SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces
This addresses the challenge of time-consuming calibration for SSVEP-based BCI users, though it appears incremental as it builds on existing data alignment methods for a specific domain.
The paper tackles the problem of data insufficiency in SSVEP-based brain-computer interfaces by introducing SSVEP-DAN, a neural network for aligning SSVEP data across domains, which significantly enhances decoding accuracy in scenarios with limited calibration data.
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.