Achieving a reliable classification of motor imagery (MI) tasks is a major challenge in brain-computer interface (BCI) implementation. The set of relevant and discriminative features plays an important role in the classification scheme. This paper presents a supervised approach to select discriminative features for the enhancement of MI classification using multichannel electroencephalography (EEG) signal. The dimension of multiband feature space is reduced using the feature selection method. Each trial of the multichannel EEG signal representing MI tasks is decomposed into a finite set of narrowband signals. The common spatial pattern-based features are extracted from each subband. The features obtained from the multiple subbands are combined to derive a high-dimensional feature vector. The neighborhood component analysis-based feature selection method is implemented to select the features that are relevant in performing an accurate classification. It is a nearest-neighbor-based approach to learn the feature weights with regularization by maximizing the average leave-one-out classification accuracy over the labeled training data. The selected features are used to train the support vector machine for classification. The features relatively irrelevant to the classification task are discarded, yielding a reduction of feature dimension. The evaluation of the proposed method is performed using BCI Competition III dataset 4a and IV dataset 2b. Both are publicly available datasets and are used as types of benchmark data to evaluate the MI classification algorithm to implement BCI. The obtained simulation results confirm the superiority of the proposed method compared to the recently developed algorithms.INDEX TERMS Brain-computer interface (BCI), electroencephalography (EEG), machine learning, motor imagery (MI), subband decomposition, supervised feature selection.

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