信息工程大学学报2025，Vol.26Issue(6)：674-682,9.DOI:10.3969/j.issn.1671-0673.2025.06.007
脑电大模型研究进展综述
A Review of Research Progress on Electroencephalogram Large Models
摘要
Abstract
Electroencephalogram(EEG)large models have emerged as a significant research direction in EEG analysis due to their exceptional representation learning capabilities.To systematically review progress in this field,the core theories and key technologies underlying these models are first analyzed,examining their theoretical foundations and design principles.Subsequently,a comprehensive compari-son of mainstream EEG large models is conducted across dimensions including model concepts,archi-tectural designs,feature extraction methods,learning paradigms,and model scales.A particular em-phasis is placed on comparing and analyzing the binary classification performance of the datasets and downstream tasks used by these models.Finally,based on the analytical results,future development di-rections for EEG large models are discussed.A knowledge framework of EEG large models is estab-lished to serve as a reference for subsequent research and promote in-depth development in the field.关键词
脑电图/Transformer架构/自监督学习/脑电大模型Key words
EEG/Transformer/self-supervised learning/EEG large models分类
信息技术与安全科学引用本文复制引用
HE Zhongyang,GAO Yuanlong,ZENG Ying,WANG Linyuan,PEI Changfu,YAN Bin..脑电大模型研究进展综述[J].信息工程大学学报,2025,26(6):674-682,9.基金项目
科技创新2030重大项目(2022ZD0208500) （2022ZD0208500）
国家自然科学基金(62106285) （62106285）
