EEG Signal Channel Selection using Genetic Algorithm

Abstract

This thesis proposes a novel approach for selecting the optimal subset of EEG channels that can best discriminate between different types of events. The proposed method combines common spatial pattern (CSP) filtering, artifact removal, linear discriminant analysis (LDA) classification, and genetic algorithm (GA) optimization. The proposed methodology involves several steps, including preprocessing, event separation, artifact removal, initial population creation, fitness calculation, genetic algorithm iteration, and validation. The aim is to find the best set of EEG channels that can best discriminate between different types of events using a combination of machine learning and genetic algorithm techniques. The proposed approach has the potential to enhance the accuracy and reliability of EEG signal analysis, while considering the individual variability, spatial resolution, and frequency band of the signal. The approach is tested on a publicly available dataset BCI Competition IV Datasets 2a and 2b, and the results show that it outperforms existing methods in the literature with the accuracy of 87.40 % at 16 channels. Overall, the proposed methodology has important implications for advancing the state-of-the-art in EEG signal channel selection and improving the interpretability and clinical utility of EEGbased brain-computer interfaces.