Academic Journal
Real-time channel selection for enhanced steady-state visual evoked potentials online brain-computer interface systems.
| Τίτλος: | Real-time channel selection for enhanced steady-state visual evoked potentials online brain-computer interface systems. |
|---|---|
| Συγγραφείς: | Guo W; The School of Microelectronics, Shanghai University, Shanghai, 200444, China. Electronic address: albert_guo@shu.edu.cn., Zhao X; Guoke Xinnao (Shanghai) Intelligent Technology Co., Ltd., Shanghai, 201601, China; Shanghai Frontier Innovation Research Institute, Shanghai, 201108, China. Electronic address: zhaoxi@shu.edu.cn., Xu G; The School of Microelectronics, Shanghai University, Shanghai, 200444, China. Electronic address: xugy@shu.edu.cn., Wang Z; Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201210, China. Electronic address: wangzhenyu@sari.ac.cn., Zhou T; The School of Microelectronics, Shanghai University, Shanghai, 200444, China. Electronic address: zhouting@shu.edu.cn., Zhou W; The School of Microelectronics, Shanghai University, Shanghai, 200444, China. Electronic address: zhouwenhao@shu.edu.cn., Liu H; ShanghaiTech University, Shanghai, 201210, China. Electronic address: liuhf@shanghaitech.edu.cn., Xu T; Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201210, China. Electronic address: xuth@sari.ac.cn., Hu H; ShanghaiTech University, Shanghai, 201210, China. Electronic address: huhl@sari.ac.cn. |
| Πηγή: | Journal of neuroscience methods [J Neurosci Methods] 2026 Aug; Vol. 432, pp. 110757. Date of Electronic Publication: 2026 Mar 27. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7905558 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-678X (Electronic) Linking ISSN: 01650270 NLM ISO Abbreviation: J Neurosci Methods Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press. |
| Ιατρικοί όροι (MeSH): | Evoked Potentials, Visual*/physiology , Electroencephalography*/methods , Brain*/physiology , Brain-Computer Interfaces* , Signal Processing, Computer-Assisted*, Humans ; Photic Stimulation ; Algorithms ; Male ; Online Systems ; Female ; Adult ; Young Adult |
| Περίληψη: | Background: In recent years, researchers have actively developed new brain-computer interface (BCI) systems while seeking optimal channel combinations. Although channel selection has advanced significantly in BCI systems, few studies have specifically addressed channel selection for steady-state visual evoked potentials-BCI (SSVEP-BCI). New Method: This study proposes an online SSVEP-BCI system with dynamic channel selection during experiments. The proposed method constructs a multidimensional feature framework that incorporates signal energy, stability, and inter-channel correlation. The abnormality of each feature is then quantified to generate corresponding anomaly scores. These anomaly scores are integrated through a hierarchical decision mechanism to produce a comprehensive channel quality score. Based on this score, bad channels are precisely identified and removed, enabling training-free and dynamic channel selection. Results: The proposed multi-Adaptive priority-based SSVEP channel selection (MAPS-CS) method achieves the best performance compared with three existing channel selection methods. For the standard filter bank canonical correlation analysis (FBCCA), the accuracy of the proposed method was increased for four stimulus durations. Compared with the channel ensemble (CE) method, the proposed method achieved accuracy improvements of 3.5%, 4.1%, 4.4%, and 6.5% for stimulus durations of 2 s, 1.5 s, 1 s, and 0.5 s, respectively. Comparison With Existing Methods: The proposed method provides the best performance in FBCCA compared with CE, binary harmony search (BHS) and TOP-K local optimization channel selection (TOP-K-LOCS). Conclusions: These results confirm that the system can effectively detect bad channels in laboratory-based online experiments. (Copyright © 2026. Published by Elsevier B.V.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Bad channel detection; Brain–computer interface (BCI); Online SSVEP-BCI system; Steady-state visual evoked potentials (SSVEP) |
| Entry Date(s): | Date Created: 20260329 Date Completed: 20260713 Latest Revision: 20260713 |
| Update Code: | 20260714 |
| DOI: | 10.1016/j.jneumeth.2026.110757 |
| PMID: | 41905536 |
| Βάση Δεδομένων: | MEDLINE |
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