Optimizing steady-state visual evoked potential classifiers for high performance and low computational costs in brain-computer interfacing

While assistive robotic devices can improve the quality of life for individuals with tetraplegia, it is difficult to provide a high-performing interface that can be fully utilized, with little to no motor functionality. While a brain-computer interface (BCI) can be used with little to no motor functionality, it typically has a low performance. Steady-state visually evoked potentials (SSVEP) provide some of the best performing signals for a BCI, but are rarely investigated for online asynchronous control where not only accuracy is important, but also the computational costs. This study investigates and compares three classifiers: the well-known and high-performing task-related component analysis (TRCA), the computational efficient Spatiotemporal beamformer (STBF) build on the stimulus-locked inter-trace correlation (SLIC) algorithm and our proposed novel algorithm which combines the two: the SLIC-TRCA. Results show the SLIC-TRCA achieving higher accuracies ${(95.00\pm 5.36\%}$ with a 1s classification window) compared to the TRCA ${(88.25\pm 14.58\%)}$ and similar compared to the STBF ${(91.00\pm 11.02\%)}$ while having a much lower computational cost (519% faster than the TRCA and 144% faster than the STBF). We, therefore, believe this algorithm has an exciting potential as it will allow a high classification accuracy without requiring a high-performing CPU.