{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T18:43:42Z","timestamp":1764355422094,"version":"3.46.0"},"reference-count":29,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,30]]},"DOI":"10.1109\/icme59968.2025.11210188","type":"proceedings-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T17:57:42Z","timestamp":1761847062000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["Harnessing Pre-trained Language Models for EEG-based Epilepsy Detection"],"prefix":"10.1109","author":[{"given":"Tao","family":"Lu","sequence":"first","affiliation":[{"name":"Guangdong Institute of Intelligence Science and Technology,Hengqin, Zhuhai,China,519031"}]},{"given":"Shangyang","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Institute of Intelligence Science and Technology,Hengqin, Zhuhai,China,519031"}]}],"member":"263","reference":[{"volume-title":"Niedermeyer\u2019s electroencephalography: basic principles, clinical applications, and related fields","year":"2011","author":"Niedermeyer","key":"ref1"},{"year":"2023","key":"ref2","article-title":"Epilepsy fact sheet"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24612-3_748"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000207127"},{"key":"ref5","first-page":"32039","article-title":"Atd: Augmenting cp tensor decomposition by self supervision","volume":"35","author":"Yang","year":"2022","journal-title":"Advances in neural information processing systems"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.622759"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103342"},{"key":"ref8","first-page":"1","article-title":"Training language models to follow instructions with human feedback, 2022","volume-title":"URL https:\/\/arxiv.org\/abs\/2203.02155","volume":"13","author":"Ouyang","year":"2022"},{"key":"ref9","article-title":"Biot: Biosignal transformer for cross-data learning in the wild","volume":"36","author":"Yang","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Vector quantization pretraining for eeg time series with random projection and phase alignment","volume-title":"Forty-first International Conference on Machine Learning","author":"Haokun","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-00123-9_54"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2021.653659"},{"article-title":"Maeeg: Masked auto-encoder for eeg representation learning","year":"2022","author":"Chien","key":"ref13"},{"key":"ref14","first-page":"43322","article-title":"One fits all: Power general time series analysis by pretrained lm","volume":"36","author":"Zhou","year":"2023","journal-title":"Advances in neural information processing systems"},{"article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","year":"2018","author":"Devlin","key":"ref15"},{"article-title":"Roberta: A robustly optimized bert pretraining approach","year":"2019","author":"Liu","key":"ref16"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20729"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-short.1"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/SPMB.2015.7405423"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/SPMB.2015.7405421"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000207127"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC48229.2022.9871916"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103342"},{"article-title":"Transformer-based spatial-temporal feature learning for eeg decoding","year":"2021","author":"Song","key":"ref24"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671600"},{"article-title":"The llama 3 herd of models","year":"2024","author":"Dubey","key":"ref26"},{"article-title":"Gemma 2: Improving open language models at a practical size","year":"2024","author":"Team","key":"ref27"},{"key":"ref28","first-page":"43322","article-title":"One fits all: Power general time series analysis by pretrained lm","volume":"36","author":"Zhou","year":"2023","journal-title":"Advances in neural information processing systems"},{"article-title":"Position: The platonic representation hypothesis","volume-title":"Forty-first International Conference on Machine Learning","author":"Huh","key":"ref29"}],"event":{"name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","start":{"date-parts":[[2025,6,30]]},"location":"Nantes, France","end":{"date-parts":[[2025,7,4]]}},"container-title":["2025 IEEE International Conference on Multimedia and Expo (ICME)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11208895\/11208897\/11210188.pdf?arnumber=11210188","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T18:39:44Z","timestamp":1764355184000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11210188\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/icme59968.2025.11210188","relation":{},"subject":[],"published":{"date-parts":[[2025,6,30]]}}}