Reinforcement Learning May Demystify the Limited Human Motor Learning Efficacy Due to Visual-Proprioceptive Mismatch

Vision and proprioception have fundamental sensory mismatches in delivering locational information, and such mismatches are critical factors limiting the efficacy of motor learning. However, it is still not clear how and to what extent this mismatch limits motor learning outcomes. To further the understanding of the effect of sensory mismatch on motor learning outcomes, a reinforcement learning algorithm and the simplified biomechanical elbow joint model were employed to mimic the motor learning process in a computational environment. By applying a reinforcement learning algorithm to the motor learning of elbow joint flexion task, simulation results successfully explained how visual-proprioceptive mismatch limits motor learning outcomes in terms of motor control accuracy and task completion speed. The larger the perceived angular offset between the two sensory modalities, the lower the motor control accuracy. Also, the more similar the peak reward amplitude of the two sensory modalities, the lower the motor control accuracy. In addition, simulation results suggest that insufficient exploration rate limits task completion speed, and excessive exploration rate limits motor control accuracy. Such a speed-accuracy trade-off shows that a moderate exploration rate could serve as another important factor in motor learning.

sEMG-Based Inter-Session Hand Gesture Recognition via Domain Adaptation with Locality Preserving and Maximum Margin

Surface electromyography (sEMG)-based gesture recognition can achieve high intra-session performance. However, the inter-session performance of gesture recognition decreases sharply due to the shift in data distribution. Therefore, developing a robust model to minimize the data distribution difference is crucial to improving the user experience. In this work, based on the inter-session gesture recognition task, we propose a novel algorithm called locality preserving and maximum margin criterion (LPMM). The LPMM algorithm integrates three main modules, including domain alignment, pseudo-label selection, and iteration result selection. Domain alignment is designed to preserve the neighborhood structure of the feature and minimize the overlap of different classes. The pseudo-label selection and iteration result selection can avoid the decrease in accuracy caused by mislabeled samples. The proposed algorithm was evaluated on two of the most widely used EMG databases. It achieves a mean accuracy of 98.46% and 71.64%, respectively, which is superior to state-of-the-art domain adaptation methods.

A Few-Shot Transfer Learning Approach for Motion Intention Decoding from Electroencephalographic Signals

In this study, a few-shot transfer learning approach was introduced to decode movement intention from electroencephalographic (EEG) signals, allowing to recognize new tasks with minimal adaptation. To this end, a dataset of EEG signals recorded during the preparation of complex sub-movements was created from a publicly available data collection. The dataset was divided into two parts: the source domain dataset (including 5 classes) and the support (target domain) dataset, (including 2 classes) with no overlap between the two datasets in terms of classes. The proposed methodology consists in projecting EEG signals into the space-frequency-time domain, in processing such projections (rearranged in channels × frequency frames) by means of a custom EEG-based deep neural network (denoted as EEGframeNET5), and then adapting the system to recognize new tasks through a few-shot transfer learning approach. The proposed method achieved an average accuracy of 72.45 ± 4.19% in the 5-way classification of samples from the source domain dataset, outperforming comparable studies in the literature. In the second phase of the study, a few-shot transfer learning approach was proposed to adapt the neural system and make it able to recognize new tasks in the support dataset. The results demonstrated the system's ability to adapt and recognize new tasks with an average accuracy of 80 ± 0.12% in discriminating hand opening/closing preparation and outperforming reported results in the literature. This study suggests the effectiveness of EEG in capturing information related to the motor preparation of complex movements, potentially paving the way for BCI systems based on motion planning decoding. The proposed methodology could be straightforwardly extended to advanced EEG signal processing in other scenarios, such as motor imagery or neural disorder classification.

Self-Supervised Learning for Electroencephalography

Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques. However, even the most advanced machine learning techniques require relatively large, labeled EEG repositories. EEG data collection and labeling are costly. Moreover, combining available datasets to achieve a large data volume is usually infeasible due to inconsistent experimental paradigms across trials. Self-supervised learning (SSL) solves these challenges because it enables learning from EEG records across trials with variable experimental paradigms, even when the trials explore different phenomena. It aggregates multiple EEG repositories to increase accuracy, reduce bias, and mitigate overfitting in machine learning training. In addition, SSL could be employed in situations where there is limited labeled training data, and manual labeling is costly. This article: 1) provides a brief introduction to SSL; 2) describes some SSL techniques employed in recent studies, including EEG; 3) proposes current and potential SSL techniques for future investigations in EEG studies; 4) discusses the cons and pros of different SSL techniques; and 5) proposes holistic implementation tips and potential future directions for EEG SSL practices.

Unsupervised Neural Manifold Alignment for Stable Decoding of Movement from Cortical Signals

The stable decoding of movement parameters using neural activity is crucial for the success of brain-machine interfaces (BMIs). However, neural activity can be unstable over time, leading to changes in the parameters used for decoding movement, which can hinder accurate movement decoding. To tackle this issue, one approach is to transfer neural activity to a stable, low-dimensional manifold using dimensionality reduction techniques and align manifolds across sessions by maximizing correlations of the manifolds. However, the practical use of manifold stabilization techniques requires knowledge of the true subject intentions such as target direction or behavioral state. To overcome this limitation, an automatic unsupervised algorithm is proposed that determines movement target intention before manifold alignment in the presence of manifold rotation and scaling across sessions. This unsupervised algorithm is combined with a dimensionality reduction and alignment method to overcome decoder instabilities. The effectiveness of the BMI stabilizer method is represented by decoding the two-dimensional (2D) hand velocity of two rhesus macaque monkeys during a center-out-reaching movement task. The performance of the proposed method is evaluated using correlation coefficient and R-squared measures, demonstrating higher decoding performance compared to a state-of-the-art unsupervised BMI stabilizer. The results offer benefits for the automatic determination of movement intents in long-term BMI decoding. Overall, the proposed method offers a promising automatic solution for achieving stable and accurate movement decoding in BMI applications.

