  This paper presents a source free domain adaptation method for steady-state
visually evoked potentials (SSVEP) based brain-computer interface (BCI)
spellers. SSVEP-based BCI spellers assist individuals experiencing speech
difficulties by enabling them to communicate at a fast rate. However, achieving
a high information transfer rate (ITR) in most prominent methods requires an
extensive calibration period before using the system, leading to discomfort for
new users. We address this issue by proposing a novel method that adapts a
powerful deep neural network (DNN) pre-trained on data from source domains
(data from former users or participants of previous experiments) to the new
user (target domain), based only on the unlabeled target data. This adaptation
is achieved by minimizing our proposed custom loss function composed of
self-adaptation and local-regularity 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 similar labels to adjacent instances.
The proposed method priorities user comfort by removing the burden of
calibration while maintaining an excellent character identification accuracy
and ITR. In particular, 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 alternatives. Our code is available at
https://github.com/osmanberke/SFDA-SSVEP-BCI
