Brain-Computer Interface (BCI) Based on the EEG Signal Decomposition Butterfly Optimization and Machine Learning

Abstract

The Brain-Computer Interface (BCI) is a technology that helps disabled people to operate assistive devices bypassing neuromuscular channels. This study aims to process the Electroencephalography (EEG) signals and then translate these signals into commands by analyzing and categorizing them with Machine Learning algorithms. The findings can be onward used to control an assistive device. The significance of this project lies in assisting those with severe motor impairment, paralysis, or those who lost their limbs to be independent and confident by controlling their environment and offering them alternative ways of communication. The acquired EEG signals are digitally low-pass filtered and decimated. Onward, the wavelet decomposition is used for signal analysis. The features are mined from the obtained sub-bands. The dimension of extracted feature set is reduced by using the Butterfly Optimization algorithm. The Selected feature set is then processed by the classifiers. The performance of k-Nearest Neighbor, Support Vector Machine and Artificial Neural Network is compared for the categorization of motor imagery tasks by processing the selected feature set. The suggested method secures a highest accuracy score of 83.7% for the case of k-Nearest Neighbor classifier.