{"ID":2883833,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08124","arxiv_id":"2508.08124","title":"NeuroDx-LM: A Clinical Large-Scale Model for EEG-based Neurological Disorder Detection","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.","short_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 scenario...","url_abs":"https://arxiv.org/abs/2508.08124","url_pdf":"https://arxiv.org/pdf/2508.08124v1","authors":"[\"Guanghao Jin\",\"Yuan Liang\",\"Yihan Ma\",\"Jingpei Wu\",\"Guoyang Liu\"]","published":"2025-08-11T16:02:25Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":611023,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2883833,"paper_url":"https://arxiv.org/abs/2508.08124","paper_title":"NeuroDx-LM: A Clinical Large-Scale Model for EEG-based Neurological Disorder Detection","repo_url":"https://github.com/LetItBe12345/NeuroDx-LM","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}