Effects of inter-stimulus intervals on concurrent P300 and SSVEP features for hybrid brain-computer interfaces

Background: Recently, we have implemented a high-speed brain-computer interface (BCI) system with a large instruction set using the concurrent P300 and steady-state visual evoked potential (SSVEP) features (also known as hybrid features). However, it remains unclear how to select inter-stimulus interval (ISI) for the proposed BCI system to balance the encoding efficiency and decoding performance.

New method: This study developed a 6 * 9 hybrid P300-SSVEP BCI system and investigated a series of ISIs ranged from -175-0 ms with a step of 25 ms. The influence of ISI on the hybrid features was analyzed from several aspects, including the amplitude of the induced features, classification accuracy, information transfer rate (ITR). Twelve naive subjects were recruited for the experiment.

Results: The results showed the ISI factor had a significant impact on the hybrid features. Specifically, as the values of ISI decreased, the amplitudes of the induced features and accuracies decreased gradually, while the ITRs increased rapidly. It's achieved the highest ITR of 158.50 bits/min when ISI equal to - 175 ms.

Comparison with existing method: The optimal ISI in this study achieved superior performance in comparison with the one we used in the previous study.

Conclusions: The ISI can exert an important influence on the P300-SSVEP BCI system and its optimal value is - 175 ms in this study, which is significant for developing the high-speed BCI system with larger instruction sets in the future.