Classifying EEG signals preceding right hand, left hand, tongue, and right foot movements and motor imageries

A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi-Kernel Extreme Learning Machine

BACKGROUND

In the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals from the same joint remains challenging due to their similar brain spatial distribution.

NEW METHOD

We designed experiments involving three motor imagery tasks-wrist extension, wrist flexion, and wrist abduction-with six participants. Based on this, a single-joint multi-task motor imagery EEG signal recognition method using Empirical Wavelet Decomposition and Multi-Kernel Extreme Learning Machine is proposed. This method employs Empirical Wavelet Decomposition (EWT) for modal decomposition, screening, and reconstruction of raw EEG signals, feature extraction using Common Spatial Patterns (CSP), and classification using Multi-Kernel Extreme Learning Machine (MKELM).

RESULTS

After EWT processing, differences in time and frequency characteristics between EEG signals of different classes were enhanced, with the MKELM model achieving an average recognition accuracy of 91.93 %.

COMPARISON WITH OTHER METHODS AND CONCLUSIONS

We compared EWT with Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Local Mean Decomposition (LMD), and Wavelet Packet Decomposition (WPD). The results showed that the differences between various types of EEG signals processed by EWT were the most pronounced. The MKELM model outperformed traditional machine learning models such as Extreme Learning Machine (ELM), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) in terms of recognition performance, and also exhibited faster training speeds than deep learning models such as Bayesian Convolutional Neural Network (BCNN) and Attention-based Dual-scale Fusion Convolutional Neural Network (ADFCNN). In summary, the proposed method provides a new approach for achieving finer Brain-Computer Interface commands.

Primary somatosensory cortex sensitivity may increase upon completion of a motor task

OBJECTIVES

The electroencephalogram and magnetic field primary somatosensory cortex (S1)-derived components are attenuated before and during motor tasks compared to the resting state, a phenomenon called gating; however, the S1 response after a motor task has not been well studied. We aimed to investigate sensory information processing immediately after motor tasks using magnetoencephalography.

MATERIALS AND METHODS

We investigated sensory information processing immediately after finger movement using magnetoencephalography in 14 healthy adults. Volunteers performed a simple reaction task where they were required to press a button when they received a cue. In parallel, electrical stimulation to the right index finger was applied at regular intervals to detect the magnetic brain field changes. The end of the motor task timing was defined using the event-related synchronization (ERS) appearance latency in the brain magnetic field's beta band around the primary motor cortex. The ERS appearance latency and the sensory stimuli timing applied every 500 ms were synchronized over the experimental system timeline. We examined whether there was a difference in the S1 somatosensory evoked field responses between the ERS emergence and ERS disappearance phase, focusing on the N20m-P35m peak-to-peak amplitude (N20m-P35m amplitude) value. A control experiment was also conducted in which only sensory stimulation was applied with no motor task.

RESULTS

The N20m-P35m mean amplitude value was significantly higher in the ERS emergence phase (15.81 nAm; standard deviation [SD], 6.54 nAm) than in the ERS disappearance phase (13.54 nAm; SD, 5.12 nAm) (p < 0.05) and the control (12.08 nAm, SD 5.61 nAm) (p = 0.013). No statistically significant differences were identified between the ERS disappearance phase and the control (p = 0.281).

CONCLUSIONS

The S1 sensitivity may increase rapidly after exiting from the gating influence in S1 (after completing a motor task).

Functional mapping of sensorimotor activation in the human thalamus at 9.4 Tesla

Although the thalamus is perceived as a passive relay station for almost all sensory signals, the function of individual thalamic nuclei remains unresolved. In the present study, we aimed to identify the sensorimotor nuclei of the thalamus in humans using task-based fMRI at a field strength of 9.4T by assessing the individual subject-specific sensorimotor BOLD response during a combined active motor (finger-tapping) and passive sensory (tactile-finger) stimulation. We demonstrate that both tasks increase BOLD signal response in the lateral nuclei group (VPL, VA, VLa, and VLp), and in the pulvinar nuclei group (PuA, PuM, and PuL). Finger-tapping stimuli evokes a stronger BOLD response compared to the tactile stimuli, and additionally engages the intralaminar nuclei group (CM and Pf). In addition, our results demonstrate reproducible thalamic nuclei activation during motor and tactile stimuli. This work provides important insight into understanding the function of individual thalamic nuclei in processing various input signals and corroborates the benefits of using ultra-high-field MR scanners for functional imaging of fine-scale deeply located brain structures.

BCI system with lower-limb robot improves rehabilitation in spinal cord injury patients through short-term training: a pilot study

In the recent years, the increasing applications of brain-computer interface (BCI) in rehabilitation programs have enhanced the chances of functional recovery for patients with neurological disorders. We presented and validated a BCI system with a lower-limb robot for short-term training of patients with spinal cord injury (SCI). The cores of this system included: (1) electroencephalogram (EEG) features related to motor intention reported through experiments and used to drive the robot; (2) a decision tree to determine the training mode provided for patients with different degrees of injuries. Seven SCI patients (one American Spinal Injury Association Impairment Scale (AIS) A, three AIS B, and three AIS C) participated in the short-term training with this system. All patients could learn to use the system rapidly and maintained a high intensity during the training program. The strength of the lower limb key muscles of the patients was improved. Four AIS A/B patients were elevated to AIS C. The cumulative results indicate that clinical application of the BCI system with lower-limb robot is feasible and safe, and has potentially positive effects on SCI patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09801-6.

Exploring EEG spectral and temporal dynamics underlying a hand grasp movement

For brain-computer interfaces, resolving the differences between pre-movement and movement requires decoding neural ensemble activity in the motor cortex’s functional regions and behavioural patterns. Here, we explored the underlying neural activity and mechanisms concerning a grasped motor task by recording electroencephalography (EEG) signals during the execution of hand movements in healthy subjects. The grasped movement included different tasks; reaching the target, grasping the target, lifting the object upwards, and moving the object in the left or right directions. 163 trials of EEG data were acquired from 30 healthy participants who performed the grasped movement tasks. Rhythmic EEG activity was analysed during the premovement (alert task) condition and compared against grasped movement tasks while the arm was moved towards the left or right directions. The short positive to negative deflection that initiated around -0.5ms as a wave before the onset of movement cue can be used as a potential biomarker to differentiate movement initiation and movement. A rebound increment of 14% of beta oscillations and 26% gamma oscillations in the central regions was observed and could be used to distinguish pre-movement and grasped movement tasks. Comparing movement initiation to grasp showed a decrease of 10% in beta oscillations and 13% in gamma oscillations, and there was a rebound increment 4% beta and 3% gamma from grasp to grasped movement. We also investigated the combination MRCPs and spectral estimates of α, β, and γ oscillations as features for machine learning classifiers that could categorize movement conditions. Support vector machines with 3^rd order polynomial kernel yielded 70% accuracy. Pruning the ranked features to 5 leaf nodes reduced the error rate by 16%. For decoding grasped movement and in the context of BCI applications, this study identifies potential biomarkers, including the spatio-temporal characteristics of MRCPs, spectral information, and choice of classifiers for optimally distinguishing initiation and grasped movement.

