{"ID":2823205,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00573","arxiv_id":"2601.00573","title":"Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models","abstract":"Event-related potential (ERP), a specialized paradigm of electroencephalographic (EEG), reflects neurological responses to external stimuli or events, generally associated with the brain's processing of specific cognitive tasks. ERP plays a critical role in cognitive analysis, the detection of neurological diseases, and the assessment of psychological states. Recent years have seen substantial advances in deep learning-based methods for spontaneous EEG and other non-time-locked task-related EEG signals. However, their effectiveness on ERP data remains underexplored, and many existing ERP studies still rely heavily on manually extracted features. In this paper, we conduct a comprehensive benchmark study that systematically compares traditional manual features (followed by a linear classifier), deep learning models, and pre-trained EEG foundation models for ERP analysis. We establish a unified data preprocessing and training pipeline and evaluate these approaches on two representative tasks, ERP stimulus classification and ERP-based brain disease detection, across 12 publicly available datasets. Furthermore, we investigate various token-embedding strategies within advanced Transformer architectures to identify embedding designs that better suit ERP data. Our study provides a landmark framework to guide method selection and tailored model design for future ERP analysis. The code is available at https://github.com/DL4mHealth/ERP-Benchmark","short_abstract":"Event-related potential (ERP), a specialized paradigm of electroencephalographic (EEG), reflects neurological responses to external stimuli or events, generally associated with the brain's processing of specific cognitive tasks. ERP plays a critical role in cognitive analysis, the detection of neurological diseases, an...","url_abs":"https://arxiv.org/abs/2601.00573","url_pdf":"https://arxiv.org/pdf/2601.00573v2","authors":"[\"Yihe Wang\",\"Zhiqiao Kang\",\"Bohan Chen\",\"Yu Zhang\",\"Xiang Zhang\"]","published":"2026-01-02T05:19:39Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.CE\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":605480,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2823205,"paper_url":"https://arxiv.org/abs/2601.00573","paper_title":"Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models","repo_url":"https://github.com/DL4mHealth/ERP-Benchmark","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}