Fisher Linear Classifier Implementation for EEG Signal Processing

I developed a Fisher Linear Classifier using MATLAB for processing electroencephalogram (EEG) signals, utilizing datasets from the BCI competition. The implementation incorporates the Fisher Linear Discriminant Analysis (LDA) algorithm, which projects high-dimensional EEG data onto an optimal linear subspace to separate different signal classes. Through feature extraction using statistical measures (like mean amplitude and frequency band power) and subsequent classification, the system identifies distinct characteristics and patterns in EEG signals. The classifier architecture involves calculating between-class and within-class scatter matrices to determine the optimal projection direction that maximizes class separation. Key MATLAB functions used include cov() for covariance matrix computation and eig() for eigenvalue decomposition to solve the generalized eigenvalue problem. This classifier can be applied to various brain-computer interface applications, such as BCI control systems and neurological diagnostics. By implementing this Fisher Linear Classifier, researchers can gain deeper insights into EEG signal analysis, enabling more accurate pattern recognition and expanding possibilities for neurological research and clinical applications. The code structure allows for easy adaptation to different EEG datasets and classification tasks through parameter optimization and feature selection modules.