New approaches to recovery after stroke

After a stroke, several mechanisms of neural plasticity can be activated, which may lead to significant recovery. Rehabilitation therapies aim to restore surviving tissue over time and reorganize neural connections. With more patients surviving stroke with varying degrees of neurological impairment, new technologies have emerged as a promising option for better functional outcomes. This review explores restorative therapies based on brain-computer interfaces, robot-assisted and virtual reality, brain stimulation, and cell therapies. Brain-computer interfaces allow for the translation of brain signals into motor patterns. Robot-assisted and virtual reality therapies provide interactive interfaces that simulate real-life situations and physical support to compensate for lost motor function. Brain stimulation can modify the electrical activity of neurons in the affected cortex. Cell therapy may promote regeneration in damaged brain tissue. Taken together, these new approaches could substantially benefit specific deficits such as arm-motor control and cognitive impairment after stroke, and even the chronic phase of recovery, where traditional rehabilitation methods may be limited, and the window for repair is narrow.

Eye State Detection Using Frequency Features from 1 or 2-Channel EEG

Brain-computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states.

Ethics Along the Continuum of Research Involving Persons with Disorders of Consciousness

Interest in disorders of consciousness (DoC) has grown substantially over the past decade and has illuminated the importance of improving understanding of DoC biology; care needs (use of monitoring, performance of interventions, and provision of emotional support); treatment options to promote recovery; and outcome prediction. Exploration of these topics requires awareness of numerous ethics considerations related to rights and resources. The Curing Coma Campaign Ethics Working Group used its expertise in neurocritical care, neuropalliative care, neuroethics, neuroscience, philosophy, and research to formulate an informal review of ethics considerations along the continuum of research involving persons with DoC related to the following: (1) study design; (2) comparison of risks versus benefits; (3) selection of inclusion and exclusion criteria; (4) screening, recruitment, and enrollment; (5) consent; (6) data protection; (7) disclosure of results to surrogates and/or legally authorized representatives; (8) translation of research into practice; (9) identification and management of conflicts of interest; (10) equity and resource availability; and (11) inclusion of minors with DoC in research. Awareness of these ethics considerations when planning and performing research involving persons with DoC will ensure that the participant rights are respected while maximizing the impact and meaningfulness of the research, interpretation of outcomes, and communication of results.

An Integrated Neurorobotics Model of the Cerebellar-Basal Ganglia Circuitry

This work presents a neurorobotics model of the brain that integrates the cerebellum and the basal ganglia regions to coordinate movements in a humanoid robot. This cerebellar-basal ganglia circuitry is well known for its relevance to the motor control used by most mammals. Other computational models have been designed for similar applications in the robotics field. However, most of them completely ignore the interplay between neurons from the basal ganglia and cerebellum. Recently, neuroscientists indicated that neurons from both regions communicate not only at the level of the cerebral cortex but also at the subcortical level. In this work, we built an integrated neurorobotics model to assess the capacity of the network to predict and adjust the motion of the hands of a robot in real time. Our model was capable of performing different movements in a humanoid robot by respecting the sensorimotor loop of the robot and the biophysical features of the neuronal circuitry. The experiments were executed in simulation and the real world. We believe that our proposed neurorobotics model can be an important tool for new studies on the brain and a reference toward new robot motor controllers.

Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task

Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.

Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network

Accurate identification of driver's drowsiness state through Electroencephalogram (EEG) signals can effectively reduce traffic accidents, but EEG signals are usually stored in various clients in the form of small samples. This study attempts to construct an efficient and accurate privacy-preserving drowsiness monitoring system, and proposes a fusion model based on tree Federated Learning (FL) and Convolutional Neural Network (CNN), which can not only identify and explain the driver's drowsiness state, but also integrate the information of different clients under the premise of privacy protection. Each client uses CNN with the Global Average Pooling (GAP) layer and shares model parameters. The tree FL transforms communication relationships into a graph structure, and model parameters are transmitted in parallel along connected branches of the graph. Moreover, the Class Activation Mapping (CAM) is used to find distinctive EEG features for representing specific classes. On EEG data of 11 subjects, it is found that this method has higher average accuracy, F1-score and AUC than the traditional classification method, reaching 73.56%, 73.26% and 78.23%, respectively. Compared with the traditional FL algorithm, this method better protects the driver's privacy and improves communication efficiency.

Impact of Virtual Reality Cognitive and Motor Exercises on Brain Health

Innovative technologies of the 21st century have an extremely significant impact on all activities of modern humans. Among them, virtual reality (VR) offers great opportunities for scientific research and public health. The results of research to date both demonstrate the beneficial effects of using virtual worlds, and indicate undesirable effects on bodily functions. This review presents interesting recent findings related to training/exercise in virtual environments and its impact on cognitive and motor functions. It also highlights the importance of VR as an effective tool for assessing and diagnosing these functions both in research and modern medical practice. The findings point to the enormous future potential of these rapidly developing innovative technologies. Of particular importance are applications of virtual reality in basic and clinical neuroscience.

Deep Learning Methods for Multi-Channel EEG-Based Emotion Recognition

Currently, Fourier-based, wavelet-based, and Hilbert-based time–frequency techniques have generated considerable interest in classification studies for emotion recognition in human–computer interface investigations. Empirical mode decomposition (EMD), one of the Hilbert-based time–frequency techniques, has been developed as a tool for adaptive signal processing. Additionally, the multi-variate version strongly influences designing the common oscillation structure of a multi-channel signal by utilizing the common instantaneous concepts of frequency and bandwidth. Additionally, electroencephalographic (EEG) signals are strongly preferred for comprehending emotion recognition perspectives in human–machine interactions. This study aims to herald an emotion detection design via EEG signal decomposition using multi-variate empirical mode decomposition (MEMD). For emotion recognition, the SJTU emotion EEG dataset (SEED) is classified using deep learning methods. Convolutional neural networks (AlexNet, DenseNet-201, ResNet-101, and ResNet50) and AutoKeras architectures are selected for image classification. The proposed framework reaches 99% and 100% classification accuracy when transfer learning methods and the AutoKeras method are used, respectively.

