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

TL;DR

SSVEP-DAN is a neural network model that aligns SSVEP data across different domains to improve BCI performance with minimal calibration, enabling more practical and efficient SSVEP-based communication systems.

Contribution

This work introduces the first neural network designed specifically for aligning SSVEP data across sessions, subjects, or devices, enhancing decoding accuracy with limited calibration data.

Findings

Significantly improves SSVEP decoding accuracy in cross-domain scenarios.

Transforms source SSVEP data into supplementary calibration data effectively.

Enables practical BCI applications with minimal calibration requirements.

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.