  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.
