and PGPR could be an effective strategy to alleviate the adversities of drought in V. faba growing in drastic ecosystems.

RevDate: 2022-01-07
CmpDate: 2022-01-07

Liu Y, Chen C, Belkacem AN, et al (2021)

Motor Imagination of Lower Limb Movements at Different Frequencies.

Journal of healthcare engineering, 2021:4073739.

Motor imagination (MI) is the mental process of only imagining an action without an actual movement. Research on MI has made significant progress in feature information detection and machine learning decoding algorithms, but there are still problems, such as a low overall recognition rate and large differences in individual execution effects, which make the development of MI run into a bottleneck. Aiming at solving this bottleneck problem, the current study optimized the quality of the MI original signal by "enhancing the difficulty of imagination tasks," conducted the qualitative and quantitative analyses of EEG rhythm characteristics, and used quantitative indicators, such as ERD mean value and recognition rate. Research on the comparative analysis of the lower limb MI of different tasks, namely, high-frequency motor imagination (HFMI) and low-frequency motor imagination (LFMI), was conducted. The results validate the following: the average ERD of HFMI (-1.827) is less than that of LFMI (-1.3487) in the alpha band, so did (-3.4756 < -2.2891) in the beta band. In the alpha and beta characteristic frequency bands, the average ERD of HFMI is smaller than that of LFMI, and the ERD values of the two are significantly different (p=0.0074 < 0.01; r = 0.945). The ERD intensity STD values of HFMI are less than those of LFMI. which suggests that the ERD intensity individual difference among the subjects is smaller in the HFMI mode than in the LFMI mode. The average recognition rate of HFMI is higher than that of LFMI (87.84% > 76.46%), and the recognition rate of the two modes is significantly different (p=0.0034 < 0.01; r = 0.429). In summary, this research optimizes the quality of MI brain signal sources by enhancing the difficulty of imagination tasks, achieving the purpose of improving the overall recognition rate of the lower limb MI of the participants and reducing the differences of individual execution effects and signal quality among the subjects.

RevDate: 2022-01-06

Ravi A, Lu J, Pearce S, et al (2022)

Enhanced System Robustness of Asynchronous BCI in Augmented Reality using Steady-state Motion Visual Evoked Potential.

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

This study evaluated the effect of change in background on steady state visually evoked potentials (SSVEP) and steady state motion visually evoked potentials (SSMVEP) based brain computer interfaces (BCI) in a small-profile augmented reality (AR) headset. A four target SSVEP and SSMVEP BCI was implemented using the Cognixion AR headset prototype. An active (AB) and a non-active background (NB) were evaluated. The signal characteristics and classification performance of the two BCI paradigms were studied. Offline analysis was performed using canonical correlation analysis (CCA) and complex-spectrum based convolutional neural network (C-CNN). Finally, the asynchronous pseudo-online performance of the SSMVEP BCI was evaluated. Signal analysis revealed that the SSMVEP stimulus was more robust to change in background compared to SSVEP stimulus in AR. The decoding performance revealed that the C-CNN method outperformed CCA for both stimulus types and NB background, in agreement with results in the literature. The average offline accuracies for W=1s of C-CNN were (NB vs. AB): SSVEP: 82% ±15% vs. 60% ±21% and SSMVEP: 71.4% ± 22% vs. 63.5% ± 18%. Additionally, for W=2s, the AR-SSMVEP BCI with the C-CNN method was 83.3% ± 27% (NB) and 74.1% ±22% (AB). The results suggest that with the C-CNN method, the AR-SSMVEP BCI is both robust to change in background conditions and provides high decoding accuracy compared to the AR-SSVEP BCI. This study presents novel results that highlight the robustness and practical application of SSMVEP BCIs developed with a low-cost AR headset.

RevDate: 2022-01-06

Thompson DE, Mowla MR, Dhuyvetter KJ, et al (2019)

Automated Artifact Rejection Algorithms Harm P3 Speller Brain-Computer Interface Performance.

Brain computer interfaces (Abingdon, England), 6(4):141-148.

Brain-Computer Interfaces (BCIs) have been used to restore communication and control to people with severe paralysis. However, non-invasive BCIs based on electroencephalogram (EEG) are particularly vulnerable to noise artifacts. These artifacts, including electro-oculogram (EOG), can be orders of magnitude larger than the signal to be detected. Many automated methods have been proposed to remove EOG and other artifacts from EEG recordings, most based on blind source separation. This work presents a performance comparison of ten different automated artifact removal methods. Unfortunately, all tested methods substantially and significantly reduced P3 Speller BCI performance, and all methods were more likely to reduce performance than increase it. The least harmful methods were titled SOBI, JADER, and EFICA, but even these methods caused an average of approximately ten percentage points drop in BCI accuracy. Possible mechanistic causes for this empirical performance deduction are proposed.

RevDate: 2022-01-06

Wang Y, Zhou S, Qi X, et al (2022)

Efficacy of front-line immunochemotherapy for follicular lymphoma: a network meta-analysis of randomized controlled trials.

Blood cancer journal, 12(1):1.

Front-line treatment for follicular lymphoma has evolved with the introduction of maintenance therapy, bendamustine (Benda), obinutuzumab (G), and lenalidomide (Len). We conducted a random-effects Bayesian network meta-analysis (NMA) of phase 3 randomized controlled trials (RCTs) to identify the regimens with superior efficacy. Progression-free survival (PFS) was compared between 11 modern regimens with different immunochemotherapy and maintenance strategies. G-Benda-G resulted in with the best PFS, with an HR of 0.41 compared to R-Benda, a surface under the cumulative ranking curve (SUCRA) of 0.97, a probability of being the best treatment (PbBT) of 72%, and a posterior ranking distribution (PoRa) of 1 (95% BCI 1-3). This was followed by R-Benda-R4 (HR = 0.49, PbBT = 25%, PoRa = 2) and R-Benda-R (HR = 0.60, PbBT = 3%, PoRa = 3). R-CHOP-R (HR = 0.96) and R-Len-R (HR = 0.97) had similar efficacy to R-Benda. Bendamustine was a better chemotherapy backbone than CHOP either with maintenance (R-Benda-R vs R-CHOP-R, HR = 0.62; G-Benda-G vs G-CHOP-G, HR = 0.55) or without maintenance therapy (R-Benda vs R-CHOP, HR = 0.68). Rituximab maintenance improved PFS following R-CHOP (R-CHOP-R vs R-CHOP, HR = 0.65) or R-Benda (R-Benda-R vs R-Benda, HR = 0.60; R-Benda-R4 vs R-Benda, HR = 0.49). In the absence of multi-arm RCTs that include all common regimens, this NMA provides an important and useful guide to inform treatment decisions.

RevDate: 2022-01-05

Chen J, Yi W, Wang D, et al (2022)

FB-CGANet: filter bank Channel Group Attention network for multi-class motor imagery classification.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Motor imagery-based brain computer interface (MI-BCI) is one of the most important BCI paradigms and can identify the target limb of subjects from the feature of MI-based Electroencephalography (EEG) signals. Deep learning methods, especially lightweight neural networks, provide an efficient technique for MI decoding, but the performance of lightweight neural networks is still limited and need further improving. This paper aimed to design a novel lightweight neural network for improving the performance of multi-class MI decoding.

APPROACH: A hybrid filter bank structure that can extract information in both time and frequency domain was proposed and combined with a novel channel attention method Channel Group Attention (CGA) to build a lightweight neural network Filter Bank Channel Group Attention Network (FB-CGANet). Accompanied with FB-CGANet, the Band Exchange data augmentation method was proposed to generate training data for networks with filter bank structure.

MAIN RESULTS: The proposed method can achieve higher 4-class average accuracy (79.4%) than compared methods on the BCI Competition IV IIa dataset in the experiment on the unseen evaluation data. Also, higher average accuracy (93.5%) than compared methods can be obtained in the cross-validation experiment.

SIGNIFICANCE: This work implies the effectiveness of channel attention and filter bank structure in lightweight neural networks and provides a novel option for multi-class motor imagery classification.

RevDate: 2022-01-05

Wang Z, Zhang J, Zhang X, et al (2022)

Transformer Model for Functional Near-Infrared Spectroscopy Classification.

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

Functional near-infrared spectroscopy (fNIRS) is a promising neuroimaging technology. The fNIRS classification problem has always been the focus of the brain-computer interface (BCI). Inspired by the success of Transformer based on self-attention mechanism in the fields of natural language processing and computer vision, we propose an fNIRS classification network based on Transformer, named fNIRS-T. We explore the spatial-level and channel-level representation of fNIRS signals to improve data utilization and network representation capacity. Besides, a preprocessing module, which consists of one-dimensional average pooling and layer normalization, is designed to replace filtering and baseline correction of data preprocessing. It makes fNIRS-T an end-to-end network, called fNIRS-PreT. Compared with traditional machine learning classifiers, convolutional neural network (CNN), and long short-term memory (LSTM), the proposed models obtain the best accuracy on three open-access datasets. Specifically, in the most extensive ternary classification task (30 subjects) that includes three types of overt movements, fNIRS-T, CNN, and LSTM obtain 75.49%, 72.89%, and 61.94% on test sets, respectively. Compared to traditional classifiers, fNIRS-T is at least 27.41% higher than statistical features and 6.79% higher than well-designed features. In the individual subject experiment of the ternary classification task, fNIRS-T achieves an average subject accuracy of 78.22% and surpasses CNN and LSTM by a large margin of +4.75% and +11.33%. fNIRS-PreT using raw data also achieves competitive performance to fNIRS-T. Therefore, the proposed models improve the performance of fNIRS-based BCI significantly.

RevDate: 2022-01-05

Imbrosci B, Schmitz D, M Orlando (2022)

Automated detection and localization of synaptic vesicles in electron microscopy images.

eNeuro pii:ENEURO.0400-20.2021 [Epub ahead of print].

