Electroencephalography (EEG) signal processing to decode motor imagery (MI) involves high-dimensional features, which increases the computational complexity. To reduce this computational burden due to the large number of channels, an iterative multiobjective optimization for channel selection (IMOCS) is proposed in this paper. For a given MI classification task, the proposed method initializes a reference candidate solution and subsequently finds a set of the most relevant channels in an iterative manner by exploiting both the anatomical and functional relevance of EEG channels. The proposed approach is evaluated on the Wadsworth dataset for the right fist versus left fist MI tasks, while considering the cross-validation accuracy as the performance evaluation criteria. Furthermore, 12 other dimension reduction and channel selection algorithms are used for benchmarking. The proposed approach (IMOCS) achieved an average classification accuracy of about 80% when evaluated using 35 best-performing subjects. One-way analysis of variance revealed the statistical significance of the proposed approach with at least 7% improvement over other benchmarking algorithms. Furthermore, a cross-subject generalization of channel selection on untrained subjects shows that the subject-independent channels perform as good as using all channels achieving an average classification accuracy of 61%. These results are promising for the online brain-computer interface (BCI) paradigm that requires low computational complexity and also for reducing the preparation time while conducting multiple session BCI experiments for a larger pool of subjects.

Traumatic brain injury (TBI) often results in balance impairment, increasing the risk of falls, and the chances of further injuries. However, the underlying neural mechanisms of postural control after TBI are not well understood. To this end, we conducted a pilot study to explore the neural mechanisms of unpredictable balance perturbations in 17 chronic TBI participants and 15 matched healthy controls (HC) using the EEG, MRI, and diffusion tensor imaging (DTI) data. As quantitative measures of the functional integration and segregation of the brain networks during the postural task, we computed the global graph‐theoretic network measures (global efficiency and modularity) of brain functional connectivity derived from source‐space EEG in different frequency bands. We observed that the TBI group showed a lower balance performance as measured by the center of pressure displacement during the task, and the Berg Balance Scale (BBS). They also showed reduced brain activation and connectivity during the balance task. Furthermore, the decrease in brain network segregation in alpha‐band from baseline to task was smaller in TBI than HC. The DTI findings revealed widespread structural damage. In terms of the neural correlates, we observed a distinct role played by different frequency bands: theta‐band modularity during the task was negatively correlated with the BBS in the TBI group; lower beta‐band network connectivity was associated with the reduction in white matter structural integrity. Our future studies will focus on how postural training will modulate the functional brain networks in TBI.

In this article, we provide an overview of electroencephalography (EEG) source imaging (ESI) of movement decoding for brain-computer interface (BCI) applications. The current state-of-the-art neuroimaging modality-functional magnetic resonance imaging (fMRI)-is expensive and nonportable and has poor temporal resolution. EEG, however, offers an attractive choice as a portable and cost-effective neuroimaging technique that delivers excellent temporal resolution, especially in reading dynamic human motor behavior.

Objective: This article presents the design and validation of an accurate automatic diagnostic system to classify intramuscular EMG (iEMG) signals into healthy, myopathy, or neuropathy categories to aid the diagnosis of neuromuscular diseases. Method: First, an iEMG signal is decimated to produce a set of disjoint downsampled signals, which are decomposed by the lifting wavelet transform (LWT). The Higuchi's fractal dimensions (FDs) of LWT coefficients in the subbands are computed. The FDs of LWT subband coefficients are fused with one-dimensional local binary pattern derived from each downsampled signal. Next, a multilayer perceptron neural network (MLPNN) determines the class labels of downsampled signals. Finally, the sequence of class labels is fed to the Boyer-Moore majority vote (BMMV) algorithm, which assigns a class to every iEMG signal. Result: The MLPNN-BMMV classifier was experimented with 250 iEMG signals belonging to three categories. The performance of the classifier was validated in comparison with state-of-the-art approaches. The MLPNN-BMMV has resulted in impressive performance measures (%) using a 10-fold cross-validationaccuracy = 99.870.25, sensitivity (normal) = 99.970.13, sensitivity (myopathy) = 99.680.95, sensitivity (neuropathy) = 99.760.66, specificity (normal) = 99.720.61, specificity (myopathy) = 99.980.10, and specificity (neuropathy) = 99.960.14surpassing the existing approaches. Conclusion: A future research direction is to validate the classifier performance with diverse iEMG datasets, which would lead to the design of an affordable real-time expert system for neuromuscular disorder diagnosis.

Detection of multiple directional movements could be useful in designing a BCI based upper-limb rehabilitation system for stroke patients. Under the experiment protocol of voluntary right-hand center-out movement in four orthogonal directions, we will discuss how to classify the movement directions and speeds in the spatial-temporal-spectra domains by utilizing regularized wavelet-common spatial pattern, mutual information-based feature selection, adaptive trajectory tracking, and source localization.

Traumatic Brain Injury (TBI) often results in balance impairment, increasing the risk of falls, and the chances of further injuries. However, the underlying neurophysiological mechanisms of postural control after TBI are not well understood. To this end, we conducted a pilot study with a multimodal approach of EEG, MRI, and Diffusion Tensor Imaging (DTI) to explore the neural mechanisms of unpredictable balance perturbations in 17 chronic TBI participants and 15 matched Healthy Controls (HC). As quantitative measures of the functional integration and segregation of the brain networks during the postural task, we computed the global graph-theoretic network measures (global efficiency and modularity) of brain functional connectivity derived from source-space EEG in different frequency bands. We observed that the TBI group showed a lower balance performance as measured by the Center of Pressure (COP) displacement during the task, and the Berg Balance Scale. They also showed altered brain activation and connectivity during the balance task. In particular, the task modulation of brain network segregation in alpha-band was reduced in TBI. Moreover, the DTI findings revealed that the structural damage is associated with reduced network connectivity and integration. In terms of the neural correlates, we observed a distinct role played by different frequency bands; greater theta-band modularity during the task was strongly correlated with the BBS in TBI group; alpha-band and beta-band graph-theoretic measures were associated with the measures of white matter structural integrity. Our future studies will focus on how postural training will modulate the functional brain networks in TBI.