Analysis of fMRI Signals from Working Memory Tasks and Resting-State of Brain: Neutrosophic-Entropy-Based Clustering Algorithm

This study applies a neutrosophic-entropy-based clustering algorithm (NEBCA) to analyze the fMRI signals. We consider the data obtained from four different working memory tasks and the brain's resting state for the experimental purpose. Three non-overlapping clusters of data related to temporal brain activity are determined and statistically analyzed. Moreover, we used the Uniform Manifold Approximation and Projection (UMAP) method to reduce system dimensionality and present the effectiveness of NEBCA. The results show that using NEBCA, we are able to distinguish between different working memory tasks and resting-state and identify subtle differences in the related activity of brain regions. By analyzing the statistical properties of the entropy inside the clusters, the various regions of interest (ROIs), according to Automated Anatomical Labeling (AAL) atlas crucial for clustering procedure, are determined. The inferior occipital gyrus is established as an important brain region in distinguishing the resting state from the tasks. Moreover, the inferior occipital gyrus and superior parietal lobule are identified as necessary to correct the data discrimination related to the different memory tasks. We verified the statistical significance of the results through the two-sample t-test and analysis of surrogates performed by randomization of the cluster elements. The presented methodology is also appropriate to determine the influence of time of day on brain activity patterns. The differences between working memory tasks and resting-state in the morning are related to a lower index of small-worldness and sleep inertia in the first hours after waking. We also compared the performance of NEBCA to two existing algorithms, KMCA and FKMCA. We showed the advantage of the NEBCA over these algorithms that could not effectively accumulate fMRI signals with higher variability.

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

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.

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

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.

Neurodegenerative disorders management: state-of-art and prospects of nano-biotechnology

Neurodegenerative disorders (NDs) are highly prevalent among the aging population. It affects primarily the central nervous system (CNS) but the effects are also observed in the peripheral nervous system. Neural degeneration is a progressive loss of structure and function of neurons, which may ultimately involve cell death. Such patients suffer from debilitating memory loss and altered motor coordination which bring up non-affordable and unavoidable socio-economic burdens. Due to the unavailability of specific therapeutics and diagnostics, the necessity to control or manage NDs raised the demand to investigate and develop efficient alternative approaches. Keeping trends and advancements in view, this report describes both state-of-the-art and challenges in nano-biotechnology-based approaches to manage NDs, toward personalized healthcare management. Sincere efforts are being made to customize nano-theragnostics to control: therapeutic cargo packaging, delivery to the brain, nanomedicine of higher efficacy, deep brain stimulation, implanted stimulation, and managing brain cell functioning. These advancements are useful to design future therapy based on the severity of the patient's neurodegenerative disease. However, we observe a lack of knowledge shared among scientists of a variety of expertise to explore this multi-disciplinary research field for NDs management. Consequently, this review will provide a guideline platform that will be useful in developing novel smart nano-therapies by considering the aspects and advantages of nano-biotechnology to manage NDs in a personalized manner. Nano-biotechnology-based approaches have been proposed as effective and affordable alternatives at the clinical level due to recent advancements in nanotechnology-assisted theragnostics, targeted delivery, higher efficacy, and minimal side effects.

Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform

Patients with motor impairments need caregivers' help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, [Formula: see text], less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.

Galvanic Vestibular Stimulation-Based Prediction Error Decoding and Channel Optimization

A significant problem in brain-computer interface (BCI) research is decoding - obtaining required information from very weak noisy electroencephalograph signals and extracting considerable information from limited data. Traditional intention decoding methods, which obtain information from induced or spontaneous brain activity, have shortcomings in terms of performance, computational expense and usage burden. Here, a new methodology called prediction error decoding was used for motor imagery (MI) detection and compared with direct intention decoding. Galvanic vestibular stimulation (GVS) was used to induce subliminal sensory feedback between the forehead and mastoids without any burden. Prediction errors were generated between the GVS-induced sensory feedback and the MI direction. The corresponding prediction error decoding of the front/back MI task was validated. A test decoding accuracy of 77.83-78.86% (median) was achieved during GVS for every 100[Formula: see text]ms interval. A nonzero weight parameter-based channel screening (WPS) method was proposed to select channels individually and commonly during GVS. When the WPS common-selected mode was compared with the WPS individual-selected mode and a classical channel selection method based on correlation coefficients (CCS), a satisfactory decoding performance of the selected channels was observed. The results indicated the positive impact of measuring common specific channels of the BCI.

A Hybrid-Domain Deep Learning-Based BCI For Discriminating Hand Motion Planning From EEG Sources

In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to decode hand movement preparation phases from electroencephalographic (EEG) recordings. The system exploits information extracted from the temporal-domain and time-frequency-domain, as part of a hybrid strategy, to discriminate the temporal windows (i.e. EEG epochs) preceding hand sub-movements (open/close) and the resting state. To this end, for each EEG epoch, the associated cortical source signals in the motor cortex and the corresponding time-frequency (TF) maps are estimated via beamforming and Continuous Wavelet Transform (CWT), respectively. Two Convolutional Neural Networks (CNNs) are designed: specifically, the first CNN is trained over a dataset of temporal (T) data (i.e. EEG sources), and is referred to as T-CNN; the second CNN is trained over a dataset of TF data (i.e. TF-maps of EEG sources), and is referred to as TF-CNN. Two sets of features denoted as T-features and TF-features, extracted from T-CNN and TF-CNN, respectively, are concatenated in a single features vector (denoted as TTF-features vector) which is used as input to a standard multi-layer perceptron for classification purposes. Experimental results show a significant performance improvement of our proposed hybrid-domain DL approach as compared to temporal-only and time-frequency-only-based benchmark approaches, achieving an average accuracy of [Formula: see text]%.

