Over the last few decades, the advances in disciplines such as neuroscience and engineering have introduced the brain&#x2013;computer interface (BCI) as a promising tool for neurorehabilitation and neurophysiology research. BCI research primarily aims at development of assistive and rehabilitation strategies for motor-impaired users and, hence, sensorimotor-rhythm (SMR)-based BCIs are widely explored. This type of BCI performs detection and interpretation of the neural activity underlying various user intentions, specifically related to movement, and translates these brain functionalities to the external environment without relying on normal neuromuscular pathways. The interfaced device thus achieves motor control directly from the neural activity. Motor-control BCIs investigate various aspects of human motor skills and have successfully identified neural patterns related to motor execution, imagination, and intention from bilateral limbs. In this article, we provide a summary of recent advances in motor control BCI, specifically in movement kinematics decoding and motor control of localized areas of the limb. We further discuss the research challenges and future scope of work in this area of research.

Noninvasive Brain-Computer Interface: Decoding Arm Movement Kinematics and Motor Control

Brain–computer interfaces based on motor imagery (MI) have been widely used to support the rehabilitation of motor functions of the upper limbs rather than lower limbs. This is probably because it is more difficult to detect the brain activities of lower limb MI. In order to reliably detect the brain activities of lower limbs to restore or improve the walking ability of the disabled, we propose a new paradigm of walking imagery (WI) in a virtual environment (VE), in order to elicit the reliable brain activities and achieve a significant training effect. First, we extract and fuse both the spatial and time-frequency features as a multi-view feature to represent the patterns in the brain activity. Second, we design a multi-view multi-level deep polynomial network (MMDPN) to explore the complementarity among the features so as to improve the detection of walking from an idle state. Our extensive experimental results show that the VE-based paradigm significantly performs better than the traditional text-based paradigm. In addition, the VE-based paradigm can effectively help users to modulate the brain activities and improve the quality of electroencephalography signals. We also observe that the MMDPN outperforms other deep learning methods in terms of classification performance.

Walking Imagery Evaluation in Brain Computer Interfaces via a Multi-View Multi-Level Deep Polynomial Network

Motor imagery BCI is a system that is very useful to help people with disabilities who can't move their limbs. These systems use brain activity patterns that are made from motor imagery without actual movement. In this paper, we proposed enhanced One Versus One (OVO) structure to classify EEG-based multi-class motor imagery signals. Also, shrinkage estimator based Common Spatial Pattern (CSP) is used to overcome disadvantages of conventional CSP. Shrinkage estimator is a procedure to estimate covariance matrix that regularizes CSP versus overfitting. The results of four-class classification of BCI competition IV dataset 2a, show that the performance is improved to 0.61 kappa score.

Shrinkage estimator based common spatial pattern for multi-class motor imagery classification by hybrid classifier

Lower limb movement decoding is the primary objective for designing Brain-computer interface (BCI) controlled leg prosthesis. In this paper the main objective is to classify left-right leg movement directly from electroencephalography (EEG) signals by probabilistic Naive Bayesian approach. Wavelet based decomposition has been taken as feature extractor to take care of non-stationary nature of brain waves, that have been recorded from 12 healthy subjects. The proposed method achieved average accuracy of 78.33% with 11ms of execution time. Specificity, sensitivity, type 1 and type 2 error rate have also been determined for each case. Results of the classifier is compared with other standard classifiers and statistically validated by Friedman Test. Novelty of the paper lies in the fact that it considers the both frequency and spatial domain (location in time) features of EEG signal without sacrificing accuracy and very low execution time makes it feasible for real time application. It also shows the Naive Bayes classifier with uniform prior probability is better classifier than standard Naive Bayes classifier in recognizing left-right lower limb movement.

A Naïve Bayesian approach to lower limb classification from EEG signals

Motor imagery (MI) based on brain computer interfaces (BCIs) have been widely applied for upper limb motor rehabilitation. Due to the fact that a large number of disabled people need to restore or improve walking ability, it is also important to investigate the use of MI-based BCIs for lower limb motor rehabilitation. The brain activity of lower limb MI is more difficult to detect because of low reliability. The purpose of this study is to find a suitable paradigm of walking imagery to achieve better training effect and ensure reliable brain activity. We developed the text-based paradigm and the virtual environment (VE)-based paradigm, and evaluated their performance on identifying walking imagery from idle state.The experimental results provide evidences that the VE-based paradigm could improve the average classification accuracy. This paradigm would induce EEG patterns that make them easier for single-trial detection of walking imagery. This study has the potential to improve the reliability and robustness of walking imagery based BCIs.

Performance Evaluation of Walking Imagery Training Based on Virtual Environment in Brain-Computer Interfaces

Brain computer interface (BCI) is a direct communication pathway between the human central nervous system and external devices primarily aiming at restoring damaged functions such as sight, hearing and movement. Although great achievements have been made for the development of reliable BCI systems to assist people with upper-limb disabilities, researches on BCI development related to lower-limb are still rudimentary. In the current study, based on the regularized common spatial pattern analysis (R-CSP) method and statistical dependency, we have developed an improved feature selection method for lower-limb movement pattern classification. High-resolution electroencephalogram (EEG) signals were recorded from four healthy male subjects undergoing real lower-limb movements. Compared to the conventional CSP, R-CSP, and PCA methods, the proposed method achieved the best average accuracy of 83.5% for single trial classification of left and right lower-limb movement. Our findings thereby have insightful implications for developing practical BCI systems for lower-limb movement.

Single trial EEG classification of lower-limb movements using improved regularized common spatial pattern

Research on brain machine interface (BMI) has been developed very fast in recent years. Numerous feature extraction methods have successfully been applied to electroencephalogram (EEG) classification in various experiments. However, little effort has been spent on EEG based BMI systems regarding familiarity of human faces cognition. In this work, we have implemented and compared the classification performances of four common feature extraction methods, namely, common spatial pattern, principal component analysis, wavelet transform and interval features. High resolution EEG signals were collected from fifteen healthy subjects stimulated by equal number of familiar and novel faces. Principal component analysis outperforms other methods with average classification accuracy reaching 94.2% leading to possible real life applications. Our findings thereby may contribute to the BMI systems for face recognition.

Single trial EEG classification applied to a face recognition experiment using different feature extraction methods

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Single trial EEG classification of lower-limb movements using improved regularized common spatial pattern

Single trial EEG classification applied to a face recognition experiment using different feature extraction methods