Feature- and classification analysis for detection and classification of tongue movements from single-trial pre-movement EEG

Individuals with severe tetraplegia can benefit from brain-computer interfaces (BCIs). While most movement-related BCI systems focus on right/left hand and/or foot movements, very few studies have considered tongue movements to construct a multiclass BCI. The aim of this study was to decode four movement directions of the tongue (left, right, up, and down) from single-trial pre-movement EEG and provide a feature and classifier investigation. In offline analyses (from ten healthy participants) detection and classification were performed using temporal, spectral, entropy, and template features classified using either a linear discriminative analysis, support vector machine, random forest or multilayer perceptron classifiers. Besides the 4-class classification scenario, all possible 3-, and 2-class scenarios were tested to find the most discriminable movement type. The linear discriminant analysis achieved on average, higher classification accuracies for both movement detection and classification. The right- and down tongue movements provided the highest and lowest detection accuracy (95.3±4.3% and 91.7±4.8%), respectively. The 4-class classification achieved an accuracy of 62.6±7.2%, while the best 3-class classification (using left, right, and up movements) and 2-class classification (using left and right movements) achieved an accuracy of 75.6±8.4% and 87.7±8.0%, respectively. Using only a combination of the temporal and template feature groups provided further classification accuracy improvements. Presumably, this is because these feature groups utilize the movement-related cortical potentials, which are noticeably different on the left- versus right brain hemisphere for the different movements. This study shows that the cortical representation of the tongue is useful for extracting control signals for multi-class movement detection BCIs.

Increased intensity of unintended mirror muscle contractions after cervical spinal cord injury is associated with changes in interhemispheric and corticomuscular coherences

Mirror contractions refer to unintended contractions of the contralateral homologous muscles during voluntary unilateral contractions or movements. Exaggerated mirror contractions have been found in several neurological diseases and indicate dysfunction or lesion of the cortico-spinal pathway. The present study investigates mirror contractions and the associated interhemispheric and corticomuscular interactions in adults with spinal cord injury (SCI) - who present a lesion of the cortico-spinal tract - compared to able-bodied participants (AB). Eight right-handed adults with chronic cervical SCI and ten age-matched right-handed able-bodied volunteers performed sets of right elbow extensions at 20% of maximal voluntary contraction. Electromyographic activity (EMG) of the right and left elbow extensors, interhemispheric coherence over cerebral sensorimotor regions evaluated by electroencephalography (EEG) and corticomuscular coherence between signals over the cerebral sensorimotor regions and each extensor were quantified. Overall, results revealed that participants with SCI exhibited (1) increased EMG activity of both active and unintended active limbs, suggesting more mirror contractions, (2) reduced corticomuscular coherence between signals over the left sensorimotor region and the right active limb and increased corticomuscular coherence between the right sensorimotor region and the left unintended active limb, (3) decreased interhemispheric coherence between signals over the two sensorimotor regions. The increased corticomuscular communication and decreased interhemispheric communication may reflect a reduced inhibition leading to increased communication with the unintended active limb, possibly resulting to exacerbated mirror contractions in SCI. Finally, mirror contractions could represent changes of neural and neuromuscular communication after SCI.

Event-Related Desynchronization and Corticomuscular Coherence Observed During Volitional Swallow by Electroencephalography Recordings in Humans

Swallowing in humans involves many cortical areas although it is partly mediated by a series of brainstem reflexes. Cortical motor commands are sent to muscles during swallow. Previous works using magnetoencephalography showed event-related desynchronization (ERD) during swallow and corticomuscular coherence (CMC) during tongue movements in the bilateral sensorimotor and motor-related areas. However, there have been few analogous works that use electroencephalography (EEG). We investigated the ERD and CMC in the bilateral sensorimotor, premotor, and inferior prefrontal areas during volitional swallow by EEG recordings in 18 healthy human subjects. As a result, we found a significant ERD in the beta frequency band and CMC in the theta, alpha, and beta frequency bands during swallow in those cortical areas. These results suggest that EEG can detect the desynchronized activity and oscillatory interaction between the cortex and pharyngeal muscles in the bilateral sensorimotor, premotor, and inferior prefrontal areas during volitional swallow in humans.

Unraveling somatotopic organization in the human brain using machine learning and adaptive supervoxel-based parcellations

In addition to the well-established somatotopy in the pre- and post-central gyrus, there is now strong evidence that somatotopic organization is evident across other regions in the sensorimotor network. This raises several experimental questions: To what extent is activity in the sensorimotor network effector-dependent and effector-independent? How important is the sensorimotor cortex when predicting the motor effector? Is there redundancy in the distributed somatotopically organized network such that removing one region has little impact on classification accuracy? To answer these questions, we developed a novel experimental approach. fMRI data were collected while human subjects performed a precisely controlled force generation task separately with their hand, foot, and mouth. We used a simple linear iterative clustering (SLIC) algorithm to segment whole-brain beta coefficient maps to build an adaptive brain parcellation and then classified effectors using extreme gradient boosting (XGBoost) based on parcellations at various spatial resolutions. This allowed us to understand how data-driven adaptive brain parcellation granularity altered classification accuracy. Results revealed effector-dependent activity in regions of the post-central gyrus, precentral gyrus, and paracentral lobule. SMA, regions of the inferior and superior parietal lobule, and cerebellum each contained effector-dependent and effector-independent representations. Machine learning analyses showed that increasing the spatial resolution of the data-driven model increased classification accuracy, which reached 94% with 1755 supervoxels. Our SLIC-based supervoxel parcellation outperformed classification analyses using established brain templates and random simulations. Occlusion experiments further demonstrated redundancy across the sensorimotor network when classifying effectors. Our observations extend our understanding of effector-dependent and effector-independent organization within the human brain and provide new insight into the functional neuroanatomy required to predict the motor effector used in a motor control task.

Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation

Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combination of VR with electroencephalography (EEG) presents further opportunities to improve therapeutic efficacy by personalizing the paradigm. Specifically, the idea is to synchronize the choice and timing of stimuli in the perceived virtual world with fluctuating brain states relevant to motor behavior. Here, we present an open source EEG single-trial based classification pipeline that is designed to identify ongoing brain states predictive of the planning and execution of movements. 9 healthy volunteers each performed 1080 trials of a repetitive reaching task with an implicit two-alternative forced choice, i.e., use of the right or left hand, in response to the appearance of a visual target. The performance of the EEG decoding pipeline was assessed with respect to classification accuracy of right vs. left arm use, based on the EEG signal at the time of the stimulus. Different features, feature extraction methods, and classifiers were compared at different time windows; the number and location of informative EEG channels and the number of calibration trials needed were also quantified, as well as any benefits from individual-level optimization of pipeline parameters. This resulted in a set of recommended parameters that achieved an average 83.3% correct prediction on never-before-seen testing data, and a state-of-the-art 77.1% in a real-time simulation. Neurophysiological plausibility of the resulting classifiers was assessed by time-frequency and event-related potential analyses, as well as by Independent Component Analysis topographies and cortical source localization. We expect that this pipeline will facilitate the identification of relevant brain states as prospective therapeutic targets in closed-loop EEG-VR motor neurorehabilitation.

An end-to-end CNN with attentional mechanism applied to raw EEG in a BCI classification task

Objective.Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based MI classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training data, which is particularly acute for MI tasks, because these are cognitively demanding and thus fatigue inducing.Approach.To address these issues, we proposed an end-to-end CNN-based neural network with attentional mechanism together with different data augmentation (DA) techniques. We tested it on two benchmark MI datasets, brain-computer interface (BCI) competition IV 2a and 2b. In addition, we collected a new dataset, recorded using high-density EEG, and containing both MI and motor execution (ME) tasks, which we share with the community.Main results.Our proposed neural-network architecture outperformed all state-of-the-art methods that we found in the literature, with and without DA, reaching an average classification accuracy of 93.6% and 87.83% on BCI 2a and 2b, respectively. We also directly compare decoding of MI and ME tasks. Focusing on MI classification, we find optimal channel configurations and the best DA techniques as well as investigate combining data across participants and the role of transfer learning.Significance.Our proposed approach improves the classification accuracy for MI in the benchmark datasets. In addition, collecting our own dataset enables us to compare MI and ME and investigate various aspects of EEG decoding critical for neuroscience and BCI.

Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface

A brain-computer interface (BCI) can connect humans and machines directly and has achieved successful applications in the past few decades. Many new BCI paradigms and algorithms have been developed in recent years. Therefore, it is necessary to review new progress in BCIs. This paper summarizes progress for EEG-based BCIs from the perspective of encoding paradigms and decoding algorithms, which are two key elements of BCI systems. Encoding paradigms are grouped by their underlying neural meachanisms, namely sensory- and motor-related, vision-related, cognition-related and hybrid paradigms. Decoding algorithms are reviewed in four categories, namely decomposition algorithms, Riemannian geometry, deep learning and transfer learning. This review will provide a comprehensive overview of both modern primary paradigms and algorithms, making it helpful for those who are developing BCI systems.

A Comparison of Conventional and Tri-Polar EEG Electrodes for Decoding Real and Imaginary Finger Movements from One Hand

The representations of different fingers in the sensorimotor cortex are largely overlapped, which necessitate a good signal-to-noise ratio (SNR) and high spatial resolution to classify individual finger movements from one hand. Electroencephalography (EEG) recorded with disc electrodes has low SNR and poor spatial resolution. The surface Laplacian has been applied to EEG to improve the spatial resolution and selectivity of the surface electrical activity recording. Tri-polar concentric ring electrodes (TCREs) were shown to estimate the Laplacian automatically with better spatial resolution than disc electrodes. For this work, movement-related potentials (MRPs) were recorded from four TCREs and disc electrodes while 13 subjects performed real and imaginary finger movements. The MRP signals recorded with the TCREs have significantly less mutual information and coherence between neighboring locations compared to disc electrodes. The results also show that signals from TCREs generated higher accuracy compared to disc electrodes. It further shows that TCREs using temporal EEG data as features yield an average accuracy of [Formula: see text]% and [Formula: see text]% for real and imaginary finger movements, respectively, which is significantly higher than utilizing EEG spectral power changes in [Formula: see text] and [Formula: see text] bands as features. Similarly, with the disc electrodes, it achieved highest accuracy of [Formula: see text]% and [Formula: see text]% for real and imaginary finger movements, respectively, with temporal EEG data feature.

Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies

Recent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurate head model, defining how neural currents propagate from the cortex to the sensors placed over the scalp. To the best of our knowledge, no study has been conducted yet to systematically test to what extent head modeling accuracy impacts on EEG-RSN reconstruction. To address this question, we used 256-channel hdEEG data collected in a group of young healthy participants at rest. We first estimated functional connectivity in EEG-RSNs by means of band-limited power envelope correlations, using neural activity estimated with an optimized analysis workflow. Then, we defined a series of head models with different levels of complexity, specifically testing the effect of different electrode positioning techniques and head tissue segmentation methods. We observed that robust EEG-RSNs can be obtained using a realistic head model, and that inaccuracies due to head tissue segmentation impact on RSN reconstruction more than those due to electrode positioning. Additionally, we found that EEG-RSN robustness to head model variations had space and frequency specificity. Overall, our results may contribute to defining a benchmark for assessing the reliability of hdEEG functional connectivity measures.

The Riemannian Spatial Pattern method: mapping and clustering movement imagery using Riemannian geometry

OBJECTIVE

Over the last decade, Riemannian geometry has shown promising results for motor imagery classification. However, extracting the underlying spatial features is not as straightforward as for applying Common Spatial Pattern (CSP) filtering prior to classification. In this article, we propose a simple way to extract the spatial patterns obtained from Riemannian classification: the Riemannian Spatial Pattern (RSP) method, which is based on the backward channel selection procedure.

APPROACH

The RSP method was compared to the CSP approach on ECoG data obtained from a quadriplegic patient while performing imagined movements of arm articulations and fingers.