Information transfer and integration in the brain occurs at chemical synapses and is mediated by the fusion of synaptic vesicles filled with neurotransmitter. Synaptic vesicle dynamic spatial organization regulates synaptic transmission as well as synaptic plasticity. Because of their small size, synaptic vesicles require electron microscopy for their imaging, and their analysis is conducted manually. The manual annotation and segmentation of the hundreds to thousands of synaptic vesicles, is highly time consuming and limits the throughput of data collection. To overcome this limitation, we built an algorithm, mainly relying on convolutional neural networks, capable of automatically detecting and localizing synaptic vesicles in electron micrographs. The algorithm was trained on murine synapses but we show that it works well on synapses from different species, ranging from zebrafish to human, and from different preparations. As output, we provide the vesicles count and coordinates, the nearest neighbor distance and the estimate of the vesicles area. We also provide a graphical user interface (GUI) to guide users through image analysis, result visualization and manual proof-reading. The application of our algorithm is especially recommended for images produced by transmission electron microscopy. Since this type of imaging is used routinely to investigate presynaptic terminals, our solution will likely be of interest for numerous research groups.Significance StatementThe analysis of synaptic vesicles provides important insights towards the understanding of synaptic transmission and plasticity mechanisms. However, up to date, this analysis is still a very time-consuming manual process. In the present study we present a user-friendly algorithm, mainly based on convolutional neural networks, for automating the detection of synaptic vesicles in electron micrographs. This approach allows faster and more standardized analyses.

RevDate: 2022-01-05
CmpDate: 2022-01-05

Tiwari K, Kyrki V, Cheung A, et al (2021)

DeFINE: Delayed feedback-based immersive navigation environment for studying goal-directed human navigation.

Behavior research methods, 53(6):2668-2688.

With the advent of consumer-grade products for presenting an immersive virtual environment (VE), there is a growing interest in utilizing VEs for testing human navigation behavior. However, preparing a VE still requires a high level of technical expertise in computer graphics and virtual reality, posing a significant hurdle to embracing the emerging technology. To address this issue, this paper presents Delayed Feedback-based Immersive Navigation Environment (DeFINE), a framework that allows for easy creation and administration of navigation tasks within customizable VEs via intuitive graphical user interfaces and simple settings files. Importantly, DeFINE has a built-in capability to provide performance feedback to participants during an experiment, a feature that is critically missing in other similar frameworks. To show the usability of DeFINE from both experimentalists' and participants' perspectives, a demonstration was made in which participants navigated to a hidden goal location with feedback that differentially weighted speed and accuracy of their responses. In addition, the participants evaluated DeFINE in terms of its ease of use, required workload, and proneness to induce cybersickness. The demonstration exemplified typical experimental manipulations DeFINE accommodates and what types of data it can collect for characterizing participants' task performance. With its out-of-the-box functionality and potential customizability due to open-source licensing, DeFINE makes VEs more accessible to many researchers.

RevDate: 2022-01-05
CmpDate: 2022-01-05

Baarbé J, Lizarraga KJ, Brown MJN, et al (2021)

Motor blocks during bilateral stepping in Parkinson's disease and effects of dopaminergic medication.

Parkinsonism & related disorders, 85:1-4.

INTRODUCTION: Freezing of gait (FOG) is a complex symptom in Parkinson's disease (PD) that manifests during walking as limited forward progression despite the intention to walk. It is unclear if lower limb motor blocks (LLMB) that occur independently from FOG are related to overground FOG and the effects of dopaminergic medications.

METHODS: Nineteen patients with PD were tested on two separate days in the dopaminergic medication "on" and "off" states. The patients completed a series of freezing-provoking tasks while videotaped. Raters assessed videos for FOG presence using Movement Disorders Society Unified Parkinson's Disease Rating Scale item 3.11 score greater than or equal to 1 and FOG severity using the standardized FOG score. Whilst seated in a virtual environment, patients and 20 healthy controls stepped in right-left sequence on foot pedals. Frequency and percent time in LLMB were assessed for accurate classification of FOG presence and correlation to the FOG score.

RESULTS: Frequency and percent time spent in LLMB predicted the presence of FOG in both medication states. Percent time spent in LLMB correlated with FOG severity in both medication states. LLMB frequency predicted FOG severity in the "off" state only.

CONCLUSIONS: LLMB during bilateral stepping in a virtual environment predicted the presence and severity of FOG in PD in both "on" and "off" medication states. These findings support the use of this non-walking paradigm to detect and assess FOG in PD patients unable or unsafe to walk.

RevDate: 2022-01-04

Andersen RA, Aflalo T, Bashford L, et al (2022)

Exploring Cognition with Brain-Machine Interfaces.

Annual review of psychology, 73:131-158.

Traditional brain-machine interfaces decode cortical motor commands to control external devices. These commands are the product of higher-level cognitive processes, occurring across a network of brain areas, that integrate sensory information, plan upcoming motor actions, and monitor ongoing movements. We review cognitive signals recently discovered in the human posterior parietal cortex during neuroprosthetic clinical trials. These signals are consistent with small regions of cortex having a diverse role in cognitive aspects of movement control and body monitoring, including sensorimotor integration, planning, trajectory representation, somatosensation, action semantics, learning, and decision making. These variables are encoded within the same population of cells using structured representations that bind related sensory and motor variables, an architecture termed partially mixed selectivity. Diverse cognitive signals provide complementary information to traditional motor commands to enable more natural and intuitive control of external devices.

RevDate: 2022-01-04

Kerley CI, Chaganti S, Nguyen TQ, et al (2022)

pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis.

Neuroinformatics [Epub ahead of print].

Along with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering the secrets of EMR. Despite this recent growth, there is a lack of approachable software tools for conducting these analyses on large-scale EMR cohorts. In this article, we introduce pyPheWAS, an open-source python package for conducting PheDAS and related analyses. This toolkit includes 1) data preparation, such as cohort censoring and age-matching; 2) traditional PheDAS analysis of ICD-9 and ICD-10 billing codes; 3) PheDAS analysis applied to a novel EMR phenotype mapping: current procedural terminology (CPT) codes; and 4) novelty analysis of significant disease-phenotype associations found through PheDAS. The pyPheWAS toolkit is approachable and comprehensive, encapsulating data prep through result visualization all within a simple command-line interface. The toolkit is designed for the ever-growing scale of available EMR data, with the ability to analyze cohorts of 100,000 + patients in less than 2 h. Through a case study of Down Syndrome and other intellectual developmental disabilities, we demonstrate the ability of pyPheWAS to discover both known and potentially novel disease-phenotype associations across different experiment designs and disease groups. The software and user documentation are available in open source at https://github.com/MASILab/pyPheWAS .

RevDate: 2022-01-03

Wang Y, Luo Z, Zhao S, et al (2021)

Spatial Localization in Target Detection Based on Decoding N2pc Component.

Journal of neuroscience methods pii:S0165-0270(21)00375-7 [Epub ahead of print].

BACKGROUND: The Gaze-independent BCI system is used to restore communication in patients with eye movement disorders. One available control mechanism is the utilization of spatial attention. However, spatial information is mostly used to simply answer the "True/False" target recognition question and is seldom used to improve the efficiency of target detection. Therefore, it is necessary to utilize the potential advantages of spatial attention to improving the target detection efficiency.

NEW METHOD: We found that N2pc could be used to assess spatial attention shift and determine target position. It was a negative wave in the posterior brain on the contralateral target stimulus. From this, we designed a novel spatial coding paradigm to achieve two main purposes at each stimulus presentation: target recognition and spatial localization.

We used a two-step classification framework to decode the P300 and N2pc components.

RESULTS: The average decoding accuracy of fourteen subjects was 84.43% (σ=1.14%), and the classification accuracy of six subjects was more than 85%. The information transfer rate of the spatial coding paradigm could reach 60.52 bits/min. Compared with the single stimulus paradigm, the target detection efficiency was successfully improved by approximately 10%.

CONCLUSIONS: The spatial coding paradigm proposed in this paper answered both "True/False" and "Left/Right" questions by decoding spatial attention information. This method could significantly improve image detection efficiencies, such as visual search tasks, Internet image screening, or military target determination.

RevDate: 2022-01-04

Sykes AL, Larrieu E, Poggio TV, et al (2022)

Modelling diagnostics for Echinococcus granulosus surveillance in sheep using Latent Class Analysis: Argentina as a case study.

One health (Amsterdam, Netherlands), 14:100359.

Echinococcus granulosus sensu lato is a globally prevalent zoonotic parasitic cestode leading to cystic echinococcosis (CE) in both humans and sheep with both medical and financial impacts, whose reduction requires the application of a One Health approach to its control. Regarding the animal health component of this approach, lack of accurate and practical diagnostics in livestock impedes the assessment of disease burden and the implementation and evaluation of control strategies. We use of a Bayesian Latent Class Analysis (LCA) model to estimate ovine CE prevalence in sheep samples from the Río Negro province of Argentina accounting for uncertainty in the diagnostics. We use model outputs to evaluate the performance of a novel recombinant B8/2 antigen B subunit (rEgAgB8/2) indirect enzyme-linked immunosorbent assay (ELISA) for detecting E. granulosus in sheep. Necropsy (as a partial gold standard), western blot (WB) and ELISA diagnostic data were collected from 79 sheep within two Río Negro slaughterhouses, and used to estimate individual infection status (assigned as a latent variable within the model). Using the model outputs, the performance of the novel ELISA at both individual and flock levels was evaluated, respectively, using a receiver operating characteristic (ROC) curve, and simulating a range of sample sizes and prevalence levels within hypothetical flocks. The estimated (mean) prevalence of ovine CE was 27.5% (95%Bayesian credible interval (95%BCI): 13.8%-58.9%) within the sample population. At the individual level, the ELISA had a mean sensitivity and specificity of 55% (95%BCI: 46%-68%) and 68% (95%BCI: 63%-92%), respectively, at an optimal optical density (OD) threshold of 0.378. At the flock level, the ELISA had an 80% probability of correctly classifying infection at an optimal cut-off threshold of 0.496. These results suggest that the novel ELISA could play a useful role as a flock-level diagnostic for CE surveillance in the region, supplementing surveillance activities in the human population and thus strengthening a One Health approach. Importantly, selection of ELISA cut-off threshold values must be tailored according to the epidemiological situation.

RevDate: 2022-01-04
CmpDate: 2022-01-04

Zhang S, Sun L, Mao X, et al (2021)

Review on EEG-Based Authentication Technology.

