Classification of Motor Imagery Tasks Using EEG Signal Analysis and Linear Discriminant Analysis

Brain-Computer Interfaces (BCIs) are at the forefront of technologies bridging human cognition with external devices by interpreting brain signals. This study introduces a novel framework for the binary classification of electroencephalogram (EEG) data, with an aim to enhance the generalizability of BCIs. Using BCI Competition IV dataset 2a, the research engages in preprocessing and feature extraction through the Common Spatial Pattern (CSP), to identify significant brain signal patterns. The efficacy of Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers was scrutinized, revealing that LDA slightly outperforms SVM with a classification accuracy mean of 74.00% across four subjects. Despite the need for further enhancements for real-time BCI applications, this framework shows substantial potential for practical uses, paving the way for improved interaction between neural processes and computational devices. The research aspires to develop universally adaptable Brain-Computer Interfaces (BCIs), enriching the adaptability of this technology and broadening the understanding of brain function.