NeuroDx-LM: A Clinical Large-Scale Model for EEG-based Neurological Disorder Detection

• URL: http://arxiv.org/abs/2508.08124v1
• Date: Mon, 11 Aug 2025 16:02:25 GMT
• Title: NeuroDx-LM: A Clinical Large-Scale Model for EEG-based Neurological Disorder Detection
• Authors: Guanghao Jin, Yuan Liang, Yihan Ma, Jingpei Wu, Guoyang Liu,
• Abstract summary: Large-scale models pre-trained on Electroencephalography (EEG) have shown promise in clinical applications such as neurological disorder detection.<n>NeuroDx-LM is a novel large-scale model specifically designed for detecting EEG-based neurological disorders.
• Score: 7.185477956123345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale models pre-trained on Electroencephalography (EEG) have shown promise in clinical applications such as neurological disorder detection. However, the practical deployment of EEG-based large-scale models faces critical challenges such as limited labeled EEG data and suboptimal performance in clinical scenarios. To address these issues, we propose NeuroDx-LM, a novel large-scale model specifically designed for detecting EEG-based neurological disorders. Our key contributions include (i) a Selective Temporal-Frequency Embedding mechanism that adaptively captures complex temporal and spectral patterns in EEG signals; and (ii) a Progressive Feature-Aware Training strategy that refines feature representation in a two-stage process. In the first stage, our model learns the fundamental discriminative features of EEG activities; in the second stage, the model further extracts more specialized fine-grained features for accurate diagnostic performance. We evaluated NeuroDx-LM on the CHB-MIT and Schizophrenia datasets, achieving state-of-the-art performance in EEG-based seizure and schizophrenia detection, respectively. These results demonstrate the great potential of EEG-based large-scale models to advance clinical applicability. Our code is available at https://github.com/LetItBe12345/NeuroDx-LM.

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