Wang, Huiyang and Jiang, Jiuchuan and Gan, John Q and Wang, Haixian (2023) Motor Imagery EEG Classification Based on a Weighted Multi-branch Structure Suitable for Multisubject Data. IEEE Transactions on Biomedical Engineering, 70 (11). pp. 3040-3051. DOI https://doi.org/10.1109/tbme.2023.3274231
Wang, Huiyang and Jiang, Jiuchuan and Gan, John Q and Wang, Haixian (2023) Motor Imagery EEG Classification Based on a Weighted Multi-branch Structure Suitable for Multisubject Data. IEEE Transactions on Biomedical Engineering, 70 (11). pp. 3040-3051. DOI https://doi.org/10.1109/tbme.2023.3274231
Wang, Huiyang and Jiang, Jiuchuan and Gan, John Q and Wang, Haixian (2023) Motor Imagery EEG Classification Based on a Weighted Multi-branch Structure Suitable for Multisubject Data. IEEE Transactions on Biomedical Engineering, 70 (11). pp. 3040-3051. DOI https://doi.org/10.1109/tbme.2023.3274231
Abstract
Objective : Electroencephalogram (EEG) signal recognition based on deep learning technology requires the support of sufficient data. However, training data scarcity usually occurs in subject-specific motor imagery tasks unless multisubject data can be used to enlarge training data. Unfortunately, because of the large discrepancies between data distributions from different subjects, model performance could only be improved marginally or even worsened by simply training on multisubject data. Method : This paper proposes a novel weighted multi-branch (WMB) structure for handling multisubject data to solve the problem, in which each branch is responsible for fitting a pair of source-target subject data and adaptive weights are used to integrate all branches or select branches with the largest weights to make the final decision. The proposed WMB structure was applied to six well-known deep learning models (EEGNet, Shallow ConvNet, Deep ConvNet, ResNet, MSFBCNN, and EEG_TCNet) and comprehensive experiments were conducted on EEG datasets BCICIV-2a, BCICIV-2b, high gamma dataset (HGD) and two supplementary datasets. Result : Superior results against the state-of-the-art models have demonstrated the efficacy of the proposed method in subject-specific motor imagery EEG classification. For example, the proposed WMB_EEGNet achieved classification accuracies of 84.14%, 90.23%, and 97.81% on BCICIV-2a, BCICIV-2b and HGD, respectively. Conclusion : It is clear that the proposed WMB structure is capable to make good use of multisubject data with large distribution discrepancies for subject-specific EEG classification.
Item Type: | Article |
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Uncontrolled Keywords: | Brain-computer interfaces; Data Distribution; Deep Learning; EEG decoding; Motor Imagery |
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: | 24 May 2023 15:00 |
Last Modified: | 16 May 2024 21:52 |
URI: | http://repository.essex.ac.uk/id/eprint/35668 |
Available files
Filename: Motor_Imagery_EEG_Classification_Based_on_a_Weighted_Multi-branch_Structure_Suitable_for_Multisubject_Data.pdf