Regenerative Peripheral Nerve Interfaces for Advanced Control of Upper Extremity Prosthetic Devices

The quest to find the ideal prosthetic device interface that enables intuitive control has motivated several recent innovations. Although current prosthetic device control strategies have advanced the field of neuroprosthetic control, they are limited in their ability to generate reliable, stable, and specific signals to replicate the complex movements of the upper extremity. The regenerative peripheral nerve interface (RPNI) is a promising solution to enhance prosthetic device control. This article describes the development of RPNIs and summarizes its successful use in the control of advanced prosthetic devices in patients with upper extremity amputations.

Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI

The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain-computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm. Specifically, we use the minimum covariance determinant (MCD) to detect and remove outliers in the dataset, then we use the Fisher score to evaluate and select features. In addition, in order to prevent the emergence of new outliers, we propose an iterative minimum covariance determinant (IMCD) algorithm. We evaluate our proposed algorithm in terms of iteration times, classification accuracy and feature distribution using two BCI competition datasets. The experimental results show that the average classification performance of our proposed method is 12% and 22.9% higher than that of the traditional CSP method in two datasets ([Formula: see text]), and our proposed method obtains better performance in comparison with other competing methods. The results show that our method improves the performance of MI-BCI systems.

Decision-Feedback Stages Revealed by Hidden Markov Modeling of EEG

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

Decoding Saccade Intention From Primate Prefrontal Cortical Local Field Potentials Using Spectral, Spatial, and Temporal Dimensionality Reduction

Most invasive Brain Computer Interfaces (iBCIs) use spike and Local Field Potentials (LFPs) from the motor or parietal cortices to decode movement intentions. It has been debated whether harvesting signals from other brain areas that encode global cognitive variables, such as the allocation of attention and eye movement goals in a variety of spatial reference frames, may improve the outcome of iBCIs. Here, we explore the ability of LFP signals, sampled from the lateral prefrontal cortex (LPFC) of macaque monkeys, to encode eye-movement intention during the pre-movement fixation period of a delayed saccade task. We use spectral dimensionality reduction to examine the spatiotemporal properties of the extracted non-rhythmic broadband activity and explore its usefulness in decoding saccade goals. The dynamics of the broadband signal in low spatial dimensions across the pre-movement fixation period uncovered saccade target separation; its discriminative potential was confirmed using support vector machine classifications. These findings reveal that broadband LFP from the LPFC can be used to decode intended saccade target location during pre-movement periods. We further provide a general workflow that can be implemented in iBCIs and it is relatively robust to the loss of spikes in individual electrodes.

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain-Machine Interface (BMI)

Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain-machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy. The possible answer to these design questions may lie in neuroergonomics coupled with BMI systems. In this study, two human factors are addressed: designing a lightweight wearable robotic exoskeleton hand that is used to assist the potential stroke patients with an integrated portable brain interface using mental workload (MWL) signals acquired with portable functional near-infrared spectroscopy (fNIRS) system. The system may generate command signals for operating a wearable robotic exoskeleton hand using two-state MWL signals. The fNIRS system is used to record optical signals in the form of change in concentration of oxy and deoxygenated hemoglobin (HbO and HbR) from the pre-frontal cortex (PFC) region of the brain. Fifteen participants participated in this study and were given hand-grasping tasks. Two-state MWL signals acquired from the PFC region of the participant's brain are segregated using machine learning classifier-support vector machines (SVM) to utilize in operating a robotic exoskeleton hand. The maximum classification accuracy is 91.31%, using a combination of mean-slope features with an average information transfer rate (ITR) of 1.43. These results show the feasibility of a two-state MWL (fNIRS-based) robotic exoskeleton hand (BMI system) for hemiplegic patients assisting in the physical grasping tasks.

Discrimination Improvement Through Undesirable Feedback in Coupling Object Manipulation Tasks

Subjective effort can significantly affect the ability of humans to act optimally in dynamic manipulation tasks. In a previous study, we designed a complex object coupling manipulation task that required tight performance and induced high cognitive workload. We hypothesize that strong-effort-related physiological reactivity during the dynamic manipulation task improves the user performance in an undesired task feedback situation. To test this hypothesis, using the motor intentions' discrimination from electroencephalogram (EEG) measurements, we evaluate the effort expended by 20 participants in a controlling task with constraints involving complex coupling objects. Specifically, the finer motor decisions are obtained from the controlling information in EEG by using two fingers from the same hand rather than two hands. The motor intention is decoded from a task-dependent EEG through a regularized discriminant analysis, and the area under the curve is [Formula: see text]. Furthermore, we compare the undesired and desired task feedback conditions along with the individual's effort dynamic adjustment, and investigate whether the undesired task feedback improved the discrimination of the motor activities. A stronger effort to attain the desired feedback state corresponds to improved motor activity discrimination from the EEG in the undesired task feedback scenario. The differences in the brain activities under the undesired and desired task feedback conditions are analyzed using brain-network-based topographical scalp maps. Our experiment provides preliminary evidence that inducing strong effort can improve discrimination performance during highly demanding tasks. This finding can advance our understanding of human attention, potentially improve the accuracy of intention recognition, and may inspire better EEG acquisition contexts.

Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information

A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.

