Motor Imagery Classification Using TSK Fuzzy Inference Neural Networks

Electroencephalography (EEG) signals are often employed in modern BCI (brain-computer interface) systems. For patients with irreversible nerve injury, motor imagery BCIs enable them to control devices such as artificial limbs simply with brain activities (i.e. imagination) without physical movement. In practice, EEG signals are measured with multiple electrodes and often contaminated with various kinds of artifacts and noise. In this study, a novel EEG motor imagery classification approach is developed based on discrete wavelet transform (DWT) and TSK (Tagaki-Sugeno-Kang) fuzzy inference neural networks. By using energy ratios in the frequency bands that are related with motor imagery activities, fewer features are needed for the classifier, which makes the real-time implementation of BCI systems more feasible. DWT not only provides multi-resolution analysis for non-stationary EEG signals, but also aids for noise and artifact reduction. Furthermore, the TSK fuzzy neural network is able to handle imprecise information with adaptive learning. The proposed algorithm is tested on EEG signals of PhysioNet database and compared with other methods. Satisfactory computer simulation results are obtained.