Computational intelligence and neuroscience, 2021:5229576.

With the rapid development of brain-computer interface technology, as a new biometric feature, EEG signal has been widely concerned in recent years. The safety of brain-computer interface and the long-term insecurity of biometric authentication have a new solution. This review analyzes the biometrics of EEG signals, and the latest research is involved in the authentication process. This review mainly introduced the method of EEG-based authentication and systematically introduced EEG-based biometric cryptosystems for authentication for the first time. In cryptography, the key is the core basis of authentication in the cryptographic system, and cryptographic technology can effectively improve the security of biometric authentication and protect biometrics. The revocability of EEG-based biometric cryptosystems is an advantage that traditional biometric authentication does not have. Finally, the existing problems and future development directions of identity authentication technology based on EEG signals are proposed, providing a reference for the related studies.

RevDate: 2022-01-04

Koelewijn AD, Audu M, Del-Ama AJ, et al (2021)

Adaptation Strategies for Personalized Gait Neuroprosthetics.

Frontiers in neurorobotics, 15:750519.

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.

RevDate: 2022-01-04

He C, Liu J, Zhu Y, et al (2021)

Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review.

Frontiers in human neuroscience, 15:765525.

Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.

RevDate: 2022-01-04
CmpDate: 2022-01-04

Shang B, P Shang (2021)

Multivariate synchronization curve: A measure of synchronization in different multivariate signals.

Chaos (Woodbury, N.Y.), 31(12):123121.

As a method to measure the synchronization between two different sets of signals, the multivariate synchronization index (MSI) has played an irreplaceable role in the field of frequency recognition of brain-computer interface since it was proposed. On this basis, we make a generalization of MSI by using the escort distribution to replace the original distribution. In this way, MSI can be converted from a determined value to the multivariate synchronization curve, which will vary as the parameter q of the escort distribution changes. Numerical experiments are carried out on both simulated and real-world data to confirm the effectiveness of this new method. Compared with the case of MSI (i.e., q = 1), the extended form of MSI proposed in this article can obviously capture the relationship between signals more comprehensively, implying that it is a more perfect method to describe the synchronization between them. The results reveal that this method can not only effectively extract the important information contained in different signals, but also has the potential to become a practical synchronization measurement method of multivariate signals.

RevDate: 2022-01-03

Zhou L, Zhu Q, Wu B, et al (2021)

A comparison of directed functional connectivity among fist-related brain activities during movement imagery, movement execution, and movement observation.

Brain research, 1777:147769 pii:S0006-8993(21)00628-4 [Epub ahead of print].

Brain-computer interface (BCI) has been widely used in sports training and rehabilitation training. It is primarily based on action simulation, including movement imagery (MI) and movement observation (MO). However, the development of BCI technology is limited due to the challenge of getting an in-depth understanding of brain networks involved in MI, MO, and movement execution (ME). To better understand the brain activity changes and the communications across various brain regions under MO, ME, and MI, this study conducted the fist experiment under MO, ME, and MI. We recorded 64-channel electroencephalography (EEG) from 39 healthy subjects (25 males, 14 females, all right-handed) during fist tasks, obtained intensities and locations of sources using EEG source imaging (ESI), computed source activation modes, and finally investigated the brain networks using spectral Granger causality (GC). The brain regions involved in the three motor conditions are similar, but the degree of participation of each brain region and the network connections among the brain regions are different. MO, ME, and MI did not recruit shared brain connectivity networks. In addition, both source activation modes and brain network connectivity had lateralization advantages.

RevDate: 2021-12-31

Weakley A, Liu X, Duvvur S, et al (2021)

Interactive care: A web-based platform for remote caregiving and functional independence in older adults with cognitive impairment.

Alzheimer's & dementia : the journal of the Alzheimer's Association, 17 Suppl 8:e055322.

BACKGROUND: Remote family caregiving of cognitively impaired older adults has many challenges including lack of evidence-based interventions that meets the needs of both stakeholders. Further, physical distance may lead to emotional distance including feelings of loneliness and depression in the care receiver as well as helplessness and burden in the caregiver. To fill this gap, our team sought to develop the Interactive-Care (I-Care) platform which transforms and augments the traditional whiteboard, a compensatory cognitive rehabilitation tool for dementia care, to a digital format accessible from any device. I-Care provides 5 main functions: home page (orientation information), calendar, to-do list, messaging hub, and brain health center. A beta version of I-Care was developed based on usability principles from human-computer interaction literature, caregiving needs assessments, and guidelines for creating technology for older adults with cognitive impairment. The current project focuses on the first of three rounds (1. experts, 2. caregivers, 3. individuals with mild cognitive impairment/mild dementia) of iterative interface design.

METHOD: Individual semi-structured interviews were conducted with experts in dementia caregiving, gerontechnology, and digital health. Prior to each interview, experts were provided with the I-Care weblink and a project summary for review. All interviews were conducted over live-stream video and were audio recorded for transcription. The "share screen" function was used to demonstrate the different I-Care components and feedback was solicited.

RESULTS: On a scale from 1-10, with 1 being not at all useful and 10 being very useful, experts rated I-Care as a useful tool both for individuals with mild cognitive impairment/mild dementia (M = 8.33, SD = 1.53) and remote caregivers (M = 9.33, SD = 1.15). Recommended improvements included minor modifications to the user interface (e.g., text contrast, reorganization of visual components), functionality (e.g., ability to add or subtract features based on level of cognitive impairment), and expansion (e.g., medication tracking with pillbox sensor).

CONCLUSIONS: Although more data is needed, I-Care is a promising remote caregiving tool with potential to reduce emotional distance despite physical divide. Next steps include conducting similar interviews with caregivers and individuals with cognitive impairment followed by a pilot study with 10 care dyads.

RevDate: 2022-01-01

Zhou Y, Hu L, Yu T, et al (2021)

A BCI-Based Study on the Relationship Between the SSVEP and Retinal Eccentricity in Overt and Covert Attention.

Frontiers in neuroscience, 15:746146.

Covert attention aids us in monitoring the environment and optimizing performance in visual tasks. Past behavioral studies have shown that covert attention can enhance spatial resolution. However, electroencephalography (EEG) activity related to neural processing between central and peripheral vision has not been systematically investigated. Here, we conducted an EEG study with 25 subjects who performed covert attentional tasks at different retinal eccentricities ranging from 0.75° to 13.90°, as well as tasks involving overt attention and no attention. EEG signals were recorded with a single stimulus frequency to evoke steady-state visual evoked potentials (SSVEPs) for attention evaluation. We found that the SSVEP response in fixating at the attended location was generally negatively correlated with stimulus eccentricity as characterized by Euclidean distance or horizontal and vertical distance. Moreover, more pronounced characteristics of SSVEP analysis were also acquired in overt attention than in covert attention. Furthermore, offline classification of overt attention, covert attention, and no attention yielded an average accuracy of 91.42%. This work contributes to our understanding of the SSVEP representation of attention in humans and may also lead to brain-computer interfaces (BCIs) that allow people to communicate with choices simply by shifting their attention to them.

RevDate: 2022-01-04
CmpDate: 2022-01-04

Shen X, Zhang X, Y Wang (2021)

Kernel Temporal Difference based Reinforcement Learning for 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, 2021:6721-6724.

Brain-machine interfaces (BMIs) enable people with disabilities to control external devices with their motor intentions through a decoder. Compared with supervised learning, reinforcement learning (RL) is more promising for the disabled because it can assist them to learn without actual limb movement. Current RL decoders deal with tasks with immediate reward delivery. But for tasks where the reward is only given by the end of the trial, existing RL methods may take a long time to train and are prone to becoming trapped in the local minima. In this paper, we propose to embed temporal difference method (TD) into Quantized Attention-Gated Kernel Reinforcement Learning (QAGKRL) to solve this temporal credit assignment problem. This algorithm utilizes a kernel network to ensure the global linear structure and adopts a softmax policy to efficiently explore the state-action mapping through TD error. We simulate a center-out task where the agent needs several steps to first reach a periphery target and then return to the center to get the external reward. Our proposed algorithm is tested on simulated data and compared with two state-of-the-art models. We find that introducing the TD method to QAGKRL achieves a prediction accuracy of 96.2% ± 0.77% (mean ± std), which is significantly better the other two methods.Clinical Relevance-This paper proposes a novel kernel temporal difference RL method for the multi-step task with delayed reward delivery, which potentially enables BMI online continuous decoding.

RevDate: 2022-01-04
CmpDate: 2022-01-04

Tan J, Shen X, Zhang X, et al (2021)

Multivariate Encoding Analysis of Medial Prefrontal Cortex Cortical Activity during Task Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021:6699-6702.

Studies have shown that medial prefrontal cortex (mPFC) is responsible for outcome evaluation. Some recent studies also suggest that mPFC may play an important role in goal planning and action execution when performing a task. If the information encoded in mPFC can be accurately extracted and identified, it can improve the design of brain-machine interfaces by better reconstructing subjects' motion intention guided by reward information. In this paper, we investigate whether mPFC neural signals simultaneously encode information of goal planning, action execution and outcome evaluation. Linear-nonlinear-Poisson (LNP) model is applied for encoding analysis on mPFC neural spike data when a rat is learning a two-lever-press discrimination task. We use the L2-norm of tuning parameter in LNP model to indicate the importance of the encoded information and compare the spike train prediction performance of LNP model using all information, the most significant information and reward information only. The preliminary results indicate that mPFC activity can encode simultaneously the information of goal planning, action execution and outcome evaluation and that all the relevant information could be reconstructed from mPFC spike trains on a single trial basis.

RevDate: 2022-01-04
CmpDate: 2022-01-04

Sahoo KP, Radhakrishnan A, Pratiher S, et al (2021)

Alterations in Multi-channel EEG Dynamics During a Stressful Shooting Task in Virtual Reality Systems.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021:6207-6210.