MAIN RESULTS

Similar results were found between the RSP and CSP methods for mapping each motor imagery task with activations following the classical somatotopic organization. Clustering obtained by pairwise comparisons of imagined motor movements however, revealed higher differentiation for the RSP method compared to the CSP approach. Importantly, the RSP approach could provide a precise comparison of the imagined finger flexions which added supplementary information to the mapping results.

SIGNIFICANCE

Our new RSP method illustrates the interest of the Riemannian framework in the spatial domain and as such offers new avenues for the neuroimaging community. This study is part of an ongoing clinical trial registered with ClinicalTrials.gov, NCT02550522.

Development of a Guidance System for Motor Imagery Enhancement Using the Virtual Hand Illusion

Motor imagery (MI) is widely used to produce input signals for brain-computer interfaces (BCI) due to the similarities between MI-BCI and the planning-execution cycle. Despite its usefulness, MI tasks can be ambiguous to users and MI produces weaker cortical signals than motor execution. Existing MI guidance systems, which have been reported to provide visual guidance for MI and enhance MI, still have limitations: insufficient immersion for MI or poor expandability to MI for another body parts. We propose a guidance system for MI enhancement that can immerse users in MI and will be easy to extend to other body parts and target motions with few physical constraints. To make easily extendable MI guidance system, the virtual hand illusion is applied to the MI guidance system with a motion tracking sensor. MI enhancement was evaluated in 11 healthy people by comparison with another guidance system and conventional motor commands for BCI. The results showed that the proposed MI guidance system produced an amplified cortical signal compared to pure MI (p < 0.017), and a similar cortical signal as those produced by both actual execution (p > 0.534) and an MI guidance system with the rubber hand illusion (p > 0.722) in the contralateral region. Therefore, we believe that the proposed MI guidance system with the virtual hand illusion is a viable alternative to existing MI guidance systems in various applications with MI-BCI.

Crosstalk disrupts the production of motor imagery brain signals in brain-computer interfaces

Brain-computer interfaces (BCIs) target specific brain activity for neuropsychological rehabilitation, and also allow patients with motor disabilities to control mobility and communication devices. Motor imagery of single-handed actions is used in BCIs but many users cannot control the BCIs effectively, limiting applications in the health systems. Crosstalk is unintended brain activations that interfere with bimanual actions and could also occur during motor imagery. To test if crosstalk impaired BCI user performance, we recorded EEG in 46 participants while they imagined movements in four experimental conditions using motor imagery: left hand (L), right hand (R), tongue (T) and feet (F). Pairwise classification accuracies of the tasks were compared (LR, LF, LT, RF, RT, FT), using common spatio-spectral filters and linear discriminant analysis. We hypothesized that LR classification accuracy would be lower than every other combination that included a hand imagery due to crosstalk. As predicted, classification accuracy for LR (58%) was reliably the lowest. Interestingly, participants who showed poor LR classification also demonstrated at least one good TR, TL, FR or FL classification; and good LR classification was detected in 16% of the participants. For the first time, we showed that crosstalk occurred in motor imagery, and affected BCI performance negatively. Such effects are effector-sensitive regardless of the BCI methods used; and likely not apparent to the user or the BCI developer. This means that tasks choice is crucial when designing BCI. Critically, the effects of crosstalk appear mitigatable. We conclude that understanding crosstalk mitigation is important for improving BCI applicability.

Supplementary Information

The online version of this article contains supplementary material available (10.1007/s13755-021-00142-y).

Task Transfer Learning for EEG Classification in Motor Imagery-Based BCI System

The motor-imagery brain-computer interface system (MI-BCI) has a board prospect for development. However, long calibration time and lack of enough MI commands limit its use in practice. In order to enlarge the command set, we add the combinations of traditional MI commands as new commands into the command set. We also design an algorithm based on transfer learning so as to decrease the calibration time for collecting EEG signal and training model. We create feature extractor based on data from traditional commands and transfer patterns through the data from new commands. Through the comparison of the average accuracy between our algorithm and traditional algorithms and the visualization of spatial patterns in our algorithm, we find that the accuracy of our algorithm is much higher than traditional algorithms, especially as for the low-quality datasets. Besides, the visualization of spatial patterns is meaningful. The algorithm based on transfer learning takes the advantage of the information from source data. We enlarge the command set while shortening the calibration time, which is of significant importance to the MI-BCI application.

A review of user training methods in brain computer interfaces based on mental tasks

Mental-Tasks based Brain-Computer Interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the ability to self-regulate specific brain signals. However, the human learning process to control a BCI is still relatively poorly understood and how to optimally train this ability is currently under investigation. Despite their promises and achievements, traditional training programs have been shown to be sub-optimal and could be further improved. In order to optimize user training and improve BCI performance, human factors should be taken into account. An interdisciplinary approach should be adopted to provide learners with appropriate and/or adaptive training. In this article, we provide an overview of existing methods for MT-BCI user training - notably in terms of environment, instructions, feedback and exercises. We present a categorization and taxonomy of these training approaches, provide guidelines on how to choose the best methods and identify open challenges and perspectives to further improve MT-BCI user training.

Spatial-temporal aspects of continuous EEG-based neurorobotic control

OBJECTIVE

The goal of this work is to identify the spatio-temporal facets of state-of-the-art electroencephalography (EEG)-based continuous neurorobotics that need to be addressed, prior to deployment in practical applications at home and in the clinic.

APPROACH

Nine healthy human subjects participated in five sessions of one-dimensional (1D) horizontal (LR), 1D vertical (UD) and two-dimensional (2D) neural tracking from EEG. Users controlled a robotic arm and virtual cursor to continuously track a Gaussian random motion target using EEG sensorimotor rhythm modulation via motor imagery (MI) commands. Continuous control quality was analyzed in the temporal and spatial domains separately.

MAIN RESULTS

Axis-specific errors during 2D tasks were significantly larger than during 1D counterparts. Fatigue rates were larger for control tasks with higher cognitive demand (LR, left- and right-hand MI) compared to those with lower cognitive demand (UD, both hands MI and rest). Additionally, robotic arm and virtual cursor control exhibited equal tracking error during all tasks. However, further spatial error analysis of 2D control revealed a significant reduction in tracking quality that was dependent on the visual interference of the physical device. In fact, robotic arm performance was significantly greater than that of virtual cursor control when the users' sightlines were not obstructed.

