AUTHOR=Almadhor Ahmad , Ojo Stephen , Nathaniel Thomas I. , Alsubai Shtwai , Alharthi Abdullah , Hejaili Abdullah Al , Sampedro Gabriel Avelino 

TITLE=An interpretable XAI deep EEG model for schizophrenia diagnosis using feature selection and attention mechanisms

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1630291

DOI=10.3389/fonc.2025.1630291

ISSN=2234-943X

ABSTRACT=IntroductionSchizophrenia is a severe psychological disorder that significantly impacts an individual’s life and is characterized by abnormalities in perception, behavior, 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.MethodsThis 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.ResultsThrough 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 and accuracy of 0.68%.DiscussionThe results show that the recommended approach is useful for Schizophrenia diagnosis.