1. SSVEP-DAN: Cross-Domain Data Alignment for SSVEP-Based Brain–Computer Interfaces
- Author
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Sung-Yu Chen, Chi-Min Chang, Kuan-Jung Chiang, and Chun-Shu Wei
- Subjects
Electroencephalogram (EEG) ,brain–computer interface (BCI) ,steady-state visual-evoked potentials (SSVEPs) ,domain adaptation ,data alignment ,Medical technology ,R855-855.5 ,Therapeutics. Pharmacology ,RM1-950 - Abstract
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.
- Published
- 2024
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