SIGNIFICANCE

This work emphasizes the need for practical interfaces to be designed around real-world tasks of increased complexity. Here, the dependence of control quality on cognitive task demand emphasizes the need for decoders that facilitate the translation of 1D task mastery to 2D control. When device footprint was accounted for, the introduction of a physical robotic arm improved control quality, likely due to increased user engagement. In general, this work demonstrates the need to consider both the physical footprint of devices, the complexity of training tasks, and the synergy of control strategies during the development of neurorobotic control.

Decoding kinetic features of hand motor preparation from single-trial EEG using convolutional neural networks

Building accurate movement decoding models from brain signals is crucial for many biomedical applications. Predicting specific movement features, such as speed and force, before movement execution may provide additional useful information at the expense of increasing the complexity of the decoding problem. Recent attempts to predict movement speed and force from the electroencephalogram (EEG) achieved classification accuracies at or slightly above chance levels, highlighting the need for more accurate prediction strategies. Thus, the aims of this study were to accurately predict hand movement speed and force from single-trial EEG signals and to decode neurophysiological information of motor preparation from the prediction strategies. To these ends, a decoding model based on convolutional neural networks (ConvNets) was implemented and compared against other state-of-the-art prediction strategies, such as support vector machines and decision trees. ConvNets outperformed the other prediction strategies, achieving an overall accuracy of 84% in the classification of two different levels of speed and force (four-class classification) from pre-movement single-trial EEG (100 ms and up to 1,600 ms prior to movement execution). Furthermore, an analysis of the ConvNet architectures suggests that the network performs a complex spatiotemporal integration of EEG data to optimize classification accuracy. These results show that movement speed and force can be accurately predicted from single-trial EEG, and that the prediction strategies may provide useful neurophysiological information about motor preparation.

Neurobehavioural signatures in race car driving: a case study

Recent technological developments in mobile brain and body imaging are enabling new frontiers of real-world neuroscience. Simultaneous recordings of body movement and brain activity from highly skilled individuals as they demonstrate their exceptional skills in real-world settings, can shed new light on the neurobehavioural structure of human expertise. Driving is a real-world skill which many of us acquire to different levels of expertise. Here we ran a case-study on a subject with the highest level of driving expertise-a Formula E Champion. We studied the driver's neural and motor patterns while he drove a sports car on the "Top Gear" race track under extreme conditions (high speed, low visibility, low temperature, wet track). His brain activity, eye movements and hand/foot movements were recorded. Brain activity in the delta, alpha, and beta frequency bands showed causal relation to hand movements. We herein demonstrate the feasibility of using mobile brain and body imaging even in very extreme conditions (race car driving) to study the sensory inputs, motor outputs, and brain states which characterise complex human skills.

Chaotic behaviour of EEG responses with an identical grasp posture

Individuals with severe neuromuscular ailments can benefit from restoring their grasp activities with a brain-controlled upper-limb neuroprosthesis. EEG signals can be utilized as the driving source, and to implement natural human-like grasping abilities. Although good accuracy has already been achieved in classifying the various grasp patterns for specific sets of objects, unseen objects are still a hurdle in real-life implementation. Generalization of grasp patterns should be explored without any prior knowledge of the objects. In this regard, the similarity of motor imagery for different objects requiring similar grasp pattern can be utilized. It is also necessary to identify the brain regions that exhibit prominent distinguishability during different grasp patterns. In this study, we propose a chaos-based method to decode the motor imagery of two quite similar Power grasp patterns-cylindrical and spherical-for holding various objects. Three distinct suitable objects were chosen for each of the two patterns, and a 29-channel EEG was taken of 18 healthy participants to explore motor imagery for grasping the objects. Nonlinear correlation dimension was employed on the EEG data, at sub-band levels α, upper β, and γ, to analyse the distinguishability, as well as the similarity of grasp patterns for the objects. ANOVA was subsequently performed on the obtained CD parameters to identify the contribution of each electrode channel. Furthermore, using an SVM classifier, more than 80% accuracy was obtained in classifying the grasping patterns at the upper β sub-band. The outcome may lead to identification of optimum feature sets of motor imagery from specific brain regions for random objects grasps.

Reduce brain computer interface inefficiency by combining sensory motor rhythm and movement-related cortical potential features

OBJECTIVE

Brain Computer Interface (BCI) inefficiency indicates that there would be 10% to 50% of users are unable to operate Motor-Imagery-based BCI systems. Importantly, the almost all previous studieds on BCI inefficiency were based on tests of Sensory Motor Rhythm (SMR) feature. In this work, we assessed the occurrence of BCI inefficiency with SMR and Movement-Related Cortical Potential (MRCP) features.

APPROACH

A pool of datasets of resting state and movements related EEG signals was recorded with 93 subjects during 2 sessions in separated days. Two methods, Common Spatial Pattern (CSP) and template matching, were used for SMR and MRCP feature extraction, and a winner-take-all strategy was applied to assess pattern recognition with posterior probabilities from Linear Discriminant Analysis to combine SMR and MRCP features.

MAIN RESULTS

The results showed that the two types of features showed high complementarity, in line with their weak intercorrelation. In the subject group with poor accuracies (< 70%) by SMR feature in the two-class problem (right foot vs. right hand), the combination of SMR and MRCP features improved the averaged accuracy from 62% to 79%. Importantly, accuracies obtained by feature combination exceeded the inefficiency threshold.

SIGNIFICANCE

The feature combination of SMR and MRCP is not new in BCI decoding, but the large scale and repeatable study on BCI inefficiency assessment by using SMR and MRCP features is novel. MRCP feature provides the similar classification accuracies on the two subject groups with poor (< 70%) and good (> 90%) accuracies by SMR feature. These results suggest that the combination of SMR and MRCP features may be a practical approach to reduce BCI inefficiency. While, 'BCI inefficiency' might be more aptly called 'SMR inefficiency' after this study.

Hand, foot and lip representations in primary sensorimotor cortex: a high-density electroencephalography study

The primary sensorimotor cortex plays a major role in the execution of movements of the contralateral side of the body. The topographic representation of different body parts within this brain region is commonly investigated through functional magnetic resonance imaging (fMRI). However, fMRI does not provide direct information about neuronal activity. In this study, we used high-density electroencephalography (hdEEG) to map the representations of hand, foot, and lip movements in the primary sensorimotor cortex, and to study their neural signatures. Specifically, we assessed the event-related desynchronization (ERD) in the cortical space. We found that the performance of hand, foot, and lip movements elicited an ERD in beta and gamma frequency bands. The primary regions showing significant beta- and gamma-band ERD for hand and foot movements, respectively, were consistent with previously reported using fMRI. We observed relatively weaker ERD for lip movements, which may be explained by the fact that less fine movement control was required. Overall, our study demonstrated that ERD based on hdEEG data can support the study of motor-related neural processes, with relatively high spatial resolution. An interesting avenue may be the use of hdEEG for deeper investigations into the pathophysiology of neuromotor disorders.

