AI-driven Psychological Analysis through EEG Signal Interpretation using Python

Abstract

Psychological analysis through EEG (Electroencephalography) captures electrical activity in the brain, providing insights into cognitive processes, emotions, and mental states. Integrating Artificial Intelligence (AI) with EEG analysis using Python offers a novel approach to enhance diagnostic accuracy and personalize mental health care. Existing psychological assessment methods often rely on subjective evaluations and self-reported data, hindering early detection and accurate diagnosis of mental health disorders. Utilizing AI-driven algorithms to analyze EEG signals, providing objective insights into psychological states. This research aims to leverage AI-driven techniques for interpreting EEG signals to predict psychological states and diagnose disorders. We evaluate the performance of various machine learning classifiers, including Decision Trees, Logistic Regression, K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM). Results indicate classification accuracies between 65% and 80%, with KNN achieving the highest accuracy for "Liking," highlighting the potential of AI-enhanced EEG analysis in mental health diagnostics.