This paper explores power spectrum-based features extracted from the 64-channel electroencephalogram (EEG) signals to analyze brain activity alterations during a virtual reality (VR)-based stressful shooting task, with low and high difficulty levels, from an initial resting baseline. This paper also investigates the variations in EEG across several experimental sessions performed over multiple days. Results indicate that patterns of changes in different power bands of the EEG are consistent with high mental stress levels during the shooting task compared to baseline. Although there is one inconsistency, overall, the brain patterns indicate higher stress levels during high difficulty tasks than low difficulty tasks and in the first session compared to the last session.

RevDate: 2022-01-03
CmpDate: 2022-01-03

Lu HY, Bollimunta A, Eaton RW, et al (2021)

Short-training Algorithm for Online Brain-machine Interfaces Using One-photon Microendoscopic Calcium Imaging.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021:5860-5863.

Calcium imaging has great potential to be applied to online brain-machine interfaces (BMIs). As opposed to two-photon imaging settings, a one-photon microendoscopic imaging device can be chronically implanted and is subject to little motion artifacts. Traditionally, one-photon microendoscopic calcium imaging data are processed using the constrained nonnegative matrix factorization (CNMFe) algorithm, but this batched processing algorithm cannot be applied in real-time. An online analysis of calcium imaging data algorithm (or OnACIDe) has been proposed, but OnACIDe updates the neural components by repeatedly performing neuron identification frame-by-frame, which may decelerate the update speed if applying to online BMIs. For BMI applications, the ability to track a stable population of neurons in real-time has a higher priority over accurately identifying all the neurons in the field of view. By leveraging the fact that 1) microendoscopic recordings are rather stable with little motion artifacts and 2) the number of neurons identified in a short training period is sufficient for potential online BMI tasks such as cursor movements, we proposed the short-training CNMFe algorithm (stCNMFe) that skips motion correction and neuron identification processes to enable a more efficient BMI training program in a one-photon microendoscopic setting.

RevDate: 2022-01-03
CmpDate: 2022-01-03

Malekzadeh-Arasteh O, Pu H, Danesh AR, et al (2021)

A Fully-Integrated 1µW/Channel Dual-Mode Neural Data Acquisition System for Implantable 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, 2021:5780-5783.

This paper presents an ultra-low power mixed-signal neural data acquisition (MSN-DAQ) system that enables a novel low-power hybrid-domain neural decoding architecture for implantable brain-machine interfaces with high channel count. Implemented in 180nm CMOS technology, the 32-channel custom chip operates at 1V supply voltage and achieves excellent performance including 1.07µW/channel, 2.37/5.62 NEF/PEF and 88dB common-mode rejection ratio (CMRR) with significant back-end power-saving advantage compared to prior works. The fabricated prototype was further evaluated with in vivo human tests at bedside, and its performance closely follows that of a commercial recording system.

RevDate: 2022-01-03
CmpDate: 2022-01-03

Galati A, Schoppa R, A Lu (2021)

Exploring the SenseMaking Process through Interactions and fNIRS in Immersive Visualization.

IEEE transactions on visualization and computer graphics, 27(5):2714-2724.

Theories of cognition inform our decisions when designing human-computer interfaces, and immersive systems enable us to examine these theories. This work explores the sensemaking process in an immersive environment through studying both internal and external user behaviors with a classical visualization problem: a visual comparison and clustering task. We developed an immersive system to perform a user study, collecting user behavior data from different channels: AR HMD for capturing external user interactions, functional near-infrared spectroscopy (fNIRS) for capturing internal neural sequences, and video for references. To examine sensemaking, we assessed how the layout of the interface (planar 2D vs. cylindrical 3D layout) and the challenge level of the task (low vs. high cognitive load) influenced the users' interactions, how these interactions changed over time, and how they influenced task performance. We also developed a visualization system to explore joint patterns among all the data channels. We found that increased interactions and cerebral hemodynamic responses were associated with more accurate performance, especially on cognitively demanding trials. The layout types did not reliably influence interactions or task performance. We discuss how these findings inform the design and evaluation of immersive systems, predict user performance and interaction, and offer theoretical insights about sensemaking from the perspective of embodied and distributed cognition.

RevDate: 2022-01-03
CmpDate: 2022-01-03

Monteiro P, Goncalves G, Coelho H, et al (2021)

Hands-free interaction in immersive virtual reality: A systematic review.

IEEE transactions on visualization and computer graphics, 27(5):2702-2713.

Hands are the most important tool to interact with virtual environments, and they should be available to perform the most critical tasks. For example, a surgeon in VR should keep his/her hands on the instruments and be able to do secondary tasks without performing a disruptive event to the operative task. In this common scenario, one can observe that hands are not available for interaction. The goal of this systematic review is to survey the literature and identify which hands-free interfaces are used, the performed interaction tasks, what metrics are used for interface evaluation, and the results of such evaluations. From 79 studies that met the eligibility criteria, the voice is the most studied interface, followed by the eye and head gaze. Some novel interfaces were brain interfaces and face expressions. System control and selection represent most of the interaction tasks studied and most studies evaluate interfaces for usability. Despite the best interface depending on the task and study, the voice was found to be versatile and showed good results amongst the studies. More research is recommended to improve the practical use of the interfaces and to evaluate the interfaces more formally.

RevDate: 2022-01-03
CmpDate: 2022-01-03

Sullivan JA, Dumont JR, Memar S, et al (2021)

New frontiers in translational research: Touchscreens, open science, and the mouse translational research accelerator platform.

Genes, brain, and behavior, 20(1):e12705.

Many neurodegenerative and neuropsychiatric diseases and other brain disorders are accompanied by impairments in high-level cognitive functions including memory, attention, motivation, and decision-making. Despite several decades of extensive research, neuroscience is little closer to discovering new treatments. Key impediments include the absence of validated and robust cognitive assessment tools for facilitating translation from animal models to humans. In this review, we describe a state-of-the-art platform poised to overcome these impediments and improve the success of translational research, the Mouse Translational Research Accelerator Platform (MouseTRAP), which is centered on the touchscreen cognitive testing system for rodents. It integrates touchscreen-based tests of high-level cognitive assessment with state-of-the art neurotechnology to record and manipulate molecular and circuit level activity in vivo in animal models during human-relevant cognitive performance. The platform also is integrated with two Open Science platforms designed to facilitate knowledge and data-sharing practices within the rodent touchscreen community, touchscreencognition.org and mousebytes.ca. Touchscreencognition.org includes the Wall, showcasing touchscreen news and publications, the Forum, for community discussion, and Training, which includes courses, videos, SOPs, and symposia. To get started, interested researchers simply create user accounts. We describe the origins of the touchscreen testing system, the novel lines of research it has facilitated, and its increasingly widespread use in translational research, which is attributable in part to knowledge-sharing efforts over the past decade. We then identify the unique features of MouseTRAP that stand to potentially revolutionize translational research, and describe new initiatives to partner with similar platforms such as McGill's M3 platform (m3platform.org).

RevDate: 2022-01-03
CmpDate: 2022-01-03

Van den Broeck L, Sierksma A, Hansquine P, et al (2021)

Comparison between touchscreen operant chambers and water maze to detect early prefrontal dysfunction in mice.

Genes, brain, and behavior, 20(1):e12695.

The relative lack of sensitive and clinically valid tests of rodent behavior might be one of the reasons for the limited success of the clinical translation of preclinical Alzheimer's disease (AD) research findings. There is a general interest in innovative behavioral methodology, and protocols have been proposed for touchscreen operant chambers that might be superior to existing cognitive assessment methods. We assessed and analyzed touchscreen performance in several novel ways to examine the possible occurrence of early signs of prefrontal (PFC) functional decline in the APP/PS1 mouse model of AD. Touchscreen learning performance was compared between APP/PS1-21 mice and wildtype littermates on a C57BL/6J background at 3, 6 and 12 months of age in parallel to the assessment of spatial learning, memory and cognitive flexibility in the Morris water maze (MWM). We found that older mice generally needed more training sessions to complete the touchscreen protocol than younger ones. Older mice also displayed defects in MWM working memory performance, but touchscreen protocols detected functional changes beginning at 3 months of age. Histological changes in PFC of APP/PS1 mice indeed occurred as early as 3 months. Our results suggest that touchscreen operant protocols are more sensitive to PFC dysfunction, which is of relevance to the use of these tasks and devices in preclinical AD research and experimental pharmacology.

RevDate: 2022-01-03
CmpDate: 2022-01-03

Mosser CA, Haqqee Z, Nieto-Posadas A, et al (2021)

The McGill-Mouse-Miniscope platform: A standardized approach for high-throughput imaging of neuronal dynamics during behavior.

Genes, brain, and behavior, 20(1):e12686.

Understanding the rules that govern neuronal dynamics throughout the brain to subserve behavior and cognition remains one of the biggest challenges in neuroscience research. Recent technical advances enable the recording of increasingly larger neuronal populations to produce increasingly more sophisticated datasets. Despite bold and important open-science and data-sharing policies, these datasets tend to include unique data acquisition methods, behaviors, and file structures. Discrepancies between experimental protocols present key challenges in comparing data between laboratories and across different brain regions and species. Here, we discuss our recent efforts to create a standardized and high-throughput research platform to address these issues. The McGill-Mouse-Miniscope (M3) platform is an initiative to combine miniscope calcium imaging with standardized touchscreen-based animal behavioral testing. The goal is to curate an open-source and standardized framework for acquiring, analyzing, and accessing high-quality data of the neuronal dynamics that underly cognition throughout the brain in mice, marmosets, and models of disease. We end with a discussion of future developments and a call for users to adopt this standardized approach.

RevDate: 2022-01-03
CmpDate: 2022-01-03

Broschard MB, Kim J, Love BC, et al (2021)

Category learning in rodents using touchscreen-based tasks.

Genes, brain, and behavior, 20(1):e12665.

Categorization is a fundamental cognitive function that organizes our experiences into meaningful "chunks." This category knowledge can then be generalized to novel stimuli and situations. Multiple clinical populations, including people with Parkinson's disease, amnesia, autism, ADHD and schizophrenia, have impairments in the acquisition and use of categories. Although rodent research is well suited for examining the neural mechanisms underlying cognitive functions, many rodent cognitive tasks have limited translational value. To bridge this gap, we use touchscreens to permit greater flexibility in stimulus presentation and task design, track key dependent measures, and minimize experimenter involvement. Touchscreens offer a valuable tool for creating rodent cognitive tasks that are directly comparable to tasks used with humans. Touchscreen tasks are also readily used with cutting-edge neuroscientific methods that are difficult to do in humans such as optogenetics, chemogenetics, neurophysiology and calcium imaging (using miniscopes). In this review, we show advantages of touchscreen-based tasks for studying category learning in rats. We also address multiple factors for consideration when designing category learning tasks, including the limitations of the rodent visual system, experimental design, and analysis strategies.

