An Interpretable XAI Deep EEG Model for Schizophrenia Diagnosis Using Feature Selection and Attention Mechanisms

Ahmad Almadhor^1*Stephen Ojo²Thomas Nathaniel³Shtwai Alsubai⁴Abdullah Alharthi⁵Abdullah Al Hejaili⁶Gabriel Avelino Sampedro⁷

• ¹Jouf University, Sakakah, Saudi Arabia
• ²Anderson University, Anderson, United States
• ³University of South Carolina, South Carolina, United States
• ⁴Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia
• ⁵king khalid university, Abha, Saudi Arabia
• ⁶University of Tabuk, Tabuk, Saudi Arabia
• ⁷University of the Philippines Diliman, Quezon City, Philippines

Schizophrenia is a severe psychological disorder that significantly impacts an individual's life and is characterized by abnormalities in perception, behaviour, and cognition. Conventional Schizophrenia diagnosis techniques are time-consuming and prone to error. The study proposes a novel automated technique for diagnosing Schizophrenia based on electroencephalogram (EEG) sensor data, aiming to enhance interpretability and prediction performance. This research utilizes Deep Learning (DL) models, including the Deep Neural Network (DNN), Bi-Directional Long Short-Term Memory-Gated Recurrent Unit (BiLSTM-GRU), and BiLSTM with Attention, for the detection of Schizophrenia based on EEG data. During preprocessing, SMOTE is applied to address the class imbalance. Important EEG characteristics that influence model decisions are highlighted by the interpretable BiLSTM-Attention model using attention weights in conjunction with SHAP and LIME explainability tools. In addition to fine-tuning input dimensionality, F-test feature selection increases learning efficiency. Through the integration of feature importance analysis and conventional performance measures, this study presents valuable insights into the discriminative neurophysiological patterns associated with Schizophrenia, advancing both diagnostic and neuroscientific expertise. The experiment's findings show that the BiLSTM with attention mechanism model provides an accuracy of 0.68%. The promising results show that the recommended approach is useful for Schizophrenia diagnosis.