Decoding Imagined 3D Arm Movement Trajectories From EEG to Control Two Virtual Arms-A Pilot Study

Background: Realization of online control of an artificial or virtual arm using information decoded from EEG normally occurs by classifying different activation states or voluntary modulation of the sensorimotor activity linked to different overt actions of the subject. However, using a more natural control scheme, such as decoding the trajectory of imagined 3D arm movements to move a prosthetic, robotic, or virtual arm has been reported in a limited amount of studies, all using offline feed-forward control schemes.

Objective: In this study, we report the first attempt to realize online control of two virtual arms generating movements toward three targets/arm in 3D space. The 3D trajectory of imagined arm movements was decoded from power spectral density of mu, low beta, high beta, and low gamma EEG oscillations using multiple linear regression. The analysis was performed on a dataset recorded from three subjects in seven sessions wherein each session comprised three experimental blocks: an offline calibration block and two online feedback blocks. Target classification accuracy using predicted trajectories of the virtual arms was computed and compared with results of a filter-bank common spatial patterns (FBCSP) based multi-class classification method involving mutual information (MI) selection and linear discriminant analysis (LDA) modules.

Main Results: Target classification accuracy from predicted trajectory of imagined 3D arm movements in the offline runs for two subjects (mean 45%, std 5%) was significantly higher (p < 0.05) than chance level (33.3%). Nevertheless, the accuracy during real-time control of the virtual arms using the trajectory decoded directly from EEG was in the range of chance level (33.3%). However, the results of two subjects show that false-positive feedback may increase the accuracy in closed-loop. The FBCSP based multi-class classification method distinguished imagined movements of left and right arm with reasonable accuracy for two of the three subjects (mean 70%, std 5% compared to 50% chance level). However, classification of the imagined arm movement toward three targets was not successful with the FBCSP classifier as the achieved accuracy (mean 33%, std 5%) was similar to the chance level (33.3%). Sub-optimal components of the multi-session experimental paradigm were identified, and an improved paradigm proposed.

Behind the Scenes of Noninvasive Brain-Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter

Purpose

Brain-computer interface (BCI) techniques may provide computer access for individuals with severe physical impairments. However, the relatively hidden nature of BCI control obscures how BCI systems work behind the scenes, making it difficult to understand how electroencephalography (EEG) records the BCI related brain signals, what brain signals are recorded by EEG, and why these signals are targeted for BCI control. Furthermore, in the field of speech-language-hearing, signals targeted for BCI application have been of primary interest to clinicians and researchers in the area of augmentative and alternative communication (AAC). However, signals utilized for BCI control reflect sensory, cognitive and motor processes, which are of interest to a range of related disciplines including speech science.

Method

This tutorial was developed by a multidisciplinary team emphasizing primary and secondary BCI-AAC related signals of interest to speech-language-hearing.

Results

An overview of BCI-AAC related signals are provided discussing 1) how BCI signals are recorded via EEG, 2) what signals are targeted for non-invasive BCI control, including the P300, sensorimotor rhythms, steady state evoked potentials, contingent negative variation, and the N400, and 3) why these signals are targeted. During tutorial creation, attention was given to help support EEG and BCI understanding for those without an engineering background.

Conclusion

Tutorials highlighting how BCI-AAC signals are elicited and recorded can help increase interest and familiarity with EEG and BCI techniques and provide a framework for understanding key principles behind BCI-AAC design and implementation.

EEG Neural Correlates of Self-Paced Left- and Right-Hand Movement Intention during a Reaching Task

The study of neural correlates of self-paced movement intention helps develop a more natural and practical brain computer interface. In this paper, we studied EEG neural correlates of self-paced left- and right-hand movement intention during a reaching task. A slow decreased movement related cortical potential (MRCP) related to movement intention was found before both the left- and right-hand movements. The temporal start point and decrement of MRCP before the left- and right-hand reaching movements showed differently. Moreover, alpha band power increase/decrease was observed before the onset of both left- and right-hand movements. Alpha band powers increase before both movement conditions in the frontal-central area. In the central area, alpha band powers decrease consistently at electrodes C3, Cz and C4 before the right-hand reaching movement. While alpha band powers increase at electrodes C3 and Cz before the left-hand reaching movement.

Detection of reaching intention using EEG signals and nonlinear dynamic system identification

BACKGROUND AND OBJECTIVES

Low frequency electroencephalography (EEG) signals are associated with preparation of movement and thus provide valuable information for brain-machine interface applications. The purpose of this study was to detect movement intention from EEG signals before execution of self-paced arm reaching movements.

METHODS

Ten healthy individuals were recruited. Movement onset was determined from surface electromyography recordings time-locked with EEG signals. Unlike previous studies, which employed feature extraction and classification for decoding, a nonlinear dynamic multiple-input/single output (MISO) model was developed. The MISO model consisted of a cascade of Volterra structures and a threshold block, generating the binary output corresponding to intention/no-intention. The modeling process included input selection from a pool of different EEG channels. The predictive performance of the model was evaluated using the receiver operating characteristics curve, from which the optimum threshold was also selected. The Mann-Whitney statistics was employed to select the significant EEG channels for the output by examining the statistical significance of improvement in the predictive capability of the model when the respective channels were included.

RESULTS

With the proposed approach, movement intention was detected approximately 500 ms before the movement onset and on average, with an accuracy of 96.37 ± 0.94%, a sensitivity of 77.93 ± 4.40% and a specificity of 98.52 ± 1.19%.

CONCLUSIONS

The model output can be converted to motion commands for neuroprosthetic devices and exoskeletons in future applications.

