Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces
For practical motor imagery (MI) brain-computer interface (BCI) applications, generating a reliable model for a target subject with few MI trials is important since the data collection process is labour-intensive and expensive. In this paper, we address this issue by proposing a few-shot learning me...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/9978669/ |
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| author | Xiuyu Huang Shuang Liang Yuanpeng Zhang Nan Zhou Witold Pedrycz Kup-Sze Choi |
| author_facet | Xiuyu Huang Shuang Liang Yuanpeng Zhang Nan Zhou Witold Pedrycz Kup-Sze Choi |
| author_sort | Xiuyu Huang |
| collection | DOAJ |
| description | For practical motor imagery (MI) brain-computer interface (BCI) applications, generating a reliable model for a target subject with few MI trials is important since the data collection process is labour-intensive and expensive. In this paper, we address this issue by proposing a few-shot learning method called temporal episode relation learning (TERL). TERL models MI with only limited trials from the target subject by the ability to compare MI trials through episode-based training. It can be directly applied to a new user without being re-trained, which is vital to improve user experience and realize real-world MIBCI applications. We develop a new and effective approach where, unlike the original episode learning, the temporal pattern between trials in each episode is encoded during the learning to boost the classification performance. We also perform an online evaluation simulation, in addition to the offline analysis that the previous studies only conduct, to better understand the performance of different approaches in real-world scenario. Extensive experiments are completed on four publicly available MIBCI datasets to evaluate the proposed TERL. Results show that TERL outperforms baseline and recent state-of-the-art methods, demonstrating competitive performance for subject-specific MIBCI where few trials are available from a target subject and a considerable number of trials from other source subjects. |
| format | Article |
| id | doaj-art-71f105a4fee34aefa927be68d1e9b858 |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-71f105a4fee34aefa927be68d1e9b8582025-08-20T03:05:52ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102023-01-013153054310.1109/TNSRE.2022.32282169978669Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer InterfacesXiuyu Huang0https://orcid.org/0000-0003-1600-9109Shuang Liang1https://orcid.org/0000-0003-0305-7558Yuanpeng Zhang2https://orcid.org/0000-0003-1736-3425Nan Zhou3https://orcid.org/0000-0002-0434-6231Witold Pedrycz4https://orcid.org/0000-0002-9335-9930Kup-Sze Choi5https://orcid.org/0000-0003-0836-7088Center for Smart Health, The Hong Kong Polytechnic University, Hong Kong, SAR, ChinaSmart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, ChinaMedical Informatics, Nantong University, Nantong, ChinaSchool of Electronic Information and Electronic Engineering, Chengdu University, Chengdu, ChinaDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, CanadaCenter for Smart Health, The Hong Kong Polytechnic University, Hong Kong, SAR, ChinaFor practical motor imagery (MI) brain-computer interface (BCI) applications, generating a reliable model for a target subject with few MI trials is important since the data collection process is labour-intensive and expensive. In this paper, we address this issue by proposing a few-shot learning method called temporal episode relation learning (TERL). TERL models MI with only limited trials from the target subject by the ability to compare MI trials through episode-based training. It can be directly applied to a new user without being re-trained, which is vital to improve user experience and realize real-world MIBCI applications. We develop a new and effective approach where, unlike the original episode learning, the temporal pattern between trials in each episode is encoded during the learning to boost the classification performance. We also perform an online evaluation simulation, in addition to the offline analysis that the previous studies only conduct, to better understand the performance of different approaches in real-world scenario. Extensive experiments are completed on four publicly available MIBCI datasets to evaluate the proposed TERL. Results show that TERL outperforms baseline and recent state-of-the-art methods, demonstrating competitive performance for subject-specific MIBCI where few trials are available from a target subject and a considerable number of trials from other source subjects.https://ieeexplore.ieee.org/document/9978669/Motor imagerybrain-computer interfacetemporal encodingepisode training |
| spellingShingle | Xiuyu Huang Shuang Liang Yuanpeng Zhang Nan Zhou Witold Pedrycz Kup-Sze Choi Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces IEEE Transactions on Neural Systems and Rehabilitation Engineering Motor imagery brain-computer interface temporal encoding episode training |
| title | Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces |
| title_full | Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces |
| title_fullStr | Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces |
| title_full_unstemmed | Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces |
| title_short | Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces |
| title_sort | relation learning using temporal episodes for motor imagery brain computer interfaces |
| topic | Motor imagery brain-computer interface temporal encoding episode training |
| url | https://ieeexplore.ieee.org/document/9978669/ |
| work_keys_str_mv | AT xiuyuhuang relationlearningusingtemporalepisodesformotorimagerybraincomputerinterfaces AT shuangliang relationlearningusingtemporalepisodesformotorimagerybraincomputerinterfaces AT yuanpengzhang relationlearningusingtemporalepisodesformotorimagerybraincomputerinterfaces AT nanzhou relationlearningusingtemporalepisodesformotorimagerybraincomputerinterfaces AT witoldpedrycz relationlearningusingtemporalepisodesformotorimagerybraincomputerinterfaces AT kupszechoi relationlearningusingtemporalepisodesformotorimagerybraincomputerinterfaces |