Fuzzy system with tabu search learning for classification of motor imagery data

•Combine wavelet transform and Wilcoxon statistics for EEG signal feature extraction.•Introduce fuzzy standard additive model with tabu search learning for classification.•Proposed tabu-FSAM method considerably dominates competitive classifiers.•Tabu-FSAM outperforms the best performance reported in the BCI competition II.•Proposed method can be implemented into a real-time EEG signal analysis system.

This paper introduces an approach to classify EEG signals using wavelet transform and a fuzzy standard additive model (FSAM) with tabu search learning mechanism. Wavelet coefficients are ranked based on statistics of the Wilcoxon test. The most informative coefficients are assembled to form a feature set that serves as inputs to the tabu-FSAM. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed tabu-FSAM method considerably dominates the competitive classifiers, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II.