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Highlights• The paper proposes a Kernel Partial Least Square (KPLS) based Feature Selection Method aiming for easy computation and improving classification accuracy for high dimensional data.• The proposed method makes use of KPLS regression coefficients to identify an optimal set of features, thus avoiding non-linear optimization.• Experiments were carried out on seven real life datasets with four different classifiers: SVM, LDA, Random Forest and Naïve Bayes.• Experimental results highlight the advantage of the proposed method over several competing feature selection techniques.

Objective: Electroencephalogram (EEG) signals are non-stationary. This could be due to internal fluctuation of brain states such as fatigue, frustration, etc. This necessitates the development of adaptive Brain-Computer Interfaces (BCI) whose performance does not deteriorate significantly with the adversary change in the cognitive state. In this paper, we put forward an unsupervised adaptive scheme to adapt the feature extractor of motor imagery (MI) BCIs by tracking the fatigue level of the user. Approach: Eleven subjects participated in the study during which they accomplished MI tasks while self-reporting their perceived levels of mental fatigue. Out of the 11 subjects, only 6 completed the whole experiment, while the others quit in the middle because of experiencing high fatigue. The adaptive feature extractor is attained through the adaptation of the common spatial patterns (CSP), one of the most popular feature extraction algorithms in EEG-based BCIs. The proposed method was analyzed in two ways: offline and in near real-time. The separability of the MI EEG features extracted by the proposed adaptive CSP (ADCSP) has been compared with that by the conventional CSP (C-CSP) and another CSP based adaptive method (ACSP) in terms of: Davies Bouldin Index (DBI), Fisher Score (FS) and Dunn's Index (DI). Results: Experimental results show significant improvement in the separability of MI EEG features extracted by ADCSP as compared to that by C-CSP and ACSP. Significance: Collectively, the results of the experiments in this study suggest that adapting CSP based on mental fatigue can improve the class separability of MI EEG features.

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