Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection
Md. Milon Hossain, Md. Nurul Ahad Tawhid
TL;DR
This paper introduces an explainable AI framework using EEG data to detect schizophrenia, highlighting brain lobe-specific insights and achieving high diagnostic accuracy.
Contribution
The novel contribution is a brain lobe-specific, explainable AI framework for schizophrenia detection using EEG data and mel-spectrogram images.
Findings
The framework achieved 99.82% accuracy on the repOD dataset and 98.31% on the kaggle basic sensory task dataset.
The frontal lobe showed the highest diagnostic accuracy (97.02% and 88.03%), while the occipital lobe had the lowest (79.30% and 68.33%).
XAI techniques like LIME, SHAP, and Grad-CAM were used to enhance the explainability of the model's decisions.
Abstract
Schizophrenia (ScZ) is a growing global health concern that affects millions of people and puts severe pressure on healthcare systems. Early detection and accurate diagnosis are crucial for adequate management. Electroencephalography (EEG) has evolved into a promising non-invasive tool for detecting ScZ in contemporary research. However, specific biomarkers, especially those related to brain lobes, cannot often be identified by current EEG-based diagnostic methods. Different brain lobes are associated with distinct cognitive functions and patterns of diseases. Also, there is a gap in the incorporation of the explainable artificial intelligence (XAI) technique, as medical diagnosis needs trustworthiness and explainability. This study strives to address these gaps by developing a framework using mel-spectrogram images with Convolutional Neural Networks (CNNs). EEG signals are converted…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces · Functional Brain Connectivity Studies · Emotion and Mood Recognition