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.

翻译：基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）通过高速拼写器系统提供了一种非侵入式通信方式。然而，其效率高度依赖于耗时的校准训练过程中获取的个体训练数据。为解决SSVEP脑机接口中的数据不足问题，我们提出了SSVEP-DAN，这是首个专为跨域对齐SSVEP数据设计的神经网络模型，可兼容不同会话、受试者或设备。我们在多个跨域场景下的实验结果表明，SSVEP-DAN能够将现有的源域SSVEP数据转化为辅助校准数据，在有限校准数据场景下显著提升SSVEP解码精度。我们期望SSVEP-DAN能成为推动低校准SSVEP脑机接口实际应用的催化剂。本工作源码已开源：https://github.com/CECNL/SSVEP-DAN