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.
Original language | English |
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Pages (from-to) | 2027-2037 |
Number of pages | 11 |
Journal | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
Volume | 32 |
DOIs | |
State | Published - 2024 |
Keywords
- Electroencephalogram (EEG)
- brain-computer interface (BCI)
- data alignment
- domain adaptation
- steady-state visual-evoked potentials (SSVEPs)