  In this work, a classification method for SSVEP-based BCI is proposed. The
classification method uses features extracted by traditional SSVEP-based BCI
methods and finds optimal discrimination thresholds for each feature to
classify the targets. Optimising the thresholds is formalised as a maximisation
task of a performance measure of BCIs called information transfer rate (ITR).
However, instead of the standard method of calculating ITR, which makes certain
assumptions about the data, a more general formula is derived to avoid
incorrect ITR calculation when the standard assumptions are not met. This
allows the optimal discrimination thresholds to be automatically calculated and
thus eliminates the need for manual parameter selection or performing
computationally expensive grid searches. The proposed method shows good
performance in classifying targets of a BCI, outperforming previously reported
results on the same dataset by a factor of 2 in terms of ITR. The highest
achieved ITR on the used dataset was 62 bit/min. The proposed method also
provides a way to reduce false classifications, which is important in
real-world applications.
