Inter-participant transfer learning with attention based domain adversarial training for P300 detection.

Autor: Li S; Center of Intelligent Computing, School of Mathematics, East China University of Science and Technology, Shanghai 200237, China., Daly I; Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom., Guan C; School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore., Cichocki A; Systems Research Institute, Polish Academy of Science, Warsaw 01-447, Poland; RIKEN Advanced Intelligence Project, Tokyo 103-0027, Japan; Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan., Jin J; Center of Intelligent Computing, School of Mathematics, East China University of Science and Technology, Shanghai 200237, China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China. Electronic address: jinjing@ecust.edu.cn.
Jazyk: angličtina
Zdroj: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2024 Dec; Vol. 180, pp. 106655. Date of Electronic Publication: 2024 Aug 22.
DOI: 10.1016/j.neunet.2024.106655
Abstrakt: A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which requires training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source domain transfer learning method based Attention Domain Adversarial Neural Network (OADANN) to mitigate data distribution discrepancies for cross-participant classification tasks. We train and validate our proposed model on both a publicly available OpenBMI dataset and a Self-collected dataset, employing a leave one participant out cross validation scheme. Experimental results demonstrate that the proposed OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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