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Robot. AI"],"abstract":"<jats:p>Deep networks for electroencephalogram (EEG) decoding are often only trained to solve one specific task, such as pathology or age decoding. A more general task-agnostic approach is to train deep networks to match a (clinical) EEG recording to its corresponding textual medical report and <jats:italic>vice versa<\/jats:italic>. This approach was pioneered in the computer vision domain matching images and their text captions and subsequently allowed to do successful zero-shot decoding using textual class prompts. In this work, we follow this approach and develop a contrastive learning framework, EEG-CLIP, that aligns the EEG time series and the descriptions of the corresponding clinical text in a shared embedding space. We investigated its potential for versatile EEG decoding, evaluating performance in a range of few-shot and zero-shot settings. Overall, we show that EEG-CLIP manages to non-trivially align text and EEG representations. Our work presents a promising approach to learn general EEG representations, which could enable easier analyses of diverse decoding questions through zero-shot decoding or training task-specific models from fewer training examples. The code for reproducing our results is available at <jats:ext-link>https:\/\/github.com\/tidiane-camaret\/EEGClip<\/jats:ext-link>.<\/jats:p>","DOI":"10.3389\/frobt.2025.1625731","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T05:28:05Z","timestamp":1756186085000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["EEG-CLIP: learning EEG representations from natural language descriptions"],"prefix":"10.3389","volume":"12","author":[{"given":"Tidiane","family":"Camaret Ndir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robin T.","family":"Schirrmeister","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tonio","family":"Ball","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"key":"B2","first-page":"4171","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019"},{"key":"B3","article-title":"EEG-language modeling for pathology detection","author":"Gijsen","year":"2024"},{"key":"B4","first-page":"1039","article-title":"A large-scale evaluation framework for eeg deep learning architectures","author":"Heilmeyer","year":"2018"},{"key":"B5","article-title":"Clinicalbert: modeling clinical notes and predicting hospital readmission","author":"Huang","year":"2019","journal-title":"arXiv:1904.05342"},{"key":"B6","doi-asserted-by":"publisher","first-page":"479","DOI":"10.3389\/fnhum.2018.00479","article-title":"Early detection of hemodynamic responses using EEG: a hybrid EEG-fNIRS study","volume":"12","author":"Khan","year":"2018","journal-title":"Front. 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