Motor Imagery Brain-computer Interface System Research Articles

A novel method for classification of BCI multi-class motor imagery task based on Dempster–Shafer theory

Brain-computer interface (BCI) is a promising technology to help disabled people to interact with the world only through their brain signals. These systems are designed based on recognizing the patient’s intention by processing his brain signal measurements. The essence of Motor-Imagery (MI) BCI systems is to train a model which classifies the brain signals into several major motions using several training sessions. Current methods mainly employ common spatial patterns (CSP) algorithm to extract meaningful features from the brain signals. Since the CSP algorithm primarily is designed for a two-class paradigm, often data fusion techniques are used to combine the results of CSP-based binary classifiers in multi-class problems. Noting the critical drawback of current data fusion methods due to losing information when combining the results of binary classifiers, here we propose a novel method based on Dempster–Shafer theory to fuse the results of constituent binary classifiers. We applied the proposed method to the benchmark BCI competition iv set 2a data set. The results show a significant improvement, which prove success of our method in modeling uncertainty. More specifically, the average kappa value is 75% which is higher than other competing methods. The proposed method is general and applicable to a wide range of applications.

Automated Selection of a Channel Subset Based on the Genetic Algorithm in a Motor Imagery Brain-Computer Interface System

Brain-computer interface (BCI) is a system for communication and control between the human brain and computers or other electronic devices. However, multichannel signal acquisition, which is time-consuming, laborious, and not conducive to subsequent real-time signal processing, can cause channel redundancy. When designing a BCI system, the selection of the optimal channels that match the expected pattern of potential cortical activity is useful for classifying brain activity during a mental task. The Stockwell transform and Bayesian linear discriminant analysis were applied to feature extraction and classification, respectively, and a genetic algorithm (GA) was used in the process of channel selection to extract the most relevant channel for classification. The superior performance of the algorithm is demonstrated by the test results on BCI Competition III dataset I. For a motor imagery paradigm, we show that the number of used channels can be reduced significantly, and the classification performance of the classifier can be improved. By comparing the performances of the algorithm with or without channel selection, the best channel combination was selected, and only 28 out of the 64 acquisition electrodes were used to realize classification sensitivity, specificity, precision, accuracy and Kappa coefficient values of 98%, 96%, 96.08%, 97% and 0.94, respectively, thereby outperforming existing algorithms. The proposed algorithm can reduce the number of channels and select the best channel combination to improve the classification performance, thereby overcoming the redundant channel problem. In addition, the signal processing framework that is adopted by this research can serve as a reference for related BCI application system research.

Signal processing techniques for motor imagery brain computer interface: A review

Abstract Motor Imagery Brain Computer Interface (MI-BCI) provides a non-muscular channel for communication to those who are suffering from neuronal disorders. The designing of an accurate and reliable MI-BCI system requires the extraction of informative and discriminative features. Common Spatial Pattern (CSP) has been potent and is widely used in BCI for extracting features in motor imagery tasks. The classifiers translate these features into device commands. Many classification algorithms have been devised, among those Support Vector Machine (SVM) and Linear Discriminate Analysis (LDA) have been widely used. In recent studies, the researchers are using deep neural networks for the classification of motor imagery tasks. This paper provides a comprehensive review of dominant feature extraction methods and classification algorithms in brain-computer interface for motor imagery tasks. Authors discuss existing challenges in the domain of motor imagery brain-computer interface and suggest possible research directions.

