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 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. Additionally, we incorporate Tangent Space Mapping (TSM) to make the covariance matrices more suitable for traditional classifiers by projecting them into a space that preserves their geometric properties. Empirical results on public EEG datasets show that CBR, using covariance matrices with TSM, achieves the best accuracy of 0.72 for Alzheimer’s Disease (AD) and up to 0.83 for Parkinson’s Disease (PD).