Enhanced Brain-Controlled Mobile Robot Based on SE-VEP Paradigm With Single Stimulus
Brain-computer interface (BCI) systems based on steady-state visually evoked potentials (SSVEPs) have been widely adopted because of their efficiency and accuracy. However, the traditional SSVEP method has limitations, including visual fatigue and interference between different stimuli. To address t...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/11036254/ |
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| author | Tianyi Yan Zhiyuan Ming Yilun Huang Ziyu Liu Qiming Chen Deyu Zhang Mengzhen Liu Dingjie Suo Jian Zhang Siyu Liu |
| author_facet | Tianyi Yan Zhiyuan Ming Yilun Huang Ziyu Liu Qiming Chen Deyu Zhang Mengzhen Liu Dingjie Suo Jian Zhang Siyu Liu |
| author_sort | Tianyi Yan |
| collection | DOAJ |
| description | Brain-computer interface (BCI) systems based on steady-state visually evoked potentials (SSVEPs) have been widely adopted because of their efficiency and accuracy. However, the traditional SSVEP method has limitations, including visual fatigue and interference between different stimuli. To address these issues, a new BCI paradigm, namely, a spatial encoding-visually evoked potential (SE-VEP) model, is proposed in this work. This paradigm involves deploying four target points to implement gaze restrictions around a stimulus block and optimizing the locations of these target points through offline data acquisition. This design facilitates electroencephalogram (EEG) encoding for four instructions while using only one stimulus block. Data with varying eccentricities are classified using the Riemann kernel-based support vector machine (R-SVM) approach, which achieves a classification accuracy of up to 86.11%. As the eccentricity increases, the classification accuracy initially increases but subsequently decreases. By evaluating the information transfer rate (ITR), the optimal time window length for online BCIs is determined to be 1.2 s. Additionally, an online brain-controlled robotic virtual system is developed to validate the feasibility of the proposed paradigm for online brain-computer interface applications. The results confirm the effectiveness of the proposed paradigm in implementing an online BCI control system. An evaluation conducted with scales and the information transfer rate for a single stimulus (ITRSS) indicates that compared with the traditional BCI system, the proposed paradigm yields greater reductions in user fatigue (2.8 ± 0.5 vs. 4.1 ± 0.6) and stimulus block utilization (24.6 ± 2.3 vs. 8.2 ± 1.1 bits/min). |
| format | Article |
| id | doaj-art-04e98deedd84489a8b05a04b0bf9c3d6 |
| institution | Matheson Library |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-04e98deedd84489a8b05a04b0bf9c3d62025-07-03T23:00:11ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01332498250710.1109/TNSRE.2025.357937311036254Enhanced Brain-Controlled Mobile Robot Based on SE-VEP Paradigm With Single StimulusTianyi Yan0https://orcid.org/0000-0002-2674-4134Zhiyuan Ming1https://orcid.org/0009-0003-0893-078XYilun Huang2Ziyu Liu3https://orcid.org/0009-0003-0061-6227Qiming Chen4Deyu Zhang5https://orcid.org/0000-0003-1469-0674Mengzhen Liu6https://orcid.org/0009-0006-2488-5212Dingjie Suo7https://orcid.org/0000-0002-2797-1240Jian Zhang8https://orcid.org/0000-0003-1327-7896Siyu Liu9https://orcid.org/0000-0001-5192-8739School of Medical Technology, Beijing Institute of Technology, Beijing, ChinaSchool of Medical Technology, Beijing Institute of Technology, Beijing, ChinaSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, ChinaSchool of Medical Technology, Beijing Institute of Technology, Beijing, ChinaSchool of Medical Technology, Beijing Institute of Technology, Beijing, ChinaHangzhou Innovation Institute, Beihang University, Hangzhou, ChinaSchool of Mechatronics Engineering, Beijing Institute of Technology, Beijing, ChinaSchool of Medical Technology, Beijing Institute of Technology, Beijing, ChinaSchool of Medical Technology, Beijing Institute of Technology, Beijing, ChinaSchool of Medical Technology, Beijing Institute of Technology, Beijing, ChinaBrain-computer interface (BCI) systems based on steady-state visually evoked potentials (SSVEPs) have been widely adopted because of their efficiency and accuracy. However, the traditional SSVEP method has limitations, including visual fatigue and interference between different stimuli. To address these issues, a new BCI paradigm, namely, a spatial encoding-visually evoked potential (SE-VEP) model, is proposed in this work. This paradigm involves deploying four target points to implement gaze restrictions around a stimulus block and optimizing the locations of these target points through offline data acquisition. This design facilitates electroencephalogram (EEG) encoding for four instructions while using only one stimulus block. Data with varying eccentricities are classified using the Riemann kernel-based support vector machine (R-SVM) approach, which achieves a classification accuracy of up to 86.11%. As the eccentricity increases, the classification accuracy initially increases but subsequently decreases. By evaluating the information transfer rate (ITR), the optimal time window length for online BCIs is determined to be 1.2 s. Additionally, an online brain-controlled robotic virtual system is developed to validate the feasibility of the proposed paradigm for online brain-computer interface applications. The results confirm the effectiveness of the proposed paradigm in implementing an online BCI control system. An evaluation conducted with scales and the information transfer rate for a single stimulus (ITRSS) indicates that compared with the traditional BCI system, the proposed paradigm yields greater reductions in user fatigue (2.8 ± 0.5 vs. 4.1 ± 0.6) and stimulus block utilization (24.6 ± 2.3 vs. 8.2 ± 1.1 bits/min).https://ieeexplore.ieee.org/document/11036254/Spatial codingsteady-state visually evoked potentialsEEG decodingbrain-machine interfacerobotic motion control |
| spellingShingle | Tianyi Yan Zhiyuan Ming Yilun Huang Ziyu Liu Qiming Chen Deyu Zhang Mengzhen Liu Dingjie Suo Jian Zhang Siyu Liu Enhanced Brain-Controlled Mobile Robot Based on SE-VEP Paradigm With Single Stimulus IEEE Transactions on Neural Systems and Rehabilitation Engineering Spatial coding steady-state visually evoked potentials EEG decoding brain-machine interface robotic motion control |
| title | Enhanced Brain-Controlled Mobile Robot Based on SE-VEP Paradigm With Single Stimulus |
| title_full | Enhanced Brain-Controlled Mobile Robot Based on SE-VEP Paradigm With Single Stimulus |
| title_fullStr | Enhanced Brain-Controlled Mobile Robot Based on SE-VEP Paradigm With Single Stimulus |
| title_full_unstemmed | Enhanced Brain-Controlled Mobile Robot Based on SE-VEP Paradigm With Single Stimulus |
| title_short | Enhanced Brain-Controlled Mobile Robot Based on SE-VEP Paradigm With Single Stimulus |
| title_sort | enhanced brain controlled mobile robot based on se vep paradigm with single stimulus |
| topic | Spatial coding steady-state visually evoked potentials EEG decoding brain-machine interface robotic motion control |
| url | https://ieeexplore.ieee.org/document/11036254/ |
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