Covariance Matrices and Case-Based Reasoning Synergy for Interpretable EEG Classification in Neurological Disorders

This work introduces a novel methodology for Electroencephalography (EEG) data analysis in the context of neurological diseases, emphasizing feature extraction through covariance matrices and their integration with Case-Based Reasoning (CBR). Departing from traditional techniques such as Fast Fourier Transform (FFT) and statistical analysis, we investigate the synergy between covariance matrices and CBR, highlighting their potential to improve the interpretability and efficacy of EEG data analysis over conventional methods like Random Forest (RF). Covariance matrices analyze the relationships between channels, indirectly capturing interactions between brain regions, while CBR uses similarities in these relationship patterns across cases to make decisions, both techniques focusing on understanding the data through its interrelationships. Furthermore, we explore the impact of using data windows in the analysis pipeline to assess their influence on covariance matrix feature extraction and subsequent classification performance in the context of neurological disease diagnosis. Empirical results on public EEG datasets show that CBR, using covariance matrices without temporal windows, achieved the best accuracy, with 0.64 for Alzheimer’s Disease (AD) and up to 0.83 for Parkinson’s Disease (PD), outperforming RF.