Research On Recognition And Application Of Eeg Signal Based On Ssvep-Bci

In recent years, brain-computer interface (BCI) systems based on steady-state visual evoked potentials (SSVEP) have attracted attention due to their high information transfer rate (ITR) and more and more targets. The current mainstream algorithms for SSVEP recognition have greatly improved the accuracy and target detection time. This paper designs a robotic arm application system based on the eCCA-Y method for multi-target recognition. The phase characteristics of CCA's sine and cosine signals are added to the EEG signal. Compared with mainstream algorithms, research shows that this method can improve the SSVEP-based BCI performance. And choose a six-degree-of-freedom manipulator as the actuator of the brain-computer interface, and use a phase-encoded stimulation paradigm for multi-target recognition to conduct experiments on the application of the proposed method.