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http://bura.brunel.ac.uk/handle/2438/28571
Title: | EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification |
Authors: | Chen, Z Yang, R Huang, M Li, F Lu, G Wang, Z |
Keywords: | electroencephalograph;topological spatial information;topological permutation;progressive feature extractor;progressive convolution architecture |
Issue Date: | 27-Dec-2023 |
Publisher: | Elsevier |
Citation: | Chen, Z. et al. (2024) 'EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification', Computers in Biology and Medicine, 169, 107901, pp. 1 - 10. doi: 10.1016/j.compbiomed.2023.107901. |
Abstract: | Because of the intricate topological structure and connection of the human brain, extracting deep spatial features from electroencephalograph (EEG) signals is a challenging and time-consuming task. The extraction of topological spatial information plays a crucial role in EEG classification, and the architecture of the spatial convolution greatly affects the performance and complexity of convolutional neural network (CNN) based EEG classification models. In this study, a progressive convolution CNN architecture named EEGProgress is proposed, aiming to efficiently extract the topological spatial information of EEG signals from multi-scale levels (electrode, brain region, hemisphere, global) with superior speed. To achieve this, the raw EEG data is permuted using the empirical topological permutation rule, integrating the EEG data with numerous topological properties. Subsequently, the spatial features are extracted by a progressive feature extractor including prior, electrode, region, and hemisphere convolution blocks, progressively extracting the deep spatial features with reduced parameters and speed. Finally, the comparison and ablation experiments under both cross-subject and within-subject scenarios are conducted on a public dataset to verify the performance of the proposed EEGProgress and the effectiveness of the topological permutation. The results demonstrate the superior feature extraction ability of the proposed EEGProgress, with an average increase of 4.02% compared to other CNN-based EEG classification models under both cross-subject and within-subject scenarios. Furthermore, with the obtained average testing time, FLOPs, and parameters, the proposed EEGProgress outperforms other comparison models in terms of model complexity. |
URI: | https://bura.brunel.ac.uk/handle/2438/28571 |
DOI: | https://doi.org/10.1016/j.compbiomed.2023.107901 |
ISSN: | 0010-4825 |
Other Identifiers: | ORCID: Zhige Chen https://orcid.org/0009-0007-1208-5880 ORCiD: Rui Yang https://orcid.org/0000-0002-5634-5476 ORCiD: Mengjie Huang https://orcid.org/0000-0001-8163-8679 ORCiD: Fumin Li https://orcid.org/0009-0002-5804-2978 ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401 107901 |
Appears in Collections: | Dept of Computer Science Embargoed Research Papers |
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FullText.pdf | Embargoed until 27 December 2024 | 7.02 MB | Adobe PDF | View/Open |
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