EEG-based brain-computer interface enables real-time robotic hand control at individual finger level
Abstract Brain-computer interfaces (BCIs) connect human thoughts to external devices, offering the potential to enhance life quality for individuals with motor impairments and general population. Noninvasive BCIs are accessible to a wide audience but currently face challenges, including unintuitive...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61064-x |
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| _version_ | 1849238339469705216 |
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| author | Yidan Ding Chalisa Udompanyawit Yisha Zhang Bin He |
| author_facet | Yidan Ding Chalisa Udompanyawit Yisha Zhang Bin He |
| author_sort | Yidan Ding |
| collection | DOAJ |
| description | Abstract Brain-computer interfaces (BCIs) connect human thoughts to external devices, offering the potential to enhance life quality for individuals with motor impairments and general population. Noninvasive BCIs are accessible to a wide audience but currently face challenges, including unintuitive mappings and imprecise control. In this study, we present a real-time noninvasive robotic control system using movement execution (ME) and motor imagery (MI) of individual finger movements to drive robotic finger motions. The proposed system advances state-of-the-art electroencephalography (EEG)-BCI technology by decoding brain signals for intended finger movements into corresponding robotic motions. In a study involving 21 able-bodied experienced BCI users, we achieved real-time decoding accuracies of 80.56% for two-finger MI tasks and 60.61% for three-finger tasks. Brain signal decoding was facilitated using a deep neural network, with fine-tuning enhancing BCI performance. Our findings demonstrate the feasibility of naturalistic noninvasive robotic hand control at the individuated finger level. |
| format | Article |
| id | doaj-art-d2632a6f7e17469aaa78727b8caeba24 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-d2632a6f7e17469aaa78727b8caeba242025-08-20T04:01:40ZengNature PortfolioNature Communications2041-17232025-06-0116112010.1038/s41467-025-61064-xEEG-based brain-computer interface enables real-time robotic hand control at individual finger levelYidan Ding0Chalisa Udompanyawit1Yisha Zhang2Bin He3Department of Biomedical Engineering, Carnegie Mellon UniversityDepartment of Electrical and Computer Engineering, Carnegie Mellon UniversityDepartment of Biomedical Engineering, Carnegie Mellon UniversityDepartment of Biomedical Engineering, Carnegie Mellon UniversityAbstract Brain-computer interfaces (BCIs) connect human thoughts to external devices, offering the potential to enhance life quality for individuals with motor impairments and general population. Noninvasive BCIs are accessible to a wide audience but currently face challenges, including unintuitive mappings and imprecise control. In this study, we present a real-time noninvasive robotic control system using movement execution (ME) and motor imagery (MI) of individual finger movements to drive robotic finger motions. The proposed system advances state-of-the-art electroencephalography (EEG)-BCI technology by decoding brain signals for intended finger movements into corresponding robotic motions. In a study involving 21 able-bodied experienced BCI users, we achieved real-time decoding accuracies of 80.56% for two-finger MI tasks and 60.61% for three-finger tasks. Brain signal decoding was facilitated using a deep neural network, with fine-tuning enhancing BCI performance. Our findings demonstrate the feasibility of naturalistic noninvasive robotic hand control at the individuated finger level.https://doi.org/10.1038/s41467-025-61064-x |
| spellingShingle | Yidan Ding Chalisa Udompanyawit Yisha Zhang Bin He EEG-based brain-computer interface enables real-time robotic hand control at individual finger level Nature Communications |
| title | EEG-based brain-computer interface enables real-time robotic hand control at individual finger level |
| title_full | EEG-based brain-computer interface enables real-time robotic hand control at individual finger level |
| title_fullStr | EEG-based brain-computer interface enables real-time robotic hand control at individual finger level |
| title_full_unstemmed | EEG-based brain-computer interface enables real-time robotic hand control at individual finger level |
| title_short | EEG-based brain-computer interface enables real-time robotic hand control at individual finger level |
| title_sort | eeg based brain computer interface enables real time robotic hand control at individual finger level |
| url | https://doi.org/10.1038/s41467-025-61064-x |
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