The Influence of Visual Attention on The Performance of A Novel Tactile P300 Brain-Computer Interface with Cheeks-Stim Paradigm

Tactile P300 brain-computer interface (BCI) generally has a worse accuracy and information transfer rate (ITR) than the visual-based BCI. It may be due to the fact that human beings have a relatively poor tactile perception. This study investigated the influence of visual attention on the performance of a tactile P300 BCI. We designed our paradigms based on a novel cheeks-stim paradigm which attached the stimulators on the subject's cheeks. Two paradigms were designed as follows: a paradigm with no visual attention and another paradigm with visual attention to the target position. Eleven subjects were invited to perform the two paradigms. We also recorded and analyzed the eyeball movement data during the paradigm with visual attention to explore whether the eyeball movement would have an effect on the BCI classification. The average online accuracy was 89.09% for the paradigm with visual attention, which was significantly higher than that of the paradigm with no visual attention (70.45%). Significant difference in ITR was also found between the two paradigms ([Formula: see text]). The results demonstrated that visual attention was an effective method to improve the performance of tactile P300 BCI. Our findings suggested that it may be feasible to complete an efficient tactile BCI system by adding visual attention.

Nonspecific Visuospatial Imagery as a Novel Mental Task for Online EEG-Based BCI Control

Brain-computer interfaces (BCIs) can provide a means of communication to individuals with severe motor disorders, such as those presenting as locked-in. Many BCI paradigms rely on motor neural pathways, which are often impaired in these individuals. However, recent findings suggest that visuospatial function may remain intact. This study aimed to determine whether visuospatial imagery, a previously unexplored task, could be used to signify intent in an online electroencephalography (EEG)-based BCI. Eighteen typically developed participants imagined checkerboard arrow stimuli in four quadrants of the visual field in 5-s trials, while signals were collected using 16 dry electrodes over the visual cortex. In online blocks, participants received graded visual feedback based on their performance. An initial BCI pipeline (visuospatial imagery classifier I) attained a mean accuracy of [Formula: see text]% classifying rest against visuospatial imagery in online trials. This BCI pipeline was further improved using restriction to alpha band features (visuospatial imagery classifier II), resulting in a mean pseudo-online accuracy of [Formula: see text]%. Accuracies exceeded the threshold for practical BCIs in 12 participants. This study supports the use of visuospatial imagery as a real-time, binary EEG-BCI control paradigm.

A Training Data-Driven Canonical Correlation Analysis Algorithm for Designing Spatial Filters to Enhance Performance of SSVEP-Based BCIs

Canonical correlation analysis (CCA) is an effective spatial filtering algorithm widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). In existing CCA methods, training data are used for constructing templates of stimulus targets and the spatial filters are created between the template signals and a single-trial testing signal. The fact that spatial filters rely on testing data, however, results in low classification performance of CCA compared to other state-of-the-art algorithms such as task-related component analysis (TRCA). In this study, we proposed a novel CCA method in which spatial filters are estimated using training data only. This is achieved by using observed EEG training data and their SSVEP components as the two inputs of CCA and the objective function is optimized by averaging multiple training trials. In this case, we proved in theory that the two spatial filters estimated by the CCA are equivalent, and that the CCA and TRCA are also equivalent under certain hypotheses. A benchmark SSVEP data set from 35 subjects was used to compare the performance of the two algorithms according to different lengths of data, numbers of channels and numbers of training trials. In addition, the CCA was also compared with power spectral density analysis (PSDA). The experimental results suggest that the CCA is equivalent to TRCA if the signal-to-noise ratio of training data is high enough; otherwise, the CCA outperforms TRCA in terms of classification accuracy. The CCA is much faster than PSDA in detecting time of targets. The robustness of the training data-driven CCA to noise gives it greater potential in practical applications.

Olfactory EEG Signal Classification Using a Trapezoid Difference-Based Electrode Sequence Hashing Approach

Olfactory-induced electroencephalogram (EEG) signal classification is of great significance in a variety of fields, such as disorder treatment, neuroscience research, multimedia applications and brain-computer interface. In this paper, a trapezoid difference-based electrode sequence hashing method is proposed for olfactory EEG signal classification. First, an N-layer trapezoid feature set whose size ratio of the top, bottom and height is 1:2:1 is constructed for each frequency band of each EEG sample. This construction is based on N optimized power-spectral-density features extracted from N real electrodes and N nonreal electrode's features. Subsequently, the N real electrodes' sequence (ES) codes of each layer of the constructed trapezoid feature set are obtained by arranging the feature values in ascending order. Finally, the nearest neighbor classification is used to find a class whose ES codes are the most similar to those of the testing sample. Thirteen-class olfactory EEG signals collected from 11 subjects are used to compare the classification performance of the proposed method with six traditional classification methods. The comparison shows that the proposed method gives average accuracy of 94.3%, Cohen's kappa value of 0.94, precision of 95.0%, and F1-measure of 94.6%, which are higher than those of the existing methods.

A 36-Class Bimodal ERP Brain-Computer Interface Using Location-Congruent Auditory-Tactile Stimuli

To date, traditional visual-based event-related potential brain-computer interface (ERP-BCI) systems continue to dominate the mainstream BCI research. However, these conventional BCIs are unsuitable for the individuals who have partly or completely lost their vision. Considering the poor performance of gaze independent ERP-BCIs, it is necessary to study techniques to improve the performance of these BCI systems. In this paper, we developed a novel 36-class bimodal ERP-BCI system based on tactile and auditory stimuli, in which six-virtual-direction audio files produced via head related transfer functions (HRTF) were delivered through headphones and location-congruent electro-tactile stimuli were simultaneously delivered to the corresponding position using electrodes placed on the abdomen and waist. We selected the eight best channels, trained a Bayesian linear discriminant analysis (BLDA) classifier and acquired the optimal trial number for target selection in online process. The average online information transfer rate (ITR) of the bimodal ERP-BCI reached 11.66 bit/min, improvements of 35.11% and 36.69% compared to the auditory (8.63 bit/min) and tactile approaches (8.53 bit/min), respectively. The results demonstrate the performance of the bimodal system is superior to each unimodal system. These facts indicate that the proposed bimodal system has potential utility as a gaze-independent BCI in future real-world applications.

