This paper presents a source free domain adaptation method for steady-state
visually evoked potential (SSVEP) based brain-computer interface (BCI)
spellers. SSVEP-based BCI spellers help individuals experiencing speech
difficulties, enabling them to communicate at a fast rate. However, achieving a
high information transfer rate (ITR) in the current methods requires an
extensive calibration period before using the system, leading to discomfort for
new users. We address this issue by proposing a method that adapts the deep
neural network (DNN) pre-trained on data from source domains (participants of
previous experiments conducted for labeled data collection), using only the
unlabeled data of the new user (target domain). This adaptation is achieved by
minimizing our proposed custom loss function composed of self-adaptation and
local-regularity loss terms. The self-adaptation term uses the pseudo-label
strategy, while the novel local-regularity term exploits the data structure and
forces the DNN to assign the same labels to adjacent instances. Our method
achieves striking 201.15 bits/min and 145.02 bits/min ITRs on the benchmark and
BETA datasets, respectively, and outperforms the state-of-the-art alternative
techniques. Our approach alleviates user discomfort and shows excellent
identification performance, so it would potentially contribute to the broader
application of SSVEP-based BCI systems in everyday life.Comment: 11 pages (including one page appendix), 5 figure