Optimal EEG Channel Selection for Motor Imagery BCI System Using BPSO and GA

Abstract

A motor imagery brain-computer interface system is used to translate a subject’s intention into a control command of machine, such as electrical wheelchair, robot manipulator, and so on. The overall process of classification of the motor imagery EEG signals is based on the acquisition of raw data from multiple channel of scalp when the subject tries to imagine the movement of limbs. So far, we have been concentrated which channel are activated by the imagination of the movement of limbs. Therefore, we have expected that the more channels are selected, the better results can be acquired. However, the problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is a spatial feature extraction, many channels cause an overfitting problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization (BPSO) as an optimal channel selection method. This paper examines selecting optimal channels and their combination, and comparing accuracy and the number of selected channels obtained from BPSO and simple genetic algorithm.