EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
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| Format: | Preprint |
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2025
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| author | Aristimunha, Bruno Truong, Dung Guetschel, Pierre Shirazi, Seyed Yahya Guyon, Isabelle Franco, Alexandre R. Milham, Michael P. Dotan, Aviv Makeig, Scott Gramfort, Alexandre King, Jean-Remi Corsi, Marie-Constance Valdés-Sosa, Pedro A. Majumdar, Amit Evans, Alan Sejnowski, Terrence J Shriki, Oren Chevallier, Sylvain Delorme, Arnaud |
| author_facet | Aristimunha, Bruno Truong, Dung Guetschel, Pierre Shirazi, Seyed Yahya Guyon, Isabelle Franco, Alexandre R. Milham, Michael P. Dotan, Aviv Makeig, Scott Gramfort, Alexandre King, Jean-Remi Corsi, Marie-Constance Valdés-Sosa, Pedro A. Majumdar, Amit Evans, Alan Sejnowski, Terrence J Shriki, Oren Chevallier, Sylvain Delorme, Arnaud |
| contents | Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenges. First, the Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data. Second, the Psychopathology factor prediction Challenge asks participants to infer subject measures of mental health from EEG data. For this, we use an unprecedented, multi-terabyte dataset of high-density EEG signals (128 channels) recorded from over 3,000 child to young adult subjects engaged in multiple active and passive tasks. We provide several tunable neural network baselines for each of these two challenges, including a simple network and demographic-based regression models. Developing models that generalise across tasks and individuals will pave the way for ML network architectures capable of adapting to EEG data collected from diverse tasks and individuals. Similarly, predicting mental health-relevant personality trait values from EEG might identify objective biomarkers useful for clinical diagnosis and design of personalised treatment for psychological conditions. Ultimately, the advances spurred by this challenge could contribute to the development of computational psychiatry and useful neurotechnology, and contribute to breakthroughs in both fundamental neuroscience and applied clinical research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding Aristimunha, Bruno Truong, Dung Guetschel, Pierre Shirazi, Seyed Yahya Guyon, Isabelle Franco, Alexandre R. Milham, Michael P. Dotan, Aviv Makeig, Scott Gramfort, Alexandre King, Jean-Remi Corsi, Marie-Constance Valdés-Sosa, Pedro A. Majumdar, Amit Evans, Alan Sejnowski, Terrence J Shriki, Oren Chevallier, Sylvain Delorme, Arnaud Signal Processing Machine Learning Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenges. First, the Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data. Second, the Psychopathology factor prediction Challenge asks participants to infer subject measures of mental health from EEG data. For this, we use an unprecedented, multi-terabyte dataset of high-density EEG signals (128 channels) recorded from over 3,000 child to young adult subjects engaged in multiple active and passive tasks. We provide several tunable neural network baselines for each of these two challenges, including a simple network and demographic-based regression models. Developing models that generalise across tasks and individuals will pave the way for ML network architectures capable of adapting to EEG data collected from diverse tasks and individuals. Similarly, predicting mental health-relevant personality trait values from EEG might identify objective biomarkers useful for clinical diagnosis and design of personalised treatment for psychological conditions. Ultimately, the advances spurred by this challenge could contribute to the development of computational psychiatry and useful neurotechnology, and contribute to breakthroughs in both fundamental neuroscience and applied clinical research. |
| title | EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2506.19141 |