Li, Shurui and Daly, Ian and Guan, Cuntai and Cichocki, Andrzej and Jin, Jing (2024) Inter-participant transfer learning with attention based domain adversarial training for P300 detection. Neural Networks, 180. p. 106655. DOI https://doi.org/10.1016/j.neunet.2024.106655
Li, Shurui and Daly, Ian and Guan, Cuntai and Cichocki, Andrzej and Jin, Jing (2024) Inter-participant transfer learning with attention based domain adversarial training for P300 detection. Neural Networks, 180. p. 106655. DOI https://doi.org/10.1016/j.neunet.2024.106655
Li, Shurui and Daly, Ian and Guan, Cuntai and Cichocki, Andrzej and Jin, Jing (2024) Inter-participant transfer learning with attention based domain adversarial training for P300 detection. Neural Networks, 180. p. 106655. DOI https://doi.org/10.1016/j.neunet.2024.106655
Abstract
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.
Item Type: | Article |
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Uncontrolled Keywords: | Brain-computer interface; P300 detection; Cross participant task; Domain generalization |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 17 Sep 2024 15:28 |
Last Modified: | 30 Oct 2024 21:11 |
URI: | http://repository.essex.ac.uk/id/eprint/39204 |
Available files
Filename: Shurui-NN.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Embargo Date: 22 August 2025