MAtt: A Manifold Attention Network for EEG Decoding

Yue Ting Pan, Jing Lun Chou, Chun Shu Wei

研究成果: Conference contribution同行評審

11 引文 斯高帕斯(Scopus)

摘要

Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL)-based EEG decoders offer improved performances, the development of geometric learning (GL) has attracted much attention for offering exceptional robustness in decoding noisy EEG data. However, there is a lack of studies on the merged use of deep neural networks (DNNs) and geometric learning for EEG decoding. We herein propose a manifold attention network (MAtt), a novel geometric deep learning (GDL)-based model, featuring a manifold attention mechanism that characterizes spatiotemporal representations of EEG data fully on a Riemannian symmetric positive definite (SPD) manifold. The evaluation of the proposed MAtt on both time-synchronous and -asyncronous EEG datasets suggests its superiority over other leading DL methods for general EEG decoding. Furthermore, analysis of model interpretation reveals the capability of MAtt in capturing informative EEG features and handling the non-stationarity of brain dynamics. Source codes are available at https://github.com/CECNL/MAtt.

原文English
主出版物標題Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022
編輯S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh
發行者Neural information processing systems foundation
ISBN(電子)9781713871088
出版狀態Published - 2022
事件36th Conference on Neural Information Processing Systems, NeurIPS 2022 - New Orleans, United States
持續時間: 28 11月 20229 12月 2022

出版系列

名字Advances in Neural Information Processing Systems
35
ISSN(列印)1049-5258

Conference

Conference36th Conference on Neural Information Processing Systems, NeurIPS 2022
國家/地區United States
城市New Orleans
期間28/11/229/12/22

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