A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold

Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-validation and an adaptive threshold (CV-T-TAB), to reduce the amount of data required for training by selecting and combining multiple subjects' classifiers that perform well on a new subject to train a classifier. This method adopts cross-validation to extend the amount of the new subject's training data and sets an adaptive threshold to select the optimal combination of the classifiers. Twenty-five subjects participated in the N200- and P300-based brain-computer interface. The study compares CV-T-TAB to five traditional training methods by testing them on the training of a support vector machine. The accuracy, information transfer rate, area under the curve, recall and precision are used to evaluate the performances under nine conditions with different amounts of data. CV-T-TAB outperforms the other methods and retains a high accuracy even when the amount of data is reduced to one-third of the original amount. The results imply that CV-T-TAB is effective in improving the performance of a subject-specific classifier with a small amount of data by adopting multiple subjects' classifiers, which reduces the training cost.

Tangent Space Features-Based Transfer Learning Classification Model for Two-Class Motor Imagery Brain–Computer Interface

The performance of a brain–computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier’s generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibration time in BCI systems by proposing a transfer learning model which can be used for evaluating unseen single trials for a subject without the need for training session data. A method is proposed which combines a generalization of the previously proposed subject-specific “multivariate empirical-mode decomposition” preprocessing technique by taking a fixed band of 8–30[Formula: see text]Hz for all four motor imagery tasks and a novel classification model which exploits the structure of tangent space features drawn from the Riemannian geometry framework, that is shared among the training data of multiple sessions and subjects. Results demonstrate comparable performance improvement across multiple subjects without subject-specific calibration, when compared with other state-of-the-art techniques.

Prediction of Forelimb EMGs and Movement Phases from Corticospinal Signals in the Rat During the Reach-to-Pull Task

Brain-computer interfaces access the volitional command signals from various brain areas in order to substitute for the motor functions lost due to spinal cord injury or disease. As the final common pathway of the central nervous system (CNS) outputs, the descending tracts of the spinal cord offer an alternative site to extract movement-related command signals. Using flexible 2D microelectrode arrays, we have recorded the corticospinal tract (CST) signals in rats during a reach-to-pull task. The CST activity was then classified by the forelimb movement phases into two or three classes in a training dataset and cross validated in a test set. The average classification accuracies were [Formula: see text] (min: [Formula: see text] to max: [Formula: see text]) and [Formula: see text] (min: 43% to max: 71%) for two-class and three-class cases, respectively. The forelimb flexor and extensor EMG envelopes were also predicted from the CST signals using linear regression. The average correlation coefficient between the actual and predicted EMG signals was [Formula: see text] [Formula: see text], whereas the highest correlation was 0.81 for the biceps EMG. Although the forelimb motor function cannot be explained completely by the CST activity alone, the success rates obtained in reconstructing the EMG signals support the feasibility of a spinal-cord-computer interface as a concept.

A Method for Suppressing Electrical Stimulation Artifacts from Electromyography

When surface electromyography (EMG) signal is used in a real-time functional electrical stimulation (FES) system for feedback control, the artifact from electrical stimulation is a key challenge for EMG signal processing. To address this challenge, this study proposes a novel method to suppress stimulation artifacts in the EMG-driven closed-loop FES system. The proposed method is inspired by an experimental study that compares artifacts generated by electrical stimulations with different current intensities. It is found that (1) spikes of stimulation artifacts are susceptible to the current intensity and (2) tailing components are similar under different current intensities. Based on these observations, the proposed method combines the blanking and template subtracting strategies for suppressing stimulation artifact. The length of blanking window for suppressing the stimulation spike is adaptively determined by a spike detection algorithm and the first-order derivative analysis of signal. An autoregressive model is used to estimate the tailing part of stimulation artifact, which is an adaptive template for subtracting the artifact. The proposed method is evaluated on both semi-synthetic and experimental datasets. Verified on the semi-synthetic dataset, the proposed method achieves better performance than the classic blanking method. Validated on the experimental dataset, the proposed method substantially decreases the power of stimulation artifact in the EMG. These results indicate that the proposed method can effectively suppress the stimulation artifact while retains the useful EMG signal for an EMG-driven FES system.

Identifying Suitable Brain Regions and Trial Size Segmentation for Positive/Negative Emotion Recognition

The development of suitable EEG-based emotion recognition systems has become a main target in the last decades for Brain Computer Interface applications (BCI). However, there are scarce algorithms and procedures for real-time classification of emotions. The present study aims to investigate the feasibility of real-time emotion recognition implementation by the selection of parameters such as the appropriate time window segmentation and target bandwidths and cortical regions. We recorded the EEG-neural activity of 24 participants while they were looking and listening to an audiovisual database composed of positive and negative emotional video clips. We tested 12 different temporal window sizes, 6 ranges of frequency bands and 60 electrodes located along the entire scalp. Our results showed a correct classification of 86.96% for positive stimuli. The correct classification for negative stimuli was a little bit less (80.88%). The best time window size, from the tested 1[Formula: see text]s to 12[Formula: see text]s segments, was 12[Formula: see text]s. Although more studies are still needed, these preliminary results provide a reliable way to develop accurate EEG-based emotion classification.

Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain–Computer Interface

We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) signals to improve classification performances in motor imagery-based brain–computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of noninvasive BCIs.

Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings

The human hand is capable of producing versatile yet precise movements largely owing to the complex neuromuscular systems that control our finger movement. This study seeks to quantify the spatial activation patterns of the forearm flexor muscles during individualized finger flexions. High-density (HD) surface electromyogram (sEMG) signals of forearm flexor muscles were obtained, and individual motor units were decomposed from the sEMG. Both macro-level spatial patterns of EMG activity and micro-level motor unit distributions were used to systematically characterize the forearm flexor activation patterns. Different features capturing the spatial patterns were extracted, and the unique patterns of forearm flexor activation were then quantified using pattern recognition approaches. We found that the forearm flexor spatial activation during the ring finger flexion was mostly distinct from other fingers, whereas the activation patterns of the middle finger were least distinguishable. However, all the different activation patterns can still be classified in high accuracy (94–100%) using pattern recognition. Our findings indicate that the partial overlapping of neural activation can limit accurate identification of specific finger movement based on limited recordings and sEMG features, and that HD sEMG recordings capturing detailed spatial activation patterns at both macro- and micro-levels are needed.

