  Steady-state visual evoked potential (SSVEP) recognition methods are equipped
with learning from the subject's calibration data, and they can achieve extra
high performance in the SSVEP-based brain-computer interfaces (BCIs), however
their performance deteriorate drastically if the calibration trials are
insufficient. This study develops a new method to learn from limited
calibration data and it proposes and evaluates a novel adaptive data-driven
spatial filtering approach for enhancing SSVEPs detection. The spatial filter
learned from each stimulus utilizes temporal information from the corresponding
EEG trials. To introduce the temporal information into the overall procedure,
an multitask learning approach, based on the bayesian framework, is adopted.
The performance of the proposed method was evaluated into two publicly
available benchmark datasets, and the results demonstrated that our method
outperform competing methods by a significant margin.
