AI-enhanced EEG signal interpretation: A novel approach using texture analysis with random forests

We hypothesize that the Gray-Level Co-occurrence Matrix (GLCM) and the Run-Length Matrix (RLM) techniques can effectively quantify discrete changes in EEG signals, and that the features extracted from these matrices can be utilized to train a Random Forest (RF) model. Our contribution includes the development of a robust code in sci-kit learn for a hypothetical model that, after adequate training and testing, could be used to detect and remove artifacts as well as differentiate between physiological and pathological EEG signals. Moreover, our approach envisions the RF model as a powerful tool capable of differentiating between normal and abnormal EEG signals. This approach could lead to the development of more potent AI tools that enhance clinical decision-making in neurology and psychiatry.