Separated channel convolutional neural network to realize the training free motor imagery BCI systems

In the recent context of Brain-computer interface (BCI), it has been widely known that transferring the knowledge of existing subjects to a new subject can effectively alleviate the extra training burden of BCI users. In this paper, we introduce an end-to-end deep learning framework to realize the training free motor imagery (MI) BCI systems. Specifically, we employ the common space pattern (CSP) extracted from electroencephalography (EEG) as the handcrafted feature. Instead of log-energy, we use the multi-channel series in CSP space to retain the temporal information. Then we propose a separated channel convolutional network, here termed SCCN, to encode the multi-channel data. Finally, the encoded features are concatenated and fed into a recognition network to perform the final MI task recognition. We compared the results of the deep model with classical machine learning algorithms, such as k-nearest neighbors (KNN), logistics regression (LR), linear discriminant analysis (LDA), and support vector machine (SVM). Moreover, the quantitative analysis was evaluated on our dataset and the BCI competition IV-2b dataset. The results have shown that our proposed model can improve the accuracy of EEG based MI classification (2–13% improvement for our dataset and 2–15% improvement for BCI competition IV-2b dataset) in comparison with traditional methods under the training free condition.

Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher's Criterion-Based Channel Selection.

Motor imagery is based on the volitional modulation of sensorimotor rhythms (SMRs); however, the sensorimotor processes in patients with amyotrophic lateral sclerosis (ALS) are impaired, leading to degenerated motor imagery ability. Thus, motor imagery classification in ALS patients has been considered challenging in the brain–computer interface (BCI) community. In this study, we address this critical issue by introducing the Grassberger–Procaccia and Higuchi’s methods to estimate the fractal dimensions (GPFD and HFD, respectively) of the electroencephalography (EEG) signals from ALS patients. Moreover, a Fisher’s criterion-based channel selection strategy is proposed to automatically determine the best patient-dependent channel configuration from 30 EEG recording sites. An EEG data collection paradigm is designed to collect the EEG signal of resting state and the imagination of three movements, including right hand grasping (RH), left hand grasping (LH), and left foot stepping (LF). Five late-stage ALS patients without receiving any SMR training participated in this study. Experimental results show that the proposed GPFD feature is not only superior to the previously-used SMR features (mu and beta band powers of EEG from sensorimotor cortex) but also better than HFD. The accuracies achieved by the SMR features are not satisfactory (all lower than 80%) in all binary classification tasks, including RH imagery vs. resting, LH imagery vs. resting, and LF imagery vs. resting. For the discrimination between RH imagery and resting, the average accuracies of GPFD in 30-channel (without channel selection) and top-five-channel configurations are 95.25% and 93.50%, respectively. When using only one channel (the best channel among the 30), a high accuracy of 91.00% can still be achieved by the GPFD feature and a linear discriminant analysis (LDA) classifier. The results also demonstrate that the proposed Fisher’s criterion-based channel selection is capable of removing a large amount of redundant and noisy EEG channels. The proposed GPFD feature extraction combined with the channel selection strategy can be used as the basis for further developing high-accuracy and high-usability motor imagery BCI systems from which the patients with ALS can really benefit.

Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system

Abstract In this study, three popular signal processing techniques (Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the decomposition of Electroencephalography (EEG) Signals in Brain Computer Interface (BCI) system for a classification task. Publicly available BCI competition III dataset IVa, a multichannel 2-class motor-imagery dataset, was used for this purpose. Multiscale Principal Component Analysis method was applied for the purpose of noise removal. In addition, different sets of features were formed to examine the effect of a particular group of features. The parameter selection process for signal decomposition methods was thoroughly explained as well. Our results show that the combination of Multiscale Principal Component Analysis de-noising and higher order statistics features extracted from wavelet packet decomposition sub-bands resulted in highest average classification accuracy of 92.8%. Our study is one among very few that provides a comprehensive comparison between signal decomposition methods in combination with higher order statistics in classification of BCI signals. In addition, we stressed the importance of higher frequency ranges in improving the classification task for EEG signals in Brain Computer Interface Systems. Obtained results indicate that the proposed model has the potential to obtain a reliable classification of motor imagery EEG signals, and can thus be used as a practical system for controlling a wheelchair. It can also further enhance the current rehabilitation therapies where appropriate feedback is delivered once the individual executes the correct movement. In that way, motor rehabilitation outcomes may improve over time.

Electroencephalographic Motor Imagery Brain Connectivity Analysis for BCI: A Review.

Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface (MI-BCI) for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established for a completely locked-in subject. To provide more useful and informative features, it has been recommended to take into account the relationships among electroencephalographic (EEG) sensor/source signals in the form of brain connectivity as an efficient tool of neuroscience. In this review, we briefly report the challenges and limitations of conventional MI-BCIs. Brain connectivity analysis, particularly functional and effective, has been described as one of the most promising approaches for improving MI-BCI performance. An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed. We subsequently discuss the brain connectomes during left and right hand, feet, and tongue MI movements. Moreover, key components involved in brain connectivity analysis that considerably affect the results are explained. Finally, possible technical shortcomings that may have influenced the results in previous research are addressed and suggestions are provided.

A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke.

Electroencephalography (EEG)-based motor imagery (MI) brain-computer interface (BCI) technology has the potential to restore motor function by inducing activity-dependent brain plasticity. The purpose of this study was to investigate the efficacy of an EEG-based MI BCI system coupled with MIT-Manus shoulder-elbow robotic feedback (BCI-Manus) for subjects with chronic stroke with upper-limb hemiparesis. In this single-blind, randomized trial, 26 hemiplegic subjects (Fugl-Meyer Assessment of Motor Recovery After Stroke [FMMA] score, 4-40; 16 men; mean age, 51.4 years; mean stroke duration, 297.4 days), prescreened with the ability to use the MI BCI, were randomly allocated to BCI-Manus or Manus therapy, lasting 18 hours over 4 weeks. Efficacy was measured using upper-extremity FMMA scores at weeks 0, 2, 4 and 12. ElEG data from subjects allocated to BCI-Manus were quantified using the revised brain symmetry index (rBSI) and analyzed for correlation with the improvements in FMMA score. Eleven and 15 subjects underwent BCI-Manus and Manus therapy, respectively. One subject in the Manus group dropped out. Mean total FMMA scores at weeks 0, 2, 4, and 12 weeks improved for both groups: 26.3±10.3, 27.4±12.0, 30.8±13.8, and 31.5±13.5 for BCI-Manus and 26.6±18.9, 29.9±20.6, 32.9±21.4, and 33.9±20.2 for Manus, with no intergroup differences (P=.51). More subjects attained further gains in FMMA scores at week 12 from BCI-Manus (7 of 11 [63.6%]) than Manus (5 of 14 [35.7%]). A negative correlation was found between the rBSI and FMMA score improvement (P=.044). BCI-Manus therapy was well tolerated and not associated with adverse events. In conclusion, BCI-Manus therapy is effective and safe for arm rehabilitation after severe poststroke hemiparesis. Motor gains were comparable to those attained with intensive robotic therapy (1,040 repetitions/session) despite reduced arm exercise repetitions using EEG-based MI-triggered robotic feedback (136 repetitions/session). The correlation of rBSI with motor improvements suggests that the rBSI can be used as a prognostic measure for BCI-based stroke rehabilitation.

Gamma band activity associated with BCI performance: simultaneous MEG/EEG study

While brain computer interface (BCI) can be employed with patients and healthy subjects, there are problems that must be resolved before BCI can be useful to the public. In the most popular motor imagery (MI) BCI system, a significant number of target users (called “BCI-Illiterates”) cannot modulate their neuronal signals sufficiently to use the BCI system. This causes performance variability among subjects and even among sessions within a subject. The mechanism of such BCI-Illiteracy and possible solutions still remain to be determined. Gamma oscillation is known to be involved in various fundamental brain functions, and may play a role in MI. In this study, we investigated the association of gamma activity with MI performance among subjects. Ten simultaneous MEG/EEG experiments were conducted; MI performance for each was estimated by EEG data, and the gamma activity associated with BCI performance was investigated with MEG data. Our results showed that gamma activity had a high positive correlation with MI performance in the prefrontal area. This trend was also found across sessions within one subject. In conclusion, gamma rhythms generated in the prefrontal area appear to play a critical role in BCI performance.