Performance Prediction for a Near-Infrared Spectroscopy-Brain–Computer Interface Using Resting-State Functional Connectivity of the Prefrontal Cortex

One of the most important issues in current brain–computer interface (BCI) research is the prediction of a user’s BCI performance prior to the main BCI session because it would be useful to reduce the time required to determine the BCI paradigm best suited to that user. In electroencephalography (EEG)-BCI research, whether a user has low BCI performance toward a specific BCI paradigm has been estimated using a variety of resting-state EEG features. However, no previous study has attempted to predict the performance of near-infrared spectroscopy (NIRS)-BCI using resting-state NIRS data recorded before the main BCI experiment. In this study, we investigated whether the performance of an NIRS-BCI discriminating a mental arithmetic task from the baseline state could be predicted using resting-state functional connectivity (RSFC) of the prefrontal cortex. The investigation of NIRS signals recorded from 29 participants revealed that the RSFC between bilateral channels in the prefrontal area was negatively correlated with subsequent BCI performance (e.g. a fitted line for the RSFC between L2 and R2 channels explains 41% of BCI performance variation). We expect that our indicator can be used to predict BCI performance of an individual user prior to the main NIRS-BCI experiments, thereby facilitating implementation of more efficient NIRS-BCI systems.

A Dynamic Window Recognition Algorithm for SSVEP-Based Brain–Computer Interfaces Using a Spatio-Temporal Equalizer

The past decade has witnessed rapid development in the field of brain–computer interfaces (BCIs). While the performance is no longer the biggest bottleneck in the BCI application, the tedious training process and the poor ease-of-use have become the most significant challenges. In this study, a spatio-temporal equalization dynamic window (STE-DW) recognition algorithm is proposed for steady-state visual evoked potential (SSVEP)-based BCIs. The algorithm can adaptively control the stimulus time while maintaining the recognition accuracy, which significantly improves the information transfer rate (ITR) and enhances the adaptability of the system to different subjects. Specifically, a spatio-temporal equalization algorithm is used to reduce the adverse effects of spatial and temporal correlation of background noise. Based on the theory of multiple hypotheses testing, a stimulus termination criterion is used to adaptively control the dynamic window. The offline analysis which used a benchmark dataset and an offline dataset collected from 16 subjects demonstrated that the STE-DW algorithm is superior to the filter bank canonical correlation analysis (FBCCA), canonical variates with autoregressive spectral analysis (CVARS), canonical correlation analysis (CCA) and CCA reducing variation (CCA-RV) algorithms in terms of accuracy and ITR. The results show that in the benchmark dataset, the STE-DW algorithm achieved an average ITR of 134 bits/min, which exceeds the FBCCA, CVARS, CCA and CCA-RV. In off-line experiments, the STE-DW algorithm also achieved an average ITR of 116 bits/min. In addition, the online experiment also showed that the STE-DW algorithm can effectively expand the number of applicable users of the SSVEP-based BCI system. We suggest that the STE-DW algorithm can be used as a reliable identification algorithm for training-free SSVEP-based BCIs, because of the good balance between ease of use, recognition accuracy, ITR and user applicability.

A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

Two-Source Validation of Progressive FastICA Peel-Off for Automatic Surface EMG Decomposition in Human First Dorsal Interosseous Muscle

This study aims to assess the accuracy of a novel high density surface electromyogram (SEMG) decomposition method, namely automatic progressive FastICA peel-off (APFP), for automatic decomposition of experimental electrode array SEMG signals. A two-source method was performed by simultaneous concentric needle EMG and electrode array SEMG recordings from the human first dorsal interosseous (FDI) muscle, using a protocol commonly applied in clinical EMG examination. The electrode array SEMG was automatically decomposed by the APFP while the motor unit action potential (MUAP) trains were also independently identified from the concentric needle EMG. The degree of agreement of the common motor unit (MU) discharge timings decomposed from the two different categories of EMG signals was assessed. A total of 861 and 217 MUs were identified from the 114 trials of simultaneous high density SEMG and concentric needle EMG recordings, respectively. Among them 168 common (MUs) were found with a high average matching rate of [Formula: see text] for the discharge timings. The outcomes of this study show that the APFP can reliably decompose at least a subset of MUs in the high density SEMG signals recorded from the human FDI muscle during low contraction levels using a protocol analog to clinical EMG examination.

Communication Matters-Pitfalls and Promise of Hightech Communication Devices in Palliative Care of Severely Physically Disabled Patients With Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease, leading to progressive paralysis, dysarthria, dysphagia, and respiratory disabilities. Therapy is mostly focused on palliative interventions. During the course of the disease, verbal as well as nonverbal communicative abilities become more and more impaired. In this light, communication has been argued to be “the essence of human life” and crucial for patients' quality of life. High-tech augmentative and alternative communication (HT-AAC) technologies such as eyetracking based computer devices and brain-computer-interfaces provide the possibility to maintain caregiver-independent communication and environmental control even in the advanced disease state of ALS. Thus, they enable patients to preserve social participation and to independently communicate end-of-life-decisions. In accordance with these functions of HT-AAC, their use is reported to strengthen self-determination, increase patients' quality of life and reduce caregiver burden. Therefore, HT-AAC should be considered as standard of (palliative) care for people with ALS. On the other hand, the supply with individually tailored HT-AAC technologies is limited by external and patient-inherent variables. This review aims to provide an overview of the possibilities and limitations of HT-AAC technologies and discuss their role in the palliative care for patients with ALS.