RevDate: 2022-01-03
CmpDate: 2022-01-03

Lee JH, Cho SY, E Kim (2021)

Translational cognitive neuroscience of dementia with touchscreen operant chambers.

Genes, brain, and behavior, 20(1):e12664.

Translational cognitive neuroscience of dementia involves mainly two areas: the validation of newly developed dementia animal models and the preclinical assessment of novel drug candidates in such model animals. To validate new animal models, a multidomain panel (battery) approach is essential in that dementia is, by definition, not merely a memory disorder but rather a multidomain cognitive/behavior disorder: animal modeling with a certain type of dementia would develop cognitive impairments in multiple (two at minimum) domains in a specific order according to unique spreading patterns of its neuropathology. In new drug development, the availability of highly sensitive tools assessing animal cognition is crucial to the detection of cognitive decline at the earliest stage of the disease, which may be an optimal time point to test a drug candidate. Using interspecies translatable (analogous) cognitive tasks would also be necessary to successfully predict the efficacy of drug candidates in subsequent clinical trials. Currently, this translational prediction is seriously limited given discrepancies in behavioral assessment methods between animals and humans in the preclinical and clinical trials, respectively. Since neurodegenerative diseases are often accompanied by not only cognitive but also affective and movement disorders, simultaneous assessment of task-relevant locomotor behavior and motivation is also important to rule out the effects of potential confounders. The touchscreen operant platform may satisfy these needs by offering several advantages over conventional methodology. In this review, we discuss the touchscreen operant chamber system and highlight some of its qualities as a promising and desirable tool for translational research of dementia.

RevDate: 2022-01-03
CmpDate: 2022-01-03

Dumont JR, Salewski R, F Beraldo (2021)

Critical mass: The rise of a touchscreen technology community for rodent cognitive testing.

Genes, brain, and behavior, 20(1):e12650.

The rise in the number of users and institutions utilizing the rodent touchscreen technology for cognitive testing over the past decade has prompted the need for knowledge mobilization and community building. To address the needs of the growing touchscreen community, the first international touchscreen symposium was hosted at Western University. Attendees from around the world attended talks from expert neuroscientists using touchscreens to examine a vast array of questions regarding cognition and the nervous system. In addition to the symposium, a subset of attendees was invited to partake in a hands-on training course where they received touchscreen training covering both hardware and software components. Beyond the two touchscreen events, virtual platforms have been developed to further support touchscreen users: (a) Mousebytes.ca, which includes a data repository of rodent touchscreen tasks, and (b) Touchscreencognition.org, an online community with numerous training and community resources, perhaps most notably a forum where members can ask and answer questions. The advantages of the rodent touchscreen technology for cognitive neuroscience research has allowed neuroscientists from diverse backgrounds to test specific cognitive processes using well-validated and standardized apparatus, contributing to its rise in popularity and its relevance to modern neuroscience research. The commitment of the touchscreen community to data, task development and information sharing not only ensures an expansive future of the use of rodent touchscreen technology but additionally, quality research that will increase translation from preclinical studies to clinical successes.

RevDate: 2021-12-30

Ruiz S, Virseda-Chamorro M, Salinas J, et al (2021)

Influence of ATOMS implant on the voiding phase of patients with post-prostatectomy urinary incontinence.

Neurourology and urodynamics [Epub ahead of print].

OBJECTIVE: To assess changes in voiding phase, especially urethral resistance after post-prostatectomy urinary incontinence (PPI) treatment with the Adjustable TransObturator Male System (ATOMS).

MATERIAL AND METHODS: A longitudinal prospective study was performed on 45 men treated with ATOMS for PPI, with the intention to evaluate the changes produced by the implant on the voiding phase. Patients with preoperative urodynamic study were offered postoperative urodynamic evaluation, and both studies were compared. The following urodynamic date were evaluated: maximum voiding detrusor pressure, detrusor pressure at maximum flow rate, maximum flow rate (Qmax), voiding volume, post-void residue, bladder outlet obstruction index (BOOI), urethral resistance factor (URA), and bladder contractility index (BCI). The statistical analysis used were the mean comparison test for dependent groups (Student's t test) for parametric variables and the Wilcoxon test for non-parametric variables. The signification level was set at 95% bilateral.

RESULTS: A total of 37 patients (82.2%) used zero pads/day at the time of urodynamic postoperative evaluation and pad-test evolved from 592 ± 289 ml baseline to 25 ± 40 ml (p = 0.0001). Significant differences were observed in Qmax (15 ± 8.3 before and 11 ± 8.3 after surgery; p = 0.008), voiding volume (282 ± 130.7 before and 184 ± 99.92 after surgery). BOOI (-12 ± 23.9 before and -2 ± 21.4 after surgery; p = 0.025) and BCI (93 ± 46.4 before and 76 ± 46.0 after surgery; p = 0.044). In no case did we observe postoperative bladder outlet obstruction, according to URA parameter below 29 cm H2 O in all cases. There was not a significant variation either in post-void urinary residual volume (15 ± 47.4 before and 14 ± 24.2 after surgery, p = 0.867).

CONCLUSIONS: The ATOMS implant induces a decrease of Qmax, voided volume, and bladder contractility and an increase of BOOI. However, our findings suggest that ATOMS device does not cause bladder outlet obstruction.

RevDate: 2021-12-30

Bibián C, Irastorza-Landa N, Schönauer M, et al (2021)

On the Extraction of Purely Motor EEG Neural Correlates during an Upper Limb Visuomotor Task.

Cerebral cortex (New York, N.Y. : 1991) pii:6490277 [Epub ahead of print].

Deciphering and analyzing the neural correlates of different movements from the same limb using electroencephalography (EEG) would represent a notable breakthrough in the field of sensorimotor neurophysiology. Functional movements involve concurrent posture co-ordination and head and eye movements, which create electrical activity that affects EEG recordings. In this paper, we revisit the identification of brain signatures of different reaching movements using EEG and present, test, and validate a protocol to separate the effect of head and eye movements from a reaching task-related visuomotor brain activity. Ten healthy participants performed reaching movements under two different conditions: avoiding head and eye movements and moving with no constrains. Reaching movements can be identified from EEG with unconstrained eye and head movement, whereas the discriminability of the signals drops to chance level otherwise. These results show that neural patterns associated with different arm movements could only be extracted from EEG if the eye and head movements occurred concurrently with the task, polluting the recordings. Although these findings do not imply that brain correlates of reaching directions cannot be identified from EEG, they show the consequences that ignoring these events can have in any EEG study that includes a visuomotor task.

RevDate: 2021-12-30

Mederos A, Galarraga D, van der Graaf-van Bloois L, et al (2021)

Performance of bovine genital campylobacteriosis diagnostic tests in bulls from Uruguay: a Bayesian latent class model approach.

Tropical animal health and production, 54(1):32.

The sensitivity (Se) and specificity (Sp) of three diagnostic tests for the detection of Campylobacter fetus venerealis (Cfv) using field samples were estimated using a Bayesian latent class model (BLCM), accounting for the absence of a gold standard. The tests included in this study were direct immunofluorescence antibody test (IFAT), polymerase chain reaction (PCR), and real-time PCR (RT-PCR). Twelve farms from two different populations were selected and bull prepuce samples were collected. The IFAT was performed according to the OIE Manual. The conventional PCR was performed as multiplex, targeting the gene nahE for C. fetus species identification and insertion element ISCfe1 for Cfv identification. The RT-PCR was performed as uniplex: one targeting the gene nahE for C. fetus and the other targeting the insertion ISCfe1 (ISC2) for Cfv. Results from the BLCM showed a median Se of 11.7% (Bayesian credibility interval (BCI): 1.93-29.79%), 53.7% (BCI: 23.1-95.0%), and 36.1% (BCI: 14.5-71.7%) for IFAT, PCR, and RT-PCR respectively. The Sp were 94.5% (BCI: 90.1-97.9%), 97.0% (BCI: 92.9-99.3%), and 98.4% (BCI: 95.3-99.7%) for IFAT, PCR, and RT-PCR respectively. The correlation between PCR and RT-PCR was positive and low in samples from both sampled population (0.63% vs 8.47%). These results suggest that diagnostic sensitivity of the studied tests is lower using field samples than using pure Cfv strains.

RevDate: 2021-12-30

Li C, Wei J, Huang X, et al (2021)

Effects of a Brain-Computer Interface-Operated Lower Limb Rehabilitation Robot on Motor Function Recovery in Patients with Stroke.

Journal of healthcare engineering, 2021:4710044.

Purpose: To observe the effect of a brain-computer interface-operated lower limb rehabilitation robot (BCI-LLRR) on functional recovery from stroke and to explore mechanisms.

Methods: Subacute-phase stroke patients were randomly divided into two groups. In addition to the routine intervention, patients in the treatment group trained on the BCI-LLRR and underwent the lower limb pedal training in the control group, both for the same time (30 min/day). All patients underwent assessment by instruments such as the National Institutes of Health Stroke Scale (NIHSS) and the Fugl-Meyer upper and lower limb motor function and balance tests, at 2 and 4 weeks of treatment and at 3 months after the end of treatment. Patients were also tested before treatment and after 4 weeks by leg motor evoked potential (MEP) and diffusion tensor imaging/tractography (DTI/DTT) of the head.

Results: After 4 weeks, the Fugl-Meyer leg function and NIHSS scores were significantly improved in the treatment group vs. controls (P < 0.01). At 3 months, further significant improvement was observed. The MEP amplitude and latency of the treatment group were significantly improved vs. controls. The effect of treatment on fractional anisotropy values was not significant.

