Utilization of Genetic Algorithm for Optimal EEG Channel Selection in Brain-Computer Interface Application

Brain-Computer Interface (BCI) research has provoked an enormous interest among researchers from different fields since it is an important element in assistive technology. The most popular approach is a non-invasive method, using Electroencephalogram (EEG) analysis which acquires signals from 32 to 64 electrodes' recordings and translate them to a movement using various computing algorithm which can be used in wheelchair navigation, or control robot movements. However, it will be time consuming and an exhausting experience if the single command translation from large number of electrodes is used. The aim of this project is to develop an algorithm that can choose optimal four electrodes for signal recording, and convert one thought into multiple commands with the chosen electrodes. Using sample datasets, the EEG signal is analyzed to determine the most suitable scalp area for P300 detection, while optimization with genetic algorithm (GA) is developed to select best four channels. After 30 GA runs, the optimal four sets of electrodes are chosen based on their coefficient of determination or r2 values, where higher values contributes to higher repetition rates. Using signals from the chosen four electrodes, a success rate of 75-80% is received.