A Binary PSO-Based Optimal EEG Channel Selection Method for a Motor Imagery Based BCI System

Brain-computer interface based on motor imagery is a system that transforms a subject’s intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject’s limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. Using many channels cause other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfitting problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a particle swarm optimization applied to CSP. This paper examines selecting optimal channels among all channels, and comparing the classification accuracy between CSP and CSP with PSO by linear discriminant analysis.