Conclusions: The BCI-LLRR promoted leg functional recovery after stroke and improved activities of daily living, possibly by improving cerebral-cortex excitability and white matter connectivity.

RevDate: 2021-12-30
CmpDate: 2021-12-30

Waschke J, Hlawitschka M, Anlas K, et al (2021)

linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser.

PLoS computational biology, 17(11):e1009503.

In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data can be a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise packet that makes it straightforward to create and test hypotheses collaboratively. To address these challenges, we have developed a tool, linus, which makes the process of exploring and sharing 3D trajectories as easy as browsing a website. We provide a python script that reads trajectory data, enriches them with additional features such as edge bundling or custom axes, and generates an interactive web-based visualisation that can be shared online. linus facilitates the collaborative discovery of patterns in complex trajectory data.

RevDate: 2021-12-29

Xu C, Neuroth TA, Fujiwara T, et al (2021)

A Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts.

IEEE transactions on visualization and computer graphics, PP: [Epub ahead of print].

Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce a predictive visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The systems machine-learning-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using DTI and T1-weighted images from the research database of Parkinsons Progression Markers Initiative.

RevDate: 2021-12-29

Becsei Á, Solymosi N, Csabai I, et al (2021)

Detection of antimicrobial resistance genes in urban air.

MicrobiologyOpen, 10(6):e1248.

To understand antibiotic resistance in pathogenic bacteria, we need to monitor environmental microbes as reservoirs of antimicrobial resistance genes (ARGs). These bacteria are present in the air and can be investigated with the whole metagenome shotgun sequencing approach. This study aimed to investigate the feasibility of a method for metagenomic analysis of microbial composition and ARGs in the outdoor air. Air samples were collected with a Harvard impactor in the PM10 range at 50 m from a hospital in Budapest. From the DNA yielded from samples of PM10 fraction single-end reads were generated with an Ion Torrent sequencer. During the metagenomic analysis, reads were classified taxonomically. The core bacteriome was defined. Reads were assembled to contigs and the ARG content was analyzed. The dominant genera in the core bacteriome were Bacillus, Acinetobacter, Leclercia and Paenibacillus. Among the identified ARGs best hits were vanRA, Bla1, mphL, Escherichia coli EF-Tu mutants conferring resistance to pulvomycin; BcI, FosB, and mphM. Despite the low DNA content of the samples of PM10 fraction, the number of detected airborne ARGs was surprisingly high.

RevDate: 2021-12-29
CmpDate: 2021-12-29

Covantes-Osuna C, López JB, Paredes O, et al (2021)

Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold.

Sensors (Basel, Switzerland), 21(24): pii:s21248305.

The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu's version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.

RevDate: 2021-12-28

Jin J, Sun H, Daly I, et al (2021)

A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery based Brain-Computer Interface.

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

The motor imagery (MI) based brain-computer interfaces (BCIs) have been proposed as a potential physical rehabilitation technology. However, the low classification accuracy achievable with MI tasks is still a challenge when building effective BCI systems. We propose a novel MI classification model based on measurement of functional connectivity between brain regions and graph theory. Specifically, motifs describing local network structures in the brain are extracted from functional connectivity graphs. A graph embedding model called Ego-CNNs is then used to build a classifier, which can convert the graph from a structural representation to a fixed-dimensional vector for detecting critical structure in the graph. We validate our proposed method on four datasets, and the results show that our proposed method produces high classification accuracies in two-class classification tasks (92.8% for dataset 1, 93.4% for dataset 2, 96.5% for dataset 3, and 80.2% for dataset 4) and multiclass classification tasks (90.33% for dataset 1). Our proposed method achieves a mean Kappa value of 0.88 across nine participants, which is superior to other methods we compared it to. These results indicate that there is a local structural difference in functional connectivity graphs extracted under different motor imagery tasks. Our proposed method has great potential for motor imagery classification in future studies.

RevDate: 2021-12-28

Aydemir O, Saka K, M Ozturk (2021)

Investigating the effects of stimulus duration and inter-stimulus interval parameters on P300 based BCI application performance.

Computer methods in biomechanics and biomedical engineering [Epub ahead of print].

The main goal of electroencephalography (EEG) based brain-computer interface (BCI) research is to develop a fast and higher classification accuracy (CA) rate method than those of existing ones. Generally, in BCI applications, either motor imagery or event-related P300 based techniques are used for data recording. The stimulus duration (SD) and the inter-stimulus interval (ISI) are crucial two parameters directly affecting the decision speed of the BCI system. In this study, we investigated the performance of the P300 based application in terms of speed and CA for three kinds of protocols which are called fast, medium, and slow included different SD and the ISI values. The training and test data sets were recorded in one week of delay from 8 subjects. The features were extracted by standard deviation, variance, mean, Wavelet Transform and Fourier Transform techniques. Afterwards, they were classified by the k-nearest neighbor algorithm. We obtained 87.08%, 85.41% and 83.95% average CA rate for the fast, medium, and slow protocols, respectively. The obtained results showed that the proposed fast protocol method achieved CA rate between 78.33% and 93.33%. Based on the obtained results, it can be concluded that the fast protocol values can be used for establishing a more accurate and faster P300 based BCI.

RevDate: 2021-12-28

Rossi F, Savi F, Prestia A, et al (2021)

Combining Action Observation Treatment with a Brain-Computer Interface System: Perspectives on Neurorehabilitation.

Sensors (Basel, Switzerland), 21(24): pii:s21248504.

Action observation treatment (AOT) exploits a neurophysiological mechanism, matching an observed action on the neural substrates where that action is motorically represented. This mechanism is also known as mirror mechanism. In a typical AOT session, one can distinguish an observation phase and an execution phase. During the observation phase, the patient observes a daily action and soon after, during the execution phase, he/she is asked to perform the observed action at the best of his/her ability. Indeed, the execution phase may sometimes be difficult for those patients where motor impairment is severe. Although, in the current practice, the physiotherapist does not intervene on the quality of the execution phase, here, we propose a stimulation system based on neurophysiological parameters. This perspective article focuses on the possibility to combine AOT with a brain-computer interface system (BCI) that stimulates upper limb muscles, thus facilitating the execution of actions during a rehabilitation session. Combining a rehabilitation tool that is well-grounded in neurophysiology with a stimulation system, such as the one proposed, may improve the efficacy of AOT in the treatment of severe neurological patients, including stroke patients, Parkinson's disease patients, and children with cerebral palsy.

RevDate: 2021-12-28

Hag A, Handayani D, Altalhi M, et al (2021)

Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm.

Sensors (Basel, Switzerland), 21(24): pii:s21248370.

In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.

RevDate: 2021-12-28

Mezzina G, Annese VF, D De Venuto (2021)

A Cybersecure P300-Based Brain-to-Computer Interface against Noise-Based and Fake P300 Cyberattacks.

Sensors (Basel, Switzerland), 21(24): pii:s21248280.

In a progressively interconnected world where the Internet of Things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user's physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions.

RevDate: 2021-12-28

Ascari L, Marchenkova A, Bellotti A, et al (2021)

Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies.

Sensors (Basel, Switzerland), 21(24): pii:s21248167.

Nowadays, the growing interest in gathering physiological data and human behavior in everyday life scenarios is paralleled by an increase in wireless devices recording brain and body signals. However, the technical issues that characterize these solutions often limit the full brain-related assessments in real-life scenarios. Here we introduce the Biohub platform, a hardware/software (HW/SW) integrated wearable system for multistream synchronized acquisitions. This system consists of off-the-shelf hardware and state-of-art open-source software components, which are highly integrated into a high-tech low-cost solution, complete, yet easy to use outside conventional labs. It flexibly cooperates with several devices, regardless of the manufacturer, and overcomes the possibly limited resources of recording devices. The Biohub was validated through the characterization of the quality of (i) multistream synchronization, (ii) in-lab electroencephalographic (EEG) recordings compared with a medical-grade high-density device, and (iii) a Brain-Computer-Interface (BCI) in a real driving condition. Results show that this system can reliably acquire multiple data streams with high time accuracy and record standard quality EEG signals, becoming a valid device to be used for advanced ergonomics studies such as driving, telerehabilitation, and occupational safety.

RevDate: 2021-12-28

Zheng S, Wu J, Hu Z, et al (2021)

Epidemiology and Molecular Transmission Characteristics of HIV in the Capital City of Anhui Province in China.

Pathogens (Basel, Switzerland), 10(12): pii:pathogens10121554.

Hefei, Anhui province, is one of the cities in the Yangtze River Delta, where many people migrate to Jiangsu, Zhejiang and Shanghai. High migration also contributes to the HIV epidemic. This study explored the HIV prevalence in Hefei to provide a reference for other provinces and assist in the prevention and control of HIV in China. A total of 816 newly reported people with HIV in Hefei from 2017 to 2020 were recruited as subjects. HIV subtypes were identified by a phylogenetic tree. The most prevalent subtypes were CRF07_BC (41.4%), CRF01_AE (38.1%) and CRF55_01B (6.3%). Molecular networks were inferred using HIV-TRACE. The largest and most active transmission cluster was CRF55_01B in Hefei's network. A Chinese national database (50,798 sequences) was also subjected to molecular network analysis to study the relationship between patients in Hefei and other provinces. CRF55_01B and CRF07_BC-N had higher clustered and interprovincial transmission rates in the national molecular network. People with HIV in Hefei mainly transmitted the disease within the province. Finally, we displayed the epidemic trend of HIV in Hefei in recent years with the dynamic change of effective reproductive number (Re). The weighted overall Re increased rapidly from 2012 to 2015, with a peak value of 3.20 (95% BCI, 2.18-3.85). After 2015, Re began to decline and remained stable at around 1.80. In addition, the Re of CRF55_01B was calculated to be between 2.0 and 4.0 in 2018 and 2019. More attention needs to be paid to the rapid spread of CRF55_01B and CRF07_BC-N strains among people with HIV and the high Re in Hefei. These data provide necessary support to guide the targeted prevention and control of HIV.

RevDate: 2021-12-28
CmpDate: 2021-12-28

Ran X, Zhang Y, Shen C, et al (2021)

Dimensionality Reduction of Local Field Potential Features with Convolution Neural Network in Neural Decoding: A Pilot Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021:1047-1050.