Neuromodulation in multiple sclerosis

Neuromodulation, or the utilization of advanced technology for targeted electrical or chemical neuronal stimulation or inhibition, has been expanding in several neurological subspecialties. In the past decades, immune-modulating therapy has been the main focus of multiple sclerosis (MS) research with little attention to neuromodulation. However, with the recent advances in disease-modifying therapies, it is time to shift the focus of MS research to neuromodulation and restoration of function as with other neurological subspecialties. Preliminary research supports the value of intrathecal baclofen pump and functional electrical stimulation in improving spasticity and motor function in MS patients. Deep brain stimulation can improve MS-related tremor and trigeminal neuralgia. Spinal cord stimulation has been shown to be effective against MS-related pain and bladder dysfunction. Bladder overactivity also responds to sacral neuromodulation and posterior tibial nerve stimulation. Despite limited data in MS, transcranial magnetic stimulation and brain-computer interface are promising neuromodulatory techniques for symptom mitigation and neurorehabilitation of MS patients. In this review, we provide an overview of the available neuromodulatory techniques and the evidence for their use in MS.

Neuronal Activation Detection Using Vector Phase Analysis with Dual Threshold Circles: A Functional Near-Infrared Spectroscopy Study

In this paper, a new vector phase diagram differentiating the initial decreasing phase (i.e. initial dip) and the delayed hemodynamic response (HR) phase of oxy-hemoglobin changes ( Δ HbO) of functional near-infrared spectroscopy (fNIRS) is developed. The vector phase diagram displays the trajectories of Δ HbO and deoxy-hemoglobin changes ( Δ HbR), as orthogonal components, in the Δ HbO- Δ HbR polar coordinates. To determine the occurrence of an initial dip, dual threshold circles (an inner circle from the resting state, an outer circle from the peak values of the initial dip and the main HR) are incorporated into the phase diagram for making decisions. The proposed scheme is then applied to a brain-computer interface scheme, and its performance is evaluated in classifying two finger tapping tasks (right-hand thumb and little finger) from the left motor cortex. Three gamma functions are used to model the initial dip, the main HR, and the undershoot in generating the designed HR function. In classifying two tapping tasks, the signal mean and signal minimum values during 0-2.5 s, as features of initial dip, are used. The linear discriminant analysis was utilized as a classifier. The experimental results show that the active brain locations of the two tasks were quite distinctive ( p < 0.05 ), and moreover, spatially specific if using the initial dip map at 4 s in comparison to the map of HRs at 14 s. Also, the average classification accuracy was improved from 59% to 74.9% when using the phase diagram of dual threshold circles.

Control of a 7-DOF Robotic Arm System With an SSVEP-Based BCI

Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their physical environment, it remains a challenge for individuals with severe motor impairment, who do not have the motor control ability to move robots or prosthetic devices by manual control. In this study, to mitigate this issue, a noninvasive brain-computer interface (BCI)-based robotic arm control system using gaze based steady-state visual evoked potential (SSVEP) was designed and implemented using a portable wireless electroencephalogram (EEG) system. A 15-target SSVEP-based BCI using a filter bank canonical correlation analysis (FBCCA) method allowed users to directly control the robotic arm without system calibration. The online results from 12 healthy subjects indicated that a command for the proposed brain-controlled robot system could be selected from 15 possible choices in 4[Formula: see text]s (i.e. 2[Formula: see text]s for visual stimulation and 2[Formula: see text]s for gaze shifting) with an average accuracy of 92.78%, resulting in a 15 commands/min transfer rate. Furthermore, all subjects (even naive users) were able to successfully complete the entire move-grasp-lift task without user training. These results demonstrated an SSVEP-based BCI could provide accurate and efficient high-level control of a robotic arm, showing the feasibility of a BCI-based robotic arm control system for hand-assistance.

Functional Semi-Blind Source Separation Identifies Primary Motor Area Without Active Motor Execution

High time resolution techniques are crucial for investigating the brain in action. Here, we propose a method to identify a section of the upper-limb motor area representation (FS_M1) by means of electroencephalographic (EEG) signals recorded during a completely passive condition (FS_M1bySS). We delivered a galvanic stimulation to the median nerve and we applied to EEG the semi-Blind Source Separation (s-BSS) algorithm named Functional Source Separation (FSS). In order to prove that FS_M1bySS is part of FS_M1, we also collected EEG in a motor condition, i.e. during a voluntary movement task (isometric handgrip) and in a rest condition, i.e. at rest with eyes open and closed. In motor condition, we show that the cortico-muscular coherence (CMC) of FS_M1bySS does not differ from FS_ M1 CMC (0.04 for both sources). Moreover, we show that the FS_M1bySS’s ongoing whole band activity during Motor and both rest conditions displays high mutual information and time correlation with FS_M1 (above 0.900 and 0.800, respectively) whereas much smaller ones with the primary somatosensory cortex [Formula: see text] (about 0.300 and 0.500, [Formula: see text]). FS_M1bySS as a marker of the upper-limb FS_M1 representation obtainable without the execution of an active motor task is a great achievement of the FSS algorithm, relevant in most experimental, neurological and psychiatric protocols.

Epileptic Seizure Prediction Using Diffusion Distance and Bayesian Linear Discriminate Analysis on Intracranial EEG

Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature — diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance. The features of all channels composing a feature vector are then fed into a Bayesian Linear Discriminant Analysis (BLDA) classifier. Finally, postprocessing procedure is applied to reduce false prediction alarms. The prediction method is evaluated on the public intracranial EEG dataset, which consists of 577.67[Formula: see text]h of intracranial EEG recordings from 21 patients with 87 seizures. We achieved a sensitivity of 85.11% for a seizure occurrence period of 30[Formula: see text]min and a sensitivity of 93.62% for a seizure occurrence period of 50[Formula: see text]min, both with the seizure prediction horizon of 10[Formula: see text]s. Our false prediction rate was 0.08/h. The proposed method yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.