PMSA-Net: A parallel multi-scale attention network for MI-BCI classification
Authors: 
Mingzhe Cui, Tao Chen, Yang Jiao, Qian Zheng, Yi Pan, Lei XieAuthors Info & Claims
Mingzhe Cui, Tao Chen, Yang Jiao, Qian Zheng, Yi Pan, Lei XieAuthors Info & ClaimsArticle No.: 54, Pages 1 - 6
Abstract
Decoding brain activity through electroencephalogram (EEG) signals is still challenging, due to the low signal-to-noise ratio and spatial resolution of EEG signals. To address these limitations, we propose an advanced end-to-end network, called the parallel multi-scale attention network (PMSA-Net). This novel architecture introduces the parallel structure and channel attention mechanism to enhance the accuracy and robustness of EEG signal classification. Specifically, PMSA-Net initially expands EEG signals into the depth dimension to improve spatial resolution, thereby better capturing and learning spatial information. Subsequently, the multi-scale technique is applied to enhance the feature extraction capability of the model. Finally, channel attention mechanisms are embedded in the parallel structure to recalibrate features across multiple dimensions. Experimental results based on the BCI Competition IV 2a dataset demonstrate that the model shows superior performance to other benchmark methods, achieving the highest average accuracy and kappa across all datasets. Moreover, a series of ablation experiments are conducted to further confirm the effectiveness of the structural improvements.
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Published In
November 2024
614 pages
ISBN:9798400713026
DOI:10.1145/3698587
Copyright © 2024 ACM.
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Publication History
Published: 16 December 2024
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- Short-paper
- Research
- Refereed limited
Funding Sources
- Jianbing Lingyan Foundation of Zhejiang Province, P.R.China
- National Natural Science Foundation of P.R.China
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BCB '24
Sponsor:
BCB '24: 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
November 22 - 25, 2024
Shenzhen, China
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