Local field potentials (LFPs) have better long-term stability compared with spikes in brain-machine interfaces (BMIs). Many studies have shown promising results of LFP decoding, but the high-dimensional feature of LFP still hurdle the development of the BMIs to low-cost. In this paper, we proposed a framework of a 1D convolution neural network (CNN) to reduce the dimensionality of the LFP features. For evaluating the performance of this architecture, the reduced LFP features were decoded to cursor position (Center-out task) by a Kalman filter. The Principal components analysis (PCA) was also performed as a comparison. The results showed that the CNN model could reduce the dimensionality of LFP features to a smaller size without significant performance loss. The decoding result based on the CNN features outperformed that based on the PCA features. Moreover, the reduced features by CNN also showed robustness across different sessions. These results demonstrated that the LFP features reduced by the CNN model achieved low cost without sacrificing high-performance and robustness, suggesting that this method could be used for portable BMI systems in the future.

RevDate: 2021-12-28
CmpDate: 2021-12-28

Tun NN, Sanuki F, K Iramina (2021)

EEG-EMG Correlation Analysis with Linear and Nonlinear Coupling Methods Across Four Motor Tasks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021:783-786.

Correlation between brain and muscle signal is referred to as functional coupling. The amount of correlation between two signals greatly depends on the motor task performance. In this study, we designed the experimental paradigm with four types of motor tasks such as real hand grasping movement (RM), movement intention (Inten), motor imagery (MI) and only looking at virtual hand in three dimensional head mounted display (OL). We aimed to investigate EEG-EMG correlation with linear and nonlinear coupling methods. The results proved that high correlation could be occurred in RM and Inten tasks rather than MI and OL tasks in both linear and nonlinear methods. High coherence occurred in beta and gamma bands of RM and Inten tasks whereas no coherence was detected in MI and OL tasks. In terms of nonlinear correlation, the high mutual information was detected in RM and Inten tasks. There was slight mutual information in MI and OL tasks. The results showed that the coherence in the contralateral brain cortex was higher than in the ipsilateral motor cortex during motor tasks. Furthermore, the amount of EEG-EMG functional coupling changed according to the motor task executed.

RevDate: 2021-12-28
CmpDate: 2021-12-28

Igasaki T, Kuramura Y, J Takemoto (2021)

Motor Imagery, Execution, and Observation Classification using Small Amount of EEG Data with Multiple Two-Class CNNs.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021:443-446.

This study attempted to classify a small amount of electroencephalogram (EEG) data on five states: four tasks involving right index-finger flexion (kinesthetic motor imagery, visual motor imagery, motor execution, and motor observation) and resting with eyes open. We employed a convolutional neural network (CNN) as a classifier and compared the classification accuracies of two types of CNNs: 1) a "single five-class CNN," which classified the aforementioned states with a single CNN and 2) "multiple two-class CNNs," wherein ten CNNs that classify pairs of states were combined. In addition, the classification accuracies were compared between two scenarios: one wherein the EEGs from all 19 scalp probe electrodes (19-channel EEG) were adopted as input data for the CNN, and the other wherein the EEGs of four regions closely related to the motor execution and observation of the index finger (4-channel EEG) were adopted. The classification accuracies of the single five-class CNN with 19- and 4-channel EEGs were 48.2 ± 5.9% and 46.6 ± 6.9%, respectively, and those of the multiple two-class CNNs with 19- and 4-channel EEGs were 52.8 ± 9.7% and 47.5 ± 9.4%, respectively. These results indicate the effectiveness of multiple two-class CNNs that utilize the EEGs of all scalp electrodes as input data for classifying motor imagery, execution, and observation, even in the case of the marginal dataset.

RevDate: 2021-12-28
CmpDate: 2021-12-28

Sime MM, Bissoli ALC, Lavino-Júnior D, et al (2021)

Usability, occupational performance and satisfaction evaluation of a smart environment controlled by infrared oculography by people with severe motor disabilities.

PloS one, 16(8):e0256062.

A smart environment is an assistive technology space that can enable people with motor disabilities to control their equipment (TV, radio, fan, etc.) through a human-machine interface activated by different inputs. However, assistive technology resources are not always considered useful, reaching quite high abandonment rate. This study aims to evaluate the effectiveness of a smart environment controlled through infrared oculography by people with severe motor disabilities. The study sample was composed of six individuals with motor disabilities. Initially, sociodemographic data forms, the Functional Independence Measure (FIMTM), and the Canadian Occupational Performance Measure (COPM) were applied. The participants used the system in their domestic environment for a week. Afterwards, they were reevaluated with regards to occupational performance (COPM), satisfaction with the use of the assistive technology resource (QUEST 2.0), psychosocial impact (PIADS) and usability of the system (SUS), as well as through semi-structured interviews for suggestions or complaints. The most common demand from the participants of this research was 'control of the TV'. Two participants did not use the system. All participants who used the system (four) presented positive results in all assessment protocols, evidencing greater independence in the control of the smart environment equipment. In addition, they evaluated the system as useful and with good usability. Non-acceptance of disability and lack of social support may have influenced the results.

RevDate: 2021-12-28
CmpDate: 2021-12-28

Benjamin EJ, Go AS, Desvigne-Nickens P, et al (2021)

Research Priorities in Atrial Fibrillation Screening: A Report From a National Heart, Lung, and Blood Institute Virtual Workshop.

Circulation, 143(4):372-388.

Clinically recognized atrial fibrillation (AF) is associated with higher risk of complications, including ischemic stroke, cognitive decline, heart failure, myocardial infarction, and death. It is increasingly recognized that AF frequently is undetected until complications such as stroke or heart failure occur. Hence, the public and clinicians have an intense interest in detecting AF earlier. However, the most appropriate strategies to detect undiagnosed AF (sometimes referred to as subclinical AF) and the prognostic and therapeutic implications of AF detected by screening are uncertain. Our report summarizes the National Heart, Lung, and Blood Institute's virtual workshop focused on identifying key research priorities related to AF screening. Global experts reviewed major knowledge gaps and identified critical research priorities in the following areas: (1) role of opportunistic screening; (2) AF as a risk factor, risk marker, or both; (3) relationship between AF burden detected with long-term monitoring and outcomes/treatments; (4) designs of potential randomized trials of systematic AF screening with clinically relevant outcomes; and (5) role of AF screening after ischemic stroke. Our report aims to inform and catalyze AF screening research that will advance innovative, resource-efficient, and clinically relevant studies in diverse populations to improve the diagnosis, management, and prognosis of patients with undiagnosed AF.

RevDate: 2021-12-27

Pitt KM, A Dietz (2021)

Applying Implementation Science to Support Active Collaboration in Noninvasive Brain-Computer Interface Development and Translation for Augmentative and Alternative Communication.

American journal of speech-language pathology [Epub ahead of print].

PURPOSE: The purpose of this article is to consider how, alongside engineering advancements, noninvasive brain-computer interface (BCI) for augmentative and alternative communication (AAC; BCI-AAC) developments can leverage implementation science to increase the clinical impact of this technology. We offer the Consolidated Framework for Implementation Research (CFIR) as a structure to help guide future BCI-AAC research. Specifically, we discuss CFIR primary domains that include intervention characteristics, the outer and inner settings, the individuals involved in the intervention, and the process of implementation, alongside pertinent subdomains including adaptability, cost, patient needs and recourses, implementation climate, other personal attributes, and the process of engaging. The authors support their view with current citations from both the AAC and BCI-AAC fields.

CONCLUSIONS: The article aimed to provide thoughtful considerations for how future research may leverage the CFIR to support meaningful BCI-AAC translation for those with severe physical impairments. We believe that, although significant barriers to BCI-AAC development still exist, incorporating implementation research may be timely for the field of BCI-AAC and help account for diversity in end users, navigate implementation obstacles, and support a smooth and efficient translation of BCI-AAC technology. Moreover, the sooner clinicians, individuals who use AAC, their support networks, and engineers collectively improve BCI-AAC outcomes and the efficiency of translation, the sooner BCI-AAC may become an everyday tool in the AAC arsenal.

RevDate: 2021-12-27

Nojima I, Sugata H, Takeuchi H, et al (2021)

Brain-Computer Interface Training Based on Brain Activity Can Induce Motor Recovery in Patients With Stroke: A Meta-Analysis.

Neurorehabilitation and neural repair [Epub ahead of print].

BACKGROUND: Brain-computer interface (BCI) is a procedure involving brain activity in which neural status is provided to the participants for self-regulation. The current review aims to evaluate the effect sizes of clinical studies investigating the use of BCI-based rehabilitation interventions in restoring upper extremity function and effective methods to detect brain activity for motor recovery.

METHODS: A computerized search of MEDLINE, CENTRAL, Web of Science, and PEDro was performed to identify relevant articles. We selected clinical trials that used BCI-based training for post-stroke patients and provided motor assessment scores before and after the intervention. The pooled standardized mean differences of BCI-based training were calculated using the random-effects model.

RESULTS: We initially identified 655 potentially relevant articles; finally, 16 articles fulfilled the inclusion criteria, involving 382 participants. A significant effect of neurofeedback intervention for the paretic upper limb was observed (standardized mean difference = .48, [.16-.80], P = .006). However, the effect estimates were moderately heterogeneous among the studies (I2 = 45%, P = .03). Subgroup analysis of the method of measurement of brain activity indicated the effectiveness of the algorithm focusing on sensorimotor rhythm.

CONCLUSION: This meta-analysis suggested that BCI-based training was superior to conventional interventions for motor recovery of the upper limbs in patients with stroke. However, the results are not conclusive because of a high risk of bias and a large degree of heterogeneity due to the differences in the BCI interventions and the participants; therefore, further studies involving larger cohorts are required to confirm these results.

RevDate: 2021-12-27

Massetti N, Russo M, Franciotti R, et al (2021)

A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum.

Journal of Alzheimer's disease : JAD pii:JAD210573 [Epub ahead of print].

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.

OBJECTIVE: To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.

METHODS: We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.

RESULTS: The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.

CONCLUSION: Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.

