The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential
Brain-computer interface(BCI)based on steady-state visual evoked potential(SSVEP)have attracted much attention in the field of intelligent robotics.Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state,resulting in a system that lacks autonomous control capability.Therefore,this paper proposed a SSVEP asynchronous state recognition method,which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic(EEG)signals and combining with a linear discriminant analysis(LDA)to improve the accuracy of SSVEP asynchronous state recognition.Furthermore,addressing the control needs of disabled individuals in multitasking scenarios,a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed.This system enabled the collaborative control of wearable manipulator and robotic arm,where the robotic arm acts as a"third hand",offering significant advantages in complex environments.The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations.The average accuracy of user intent recognition in online control experiments was 93.0％,which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.
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