Motor resonance during linguistic processing as shown by EEG in a naturalistic VR environment

Embodied cognition studies have shown motor resonance during action language processing, indicating that linguistic representations are at least partially multimodal. However, constraints of this activation linked to linguistic and extra-linguistic context, function and timing have not yet been fully explored. Importantly, embodied cognition binds social and physical contexts to cognition, suggesting that more ecologically valid contexts will yield more valid measures of cognitive processing. Herein, we measured cortical motor activation during language processing in a fully immersive Cave automatic virtual environment (CAVE). EEG was recorded while participants engaged in a Go/No-Go task. They heard action verbs and, for Go trials, performed a corresponding action on a virtual object. ERSP (event-related spectral perturbation) was calculated during verb processing, corresponding to the pattern of power suppression (event-related desynchronization - ERD) and enhancement (event-related synchronization - ERS) relative to the reference interval. Significant ERD emerged during verb processing in both the µ (8-13 Hz) and beta band (20-30 Hz) for both Go and No-Go trials. µ ERD emerged in the 400-500 msec time window, associated with lexical-semantic processing. Greater µ ERD emerged for Go compared to No-Go trials. The present results provide compelling evidence in a naturalistic setting of how motor and linguistic processes interact.

Electroencephalography-based endogenous brain-computer interface for online communication with a completely locked-in patient

Background

Brain–computer interfaces (BCIs) have demonstrated the potential to provide paralyzed individuals with new means of communication, but an electroencephalography (EEG)-based endogenous BCI has never been successfully used for communication with a patient in a completely locked-in state (CLIS).

Methods

In this study, we investigated the possibility of using an EEG-based endogenous BCI paradigm for online binary communication by a patient in CLIS. A female patient in CLIS participated in this study. She had not communicated even with her family for more than one year with complete loss of motor function. Offline and online experiments were conducted to validate the feasibility of the proposed BCI system. In the offline experiment, we determined the best combination of mental tasks and the optimal classification strategy leading to the best performance. In the online experiment, we investigated whether our BCI system could be potentially used for real-time communication with the patient.

Results

An online classification accuracy of 87.5% was achieved when Riemannian geometry-based classification was applied to real-time EEG data recorded while the patient was performing one of two mental-imagery tasks for 5 s.

Conclusions

Our results suggest that an EEG-based endogenous BCI has the potential to be used for online communication with a patient in CLIS.

Optimizing Prediction Model for a Noninvasive Brain-Computer Interface Platform Using Channel Selection, Classification, and Regression

A brain-computer interface (BCI) platform can be utilized by a user to control an external device without making any overt movements. An EEG-based computer cursor control task is commonly used as a testbed for BCI applications. While traditional computer cursor control schemes are based on sensorimotor rhythm, a new scheme has recently been developed using imagined body kinematics (IBK) to achieve natural cursor movement in a shorter time of training. This article attempts to explore optimal decoding algorithms for an IBK paradigm using EEG signals with application to neural cursor control. The study is based on an offline analysis of 32 healthy subjects' training data. Various machine learning techniques were implemented to predict the kinematics of the computer cursor using EEG signals during the training tasks. Our results showed that a linear regression least squares model yielded the highest goodness-of-fit scores in the cursor kinematics model (70% in horizontal prediction and 40% in vertical prediction using a Theil-Sen regressor). Additionally, the contribution of each EEG channel on the predictability of cursor kinematics was examined for horizontal and vertical directions, separately. A directional classifier was also proposed to classify horizontal versus vertical cursor kinematics using EEG signals. By incorporating features extracted from specific frequency bands, we achieved 80% classification accuracy in differentiating horizontal and vertical cursor movements. The findings of the current study could facilitate a pathway to designing an optimized online neural cursor control.

Motor imagery and swallowing: a systematic literature review

ABSTRACT Objetive: to identify, in the literature, studies that address the use of motor imagery of swallowing. Methods: a systematic review in SCOPUS databases, Science Direct and Medline, with descriptors and free terms "Motor Imagery"; "Swallow"; "Feeding"; "Stomatognathic System"; "mastication ", "Chew "; "Deglutition "; "Deglutition Disorders "; and "Mental Practice". Original articles using the motor imagery of swallowing were included, while reviews were excluded. For data analysis, at the first and second steps, the reading of titles and abstracts of the studies was carried out. In the third step, all studies that were not excluded were read in full. Results: four manuscripts were selected. The use of motor imagery in the rehabilitation of swallowing shows to be a recent proposal (2014-2015). The sample was reduced and comprised mainly healthy individuals. The EMG of the supra-hyoid muscles was used in two manuscripts. The most used neuroimaging technique was the Near-Infrared Spectroscopy, demonstrating the occurrence of hemodynamic changes during motor imagery and motor execution of swallowing. Conclusion: the motor imagery produces brain response in the motor area of the brain, suggesting that mentalization of actions related to swallowing is effective. However, further studies are needed for the application of this approach in the swallowing rehabilitation.

Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks

In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces.

Advantages of EEG phase patterns for the detection of gait intention in healthy and stroke subjects

OBJECTIVE

One use of EEG-based brain-computer interfaces (BCIs) in rehabilitation is the detection of movement intention. In this paper we investigate for the first time the instantaneous phase of movement related cortical potential (MRCP) and its application to the detection of gait intention.

APPROACH

We demonstrate the utility of MRCP phase in two independent datasets, in which 10 healthy subjects and 9 chronic stroke patients executed a self-initiated gait task in three sessions. Phase features were compared to more conventional amplitude and power features.

MAIN RESULTS

The neurophysiology analysis showed that phase features have higher signal-to-noise ratio than the other features. Also, BCI detectors of gait intention based on phase, amplitude, and their combination were evaluated under three conditions: session-specific calibration, intersession transfer, and intersubject transfer. Results show that the phase based detector is the most accurate for session-specific calibration (movement intention was correctly detected in 66.5% of trials in healthy subjects, and in 63.3% in stroke patients). However, in intersession and intersubject transfer, the detector that combines amplitude and phase features is the most accurate one and the only that retains its accuracy (62.5% in healthy subjects and 59% in stroke patients) w.r.t. session-specific calibration.

SIGNIFICANCE

MRCP phase features improve the detection of gait intention and could be used in practice to remove time-consuming BCI recalibration.

