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.