Zhang, J and Li, K orcid.org/0000-0001-6657-0522 (Cover date: August 2023) A multi-view CNN encoding for motor imagery EEG signals. Biomedical Signal Processing and Control, 85. 105063. ISSN 1746-8094
Abstract
Objective: The convolution neural network (CNN) has gained lots of attentions recently in decoding electroencephalogram (EEG) signals in Motor Imagery (MI) Brain–Computer Interface (BCI) designed for improving stroke rehabilitation strategies. However, the extremely non-linear, nonstationary nature of the EEG signals and diversity among individual subjects results in the overfitting of a CNN model and limits its learning ability. In this study, a densely connected convolutional network with multi-view inputs is proposed.
Methods: First, different data subsets from the original EEG signals are created as the CNN model inputs through bandpass filters applied to the EEG signals to generate multiple frequency sub-band signals based on brain rhythms. Then, temporal and spatial features are captured based on the whole frequency band and the filtered sub-band signals, respectively. Further, two dense blocks with multi-CNN layers, which connect each layer to every other layer in the feed-forward path, are used to enhance the model learning capabilities and strengthen information propagation. Finally, a concatenation fusion method is used to integrate the extracted features and a fully connected layer for finalizing the classification.
Results: The proposed method achieves an average accuracy of 75.16% on the public Korea University EEG dataset which consists the EEG signals of 54 healthy subjects for the two-class motor imagery tasks, higher than other state-of-the-art deep learning methods.
Conclusion: The proposed method effectively extracts much richer motor imagery information from the EEG signals in the BCI system and improves the classification accuracy.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Elsevier Ltd. This is an author produced version of an article published in Biomedical Signal Processing and Control. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Motor imagery, Electroencephalography, Convolutional neural network |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 May 2023 11:33 |
Last Modified: | 06 Jun 2024 00:13 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.bspc.2023.105063 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199684 |
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