Quantitative EEG Evaluation During Robot-Assisted Foot Movement

Passiveand imagined limbmovements induce changes in cerebral oscillatory activity. Central modulatory effects play a role in plastic changes, and are of uttermost importance in rehabilitation. This has extensively been studied for upper limb, but less is known for lower limb. The aim of this study is to investigate the topographical distribution of event-related desynchronization/synchronization(ERD/ERS) and task-relatedcoherence during a robot-assisted and a motor imagery task of lower limb in healthy subjects to inform rehabilitation paradigms. 32-channels electroencephalogram (EEG) was recorded in twenty-one healthy right footed and handed subjects during a robot-assisted single-joint cyclic right ankle movement performed by the BTS ANYMOV robotic hospital bed. Data were acquired with a block protocol for passive and imagined movement at a frequency of 0.2 Hz. ERD/ERS and task related coherence were calculated in alpha1 (8-10 Hz), alpha2 (10.5-12.5 Hz) and beta (13-30 Hz) frequency ranges. During passive movement, alpha2 rhythm desynchronized overC3 and ipsilateral frontal areas (F4, FC2, FC6); betaERD was detected over the bilateral motor areas (Cz, C3, C4). During motor imagery, a significant desynchronization was evident for alpha1 over contralateral sensorimotor cortex (C3), for alpha2 over bilateral motor areas (C3 and C4), and for beta over central scalp areas. Task-related coherence decreased during passive movement in alpha2 band between contralateral central area (C3, CP5, CP1, P3) and ipsilateral frontal area (F8, FC6, T8); beta band coherence decreased between C3-C4 electrodes, and increased between C3-Cz. These data contribute to the understanding of oscillatory activity and functional neuronal interactions during lower limb robot-assisted motor performance. The final output of this line of research is to inform the design and development of neurorehabilitation protocols.

A pipeline of spatio-temporal filtering for predicting the laterality of self-initiated fine movements from single trial readiness potentials

OBJECTIVE

In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Since the RP is attenuated for involuntary movements, a BCI driven by RP alone could facilitate intentional control amid a plethora of unintentional movements. Previous studies have attempted single trial classification of RP via spatial and temporal filtering methods, or by combining the RP with event-related desynchronization. However, RP feature extraction remains challenging due to the slow non-oscillatory nature of the potential, its variability among participants and the inherent noise in EEG signals. Here, we propose a participant-specific, individually optimized pipeline of spatio-temporal filtering (PSTF) to improve RP feature extraction for laterality prediction.

APPROACH

PSTF applies band-pass filtering on RP signals, followed by Fisher criterion spatial filtering to maximize class separation, and finally temporal window averaging for feature dimension reduction. Optimal parameters are simultaneously found by cross-validation for each participant. Using EEG data from 14 participants performing self-initiated left or right key presses as well as two benchmark BCI datasets, we compared the performance of PSTF to two popular methods: common spatial subspace decomposition, and adaptive spatio-temporal filtering.

MAIN RESULTS

On the BCI benchmark data sets, PSTF performed comparably to both existing methods. With the key press EEG data, PSTF extracted more discriminative features, thereby leading to more accurate (74.99% average accuracy) predictions of RP laterality than that achievable with existing methods.

SIGNIFICANCE

Naturalistic and volitional interaction with the world is an important capacity that is lost with traditional system-paced BCIs. We demonstrated a significant improvement in fine movement laterality prediction from RP features alone. Our work supports further study of RP-based BCI for intuitive asynchronous control of the environment, such as augmentative communication or wheelchair navigation.

3D hand motion trajectory prediction from EEG mu and beta bandpower

A motion trajectory prediction (MTP) - based brain-computer interface (BCI) aims to reconstruct the three-dimensional (3D) trajectory of upper limb movement using electroencephalography (EEG). The most common MTP BCI employs a time series of bandpass-filtered EEG potentials (referred to here as the potential time-series, PTS, model) for reconstructing the trajectory of a 3D limb movement using multiple linear regression. These studies report the best accuracy when a 0.5-2Hz bandpass filter is applied to the EEG. In the present study, we show that spatiotemporal power distribution of theta (4-8Hz), mu (8-12Hz), and beta (12-28Hz) bands are more robust for movement trajectory decoding when the standard PTS approach is replaced with time-varying bandpower values of a specified EEG band, ie, with a bandpower time-series (BTS) model. A comprehensive analysis comprising of three subjects performing pointing movements with the dominant right arm toward six targets is presented. Our results show that the BTS model produces significantly higher MTP accuracy (R~0.45) compared to the standard PTS model (R~0.2). In the case of the BTS model, the highest accuracy was achieved across the three subjects typically in the mu (8-12Hz) and low-beta (12-18Hz) bands. Additionally, we highlight a limitation of the commonly used PTS model and illustrate how this model may be suboptimal for decoding motion trajectory relevant information. Although our results, showing that the mu and beta bands are prominent for MTP, are not in line with other MTP studies, they are consistent with the extensive literature on classical multiclass sensorimotor rhythm-based BCI studies (classification of limbs as opposed to motion trajectory prediction), which report the best accuracy of imagined limb movement classification using power values of mu and beta frequency bands. The methods proposed here provide a positive step toward noninvasive decoding of imagined 3D hand movements for movement-free BCIs.

Neural and cortical analysis of swallowing and detection of motor imagery of swallow for dysphagia rehabilitation-A review

Swallowing is an essential function in our daily life; nevertheless, stroke or other neurodegenerative diseases can cause the malfunction of swallowing function, ie, dysphagia. The objectives of this review are to understand the neural and cortical basis of swallowing and tongue, and review the latest techniques on the detection of motor imagery of swallow (MI-SW) and motor imagery of tongue movements (MI-TM), so that a practical system can be developed for the rehabilitation of poststroke dysphagia patients. Specifically, we firstly describe the swallowing process and how the swallowing function is assessed clinically. Secondly, we review the techniques that performed the neural and cortical analysis of swallowing and tongue based on different modalities such as functional magnetic resonance imaging, positron emission tomography, near-infrared spectroscopy (NIRS), and magnetoencephalography. Thirdly, we review the techniques that performed detection and analysis of MI-SW and MI-TM for dysphagia stroke rehabilitation based on electroencephalography (EEG) and NIRS. Finally, discussions on the advantages and limitations of the studies are presented; an example system and future research directions for the rehabilitation of stroke dysphagia patients are suggested.

Detecting the Intention to Move Upper Limbs from Electroencephalographic Brain Signals

Early decoding of motor states directly from the brain activity is essential to develop brain-machine interfaces (BMI) for natural motor control of neuroprosthetic devices. Hence, this study aimed to investigate the detection of movement information before the actual movement occurs. This information piece could be useful to provide early control signals to drive BMI-based rehabilitation and motor assisted devices, thus providing a natural and active rehabilitation therapy. In this work, electroencephalographic (EEG) brain signals from six healthy right-handed participants were recorded during self-initiated reaching movements of the upper limbs. The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related α and β frequency bands and in electrodes placed above the motor cortex. This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement. The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement. On the basis of these results, detection of movement intention could be used in BMI settings to reduce the gap between mental motor processes and the actual movement performed by an assisted or rehabilitation robotic device.