SSVEP-DAN: Cross-Domain Data Alignment for SSVEP-Based Brain–Computer Interfaces
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Jg. 32 (2024), S. 2027-2037
Online
academicJournal
Zugriff:
Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency is highly dependent on individual training data acquired during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we introduce SSVEP-DAN, the first dedicated neural network model designed to align SSVEP data across different domains, encompassing various sessions, subjects, or devices. Our experimental results demonstrate the ability of SSVEP-DAN to transform existing source SSVEP data into supplementary calibration data. This results in a significant improvement in SSVEP decoding accuracy while reducing the calibration time. We envision SSVEP-DAN playing a crucial role in future applications of high-performance SSVEP-based BCIs. The source code for this work is available at: https://github.com/CECNL/SSVEP-DAN.
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SSVEP-DAN: Cross-Domain Data Alignment for SSVEP-Based Brain–Computer Interfaces
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Autor/in / Beteiligte Person: | Chen, Sung-Yu ; Chang, Chi-Min ; Chiang, Kuan-Jung ; Wei, Chun-Shu |
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Zeitschrift: | IEEE Transactions on Neural Systems and Rehabilitation Engineering, Jg. 32 (2024), S. 2027-2037 |
Veröffentlichung: | IEEE, 2024 |
Medientyp: | academicJournal |
ISSN: | 1534-4320 (print) ; 1558-0210 (print) |
DOI: | 10.1109/TNSRE.2024.3404432 |
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