RevDate: 2021-12-27

Rui Z, Z Gu (2021)

A Review of EEG and fMRI Measuring Aesthetic Processing in Visual User Experience Research.

Computational intelligence and neuroscience, 2021:2070209.

In human-computer interaction, the visual interaction of user experience (UX) and user interface (UI) plays an important role in enriching the quality of daily life. The purpose of our study analyzes the use of brain-computer interface (BCI), wearable technology, and functional magnetic resonance imaging (fMRI) to explore the aesthetic processing of visual neural response to UI and UX designs. Specifically, this review aims to understand neuroaesthetic processing knowledge, aesthetic appreciation models, and the ways in which visual brain studies can improve the quality of current and future UI and UX designs. Recent research has found that subjective evaluations of aesthetic appreciation produce different results for objective evaluations of brain research analysis. We applied SWOT analysis and examined the advantages and disadvantages of both evaluation methods. Furthermore, we conducted a traditional literature review on topics pertaining to the use of aesthetic processing knowledge in the visual interaction field in terms of art therapy, information visualization, website or mobile applications, and other interactive platforms. Our main research findings from current studies have helped and motivated researchers and designers to use convincing scientific knowledge of brain event-related potential, electroencephalography, and fMRI to understand aesthetic judgment. The key trend finds that many designers, artists, and engineers use artistic BCI technology in the visual interaction experience. Herein, the scientific methods applied in the aesthetic appreciation to human-computer interface are summarized, and the influence of the latest wearable brain technology on visual interaction design is discussed. Furthermore, current possible research entry points for aesthetics, usability, and creativity in UI and UX designs are explicated. The study results have implications for the visual user experience research domain as well as for interaction industries, which produce interactive projects to improve people's daily lives.

RevDate: 2021-12-27

Mirjalili S, Powell P, Strunk J, et al (2021)

Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography: Abbreviated Title: Evaluating methods of classifying memory states from EEG.

NeuroImage, 247:118851 pii:S1053-8119(21)01122-8 [Epub ahead of print].

Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single trial classification holds potential for translation into the clinical realm for real-time detection of memory and other cognitive states to provide real-time interventions (i.e., brain-computer interfaces). However, most of these studies-and classification analyses in general- do not make clear if the chosen methodology is optimally suited for the classification of memory-related brain states. To address this problem, we systematically compared different methods for every step of classification (i.e., feature extraction, feature selection, classifier selection) to investigate which methods work best for decoding episodic memory brain states-the first analysis of its kind. Using an adult lifespan sample EEG dataset collected during performance of an episodic context encoding and retrieval task, we found that no specific feature type (including Common Spatial Pattern (CSP)-based features, mean, variance, correlation, features based on AR model, entropy, phase, and phase synchronization) outperformed others consistently in distinguishing different memory classes. However, extracting all of these feature types consistently outperformed extracting only one type of feature. Additionally, the combination of filtering and sequential forward selection was the optimal method to select the effective features compared to filtering alone or performing no feature selection at all. Moreover, although all classifiers performed at a fairly similar level, LASSO was consistently the highest performing classifier compared to other commonly used options (i.e., naïve Bayes, SVM, and logistic regression) while naïve Bayes was the fastest classifier. Lastly, for multiclass classification (i.e., levels of context memory confidence and context feature perception), generalizing the binary classification using the binary decision tree performed better than the voting or one versus rest method. These methods were shown to outperform alternative approaches for three orthogonal datasets (i.e., EEG working memory, EEG motor imagery, and MEG working memory), supporting their generalizability. Our results provide an optimized methodological process for classifying single-trial neural data and provide important insight and recommendations for a cognitive neuroscientist's ability to make informed choices at all stages of the classification process for predicting memory and other cognitive states.

RevDate: 2021-12-27

Cheng Z, Wang C, Wei B, et al (2021)

High resolution ultrasonic neural modulation observed via in vivo two-photon calcium imaging.

Brain stimulation, 15(1):190-196 pii:S1935-861X(21)00840-8 [Epub ahead of print].

Neural modulation plays a major role in delineating the circuit mechanisms and serves as the cornerstone of neural interface technologies. Among the various modulation mechanisms, ultrasound enables noninvasive label-free deep access to mammalian brain tissue. To date, most if not all ultrasonic neural modulation implementations are based on ∼1 MHz carrier frequency. The long acoustic wavelength results in a spatially coarse modulation zone, often spanning over multiple function regions. The modulation of one function region is inevitably linked with the modulation of its neighboring regions. Moreover, the lack of in vivo cellular resolution cell-type-specific recording capabilities in most studies prevents the revealing of the genuine cellular response to ultrasound. To significantly increase the spatial resolution, we explored the application of high-frequency ultrasound. To investigate the neuronal response at cellular resolutions, we developed a dual-modality system combining in vivo two-photon calcium imaging and focused ultrasound modulation. The studies show that the ∼30 MHz ultrasound can suppress the neuronal activity in awake mice at 100-μm scale spatial resolutions, paving the way for high-resolution ultrasonic neural modulation. The dual-modality in vivo system validated through this study will serve as a general platform for studying the dynamics of various cell types in response to ultrasound.

RevDate: 2021-12-24

Wang K, Zhai DH, Xiong Y, et al (2021)

An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control.

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

This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA), an MVMD-CCA algorithm is investigated to improve the detection ability of SSVEP electroencephalogram (EEG) signals. In comparison with the classical filter bank canonical correlation analysis (FBCCA), the nonlinear and non-stationary EEG signals are decomposed into a fixed number of sub-bands by MVMD, which can enhance the effect of SSVEP-related sub-bands. The experimental results show that MVMD-CCA can effectively reduce the influence of noise and EEG artifacts and improve the performance of SSVEP-based BCI. The offline experiments show that the average accuracies of MVMD-CCA in the training dataset and testing dataset are improved by 3.08% and 1.67%, respectively. In the SSVEP-based online robotic manipulator grasping experiment, the recognition accuracies of the four subjects are 92.5%, 93.33%, 90.83%, and 91.67%, respectively.

RevDate: 2021-12-25

Woeppel K, Hughes C, Herrera AJ, et al (2021)

Explant Analysis of Utah Electrode Arrays Implanted in Human Cortex for Brain-Computer-Interfaces.

Frontiers in bioengineering and biotechnology, 9:759711.

Brain-computer interfaces are being developed to restore movement for people living with paralysis due to injury or disease. Although the therapeutic potential is great, long-term stability of the interface is critical for widespread clinical implementation. While many factors can affect recording and stimulation performance including electrode material stability and host tissue reaction, these factors have not been investigated in human implants. In this clinical study, we sought to characterize the material integrity and biological tissue encapsulation via explant analysis in an effort to identify factors that influence electrophysiological performance. We examined a total of six Utah arrays explanted from two human participants involved in intracortical BCI studies. Two platinum (Pt) arrays were implanted for 980 days in one participant (P1) and two Pt and two iridium oxide (IrOx) arrays were implanted for 182 days in the second participant (P2). We observed that the recording quality followed a similar trend in all six arrays with an initial increase in peak-to-peak voltage during the first 30-40 days and gradual decline thereafter in P1. Using optical and two-photon microscopy we observed a higher degree of tissue encapsulation on both arrays implanted for longer durations in participant P1. We then used scanning electron microscopy and energy dispersive X-ray spectroscopy to assess material degradation. All measures of material degradation for the Pt arrays were found to be more prominent in the participant with a longer implantation time. Two IrOx arrays were subjected to brief survey stimulations, and one of these arrays showed loss of iridium from most of the stimulated sites. Recording performance appeared to be unaffected by this loss of iridium, suggesting that the adhesion of IrOx coating may have been compromised by the stimulation, but the metal layer did not detach until or after array removal. In summary, both tissue encapsulation and material degradation were more pronounced in the arrays that were implanted for a longer duration. Additionally, these arrays also had lower signal amplitude and impedance. New biomaterial strategies that minimize fibrotic encapsulation and enhance material stability should be developed to achieve high quality recording and stimulation for longer implantation periods.

RevDate: 2021-12-25

Chen H, Jin M, Li Z, et al (2021)

MS-MDA: Multisource Marginal Distribution Adaptation for Cross-Subject and Cross-Session EEG Emotion Recognition.

Frontiers in neuroscience, 15:778488.

As an essential element for the diagnosis and rehabilitation of psychiatric disorders, the electroencephalogram (EEG) based emotion recognition has achieved significant progress due to its high precision and reliability. However, one obstacle to practicality lies in the variability between subjects and sessions. Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation. We therefore propose the multi-source marginal distribution adaptation (MS-MDA) for EEG emotion recognition, which takes both domain-invariant and domain-specific features into consideration. First, we assume that different EEG data share the same low-level features, then we construct independent branches for multiple EEG data source domains to adopt one-to-one domain adaptation and extract domain-specific features. Finally, the inference is made by multiple branches. We evaluate our method on SEED and SEED-IV for recognizing three and four emotions, respectively. Experimental results show that the MS-MDA outperforms the comparison methods and state-of-the-art models in cross-session and cross-subject transfer scenarios in our settings. Codes at https://github.com/VoiceBeer/MS-MDA.

RevDate: 2021-12-25

Arlotti M, Colombo M, Bonfanti A, et al (2021)

A New Implantable Closed-Loop Clinical Neural Interface: First Application in Parkinson's Disease.

Frontiers in neuroscience, 15:763235.

Deep brain stimulation (DBS) is used for the treatment of movement disorders, including Parkinson's disease, dystonia, and essential tremor, and has shown clinical benefits in other brain disorders. A natural path for the improvement of this technique is to continuously observe the stimulation effects on patient symptoms and neurophysiological markers. This requires the evolution of conventional deep brain stimulators to bidirectional interfaces, able to record, process, store, and wirelessly communicate neural signals in a robust and reliable fashion. Here, we present the architecture, design, and first use of an implantable stimulation and sensing interface (AlphaDBSR System) characterized by artifact-free recording and distributed data management protocols. Its application in three patients with Parkinson's disease (clinical trial n. NCT04681534) is shown as a proof of functioning of a clinically viable implanted brain-computer interface (BCI) for adaptive DBS. Reliable artifact free-recordings, and chronic long-term data and neural signal management are in place.

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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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