Direct information transfer rate optimisation for SSVEP-based BCI
This work addresses the need for efficient and accurate parameter selection in BCIs, offering a method to reduce false classifications for real-world applications, though it is incremental as it builds on traditional feature extraction.
The authors tackled the problem of optimizing classification for SSVEP-based BCIs by directly maximizing information transfer rate (ITR) with a derived general formula, achieving a 2x improvement in ITR over prior results and reaching 62 bit/min on a dataset.
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.