SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces

• URL: http://arxiv.org/abs/2311.12666v1
• Date: Tue, 21 Nov 2023 15:18:29 GMT
• Title: SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces
• Authors: Sung-Yu Chen, Chi-Min Chang, Kuan-Jung Chiang, Chun-Shu Wei
• Abstract summary: Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. We present SSVEP-DAN, the first dedicated neural network model designed for aligning SSVEP data across different domains.
• Score: 2.1192321523349404
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 heavily relies on individual training data obtained during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we present SSVEP-DAN, the first dedicated neural network model designed for aligning SSVEP data across different domains, which can encompass various sessions, subjects, or devices. Our experimental results across multiple cross-domain scenarios demonstrate SSVEP-DAN's capability to transform existing source SSVEP data into supplementary calibration data, significantly enhancing SSVEP decoding accuracy in scenarios with limited calibration data. We envision SSVEP-DAN as a catalyst for practical SSVEP-based BCI applications with minimal calibration. The source codes in this work are available at: https://github.com/CECNL/SSVEP-DAN.

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