A P300-Based BCI System Using Stereoelectroencephalography and Its Application in a Brain Mechanistic Study

Stereoelectroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes. SEEG depth electrodes record brain activity from the shallow cortical layer and structures, which is not achievable through other recording techniques. Moreover, has advantage of a high signal-to-noise ratio (SNR). Therefore, it provides potential way to establish highly efficient brain-computer interface (BCI) aid in understanding human activity. In this study, we implemented P300-based BCI using signals. A single-character oddball paradigm was applied elicit P300. To predict target characters, fed feature vectors extracted collected five contacts into Bayesian linear discriminant analysis (BLDA) classifier. Thirteen epileptic patients implanted with participated experiment achieved an average online spelling accuracy 93.85%. single-contact decoding simulated analysis, found that SEEG-based system performance even when single signal channel. Furthermore, accuracies were mainly distributed visual ventral pathway, especially fusiform gyrus (FG) lingual (LG), played important role building BCIs. These results might provide new insights P300 mechanistic studies corresponding