Enhancing Trust in EEG-based Depression Classification: A LIME-powered XAI Approach to Dissecting Deep Learning Black Box Results

Depression is a widespread mental health disorder affecting millions worldwide. While deep learning models have shown promise in detecting depression using neurophysiological data, lack of result interpretability hinders clinical adoption. In this work, a new method of combining a 2D Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) depression diagnosis model based on EEG signals with Explainable AI (XAI) methods, specifically LIME. The integration provides transparency into decision-making of the model and mitigates the “black box” problem inherent in many AI systems. The results demonstrate the model’s high interpretability through clinically relevant explanations in model predictions in depression detection. This approach enhances the transparency and trustworthiness of AI-based depression diagnosis, potentially facilitating its integration into clinical practice. The paper discusses the implications of XAI in mental health diagnostics and its role in advancing responsible AI in healthcare.