Zhu, Li and Liu, Youyang and Liu, Riheng and Peng, Yong and Cao, Jianting and Li, Junhua and Kong, Wanzeng (2023) Decoding Multi-Brain Motor Imagery From EEG Using Coupling Feature Extraction and Few-Shot Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31. pp. 4683-4692. DOI https://doi.org/10.1109/tnsre.2023.3336356
Zhu, Li and Liu, Youyang and Liu, Riheng and Peng, Yong and Cao, Jianting and Li, Junhua and Kong, Wanzeng (2023) Decoding Multi-Brain Motor Imagery From EEG Using Coupling Feature Extraction and Few-Shot Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31. pp. 4683-4692. DOI https://doi.org/10.1109/tnsre.2023.3336356
Zhu, Li and Liu, Youyang and Liu, Riheng and Peng, Yong and Cao, Jianting and Li, Junhua and Kong, Wanzeng (2023) Decoding Multi-Brain Motor Imagery From EEG Using Coupling Feature Extraction and Few-Shot Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31. pp. 4683-4692. DOI https://doi.org/10.1109/tnsre.2023.3336356
Abstract
Electroencephalography (EEG)-based motor imagery (MI) is one of brain computer interface (BCI) paradigms, which aims to build a direct communication pathway between human brain and external devices by decoding the brain activities. In a traditional way, MI BCI replies on a single brain, which suffers from the limitations, such as low accuracy and weak stability. To alleviate these limitations, multi-brain BCI has emerged based on the integration of multiple individuals' intelligence. Nevertheless, the existing decoding methods mainly use linear averaging or feature integration learning from multi-brain EEG data, and do not effectively utilize coupling relationship features, resulting in undesired decoding accuracy. To overcome these challenges, we proposed an EEG-based multi-brain MI decoding method, which utilizes coupling feature extraction and few-shot learning to capture coupling relationship features among multi-brains with only limited EEG data. We performed an experiment to collect EEG data from multiple persons who engaged in the same task simultaneously and compared the methods on the collected data. The comparison results showed that our proposed method improved the performance by 14.23% compared to the single-brain mode in the 10-shot three-class decoding task. It demonstrated the effectiveness of the proposed method and usability of the method in the context of only small amount of EEG data available.
Item Type: | Article |
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Uncontrolled Keywords: | Algorithms; Brain; Brain-Computer Interfaces; Electroencephalography; Humans; Imagination |
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: | 15 Jan 2024 14:02 |
Last Modified: | 16 May 2024 22:08 |
URI: | http://repository.essex.ac.uk/id/eprint/36952 |
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