Studies Of Key Techniques For Hybrid Brain-Computer Interface Combining High-frequency Steady-state Visual Evoked Potentials

Brain-computer interface(BCI)systems have gained attention for their ability to establish a direct communication channel between the brain and external devices.In recent years,traditional BCI systems that rely on a single input signal or modality have made great progress with the support of new technologies and algorithms,but they are still limited by the inherent characteristics of a single signal and modality.The disadvantage is that the system still has limitations in terms of performance,applicable population,and application scenarios.The multi-modal or hybrid BCI system constructed by fusing a variety of brain activity signals or other physiological activity signals is more practical,universal,and robust than the single-modal BCI system.Steady-state visual evoked potentials(SSVEP)-based BCI with electroencephalography(EEG)technology has the characteristics of high information transmission rate,less training time,and simple use.It is a common modality for constructing hybrid BCI systems.However,the current research on SSVEP-BCI mainly focuses on low-frequency stimulation,which is prone to fatigue when used for a long time,while the comfort of high-frequency stimulation is relatively high.Therefore,this paper further studies the hybrid BCI system combined with high frequency SSVEP.Aiming at the poor correlation of mixed tasks in the current parallel hybrid braincomputer interface paradigm combining motor imagery(MI)and SSVEP,this paper constructed a more natural parallel hybrid of MI and SSVEP by introducing the concept of intermodulation frequency.Further,by optimizing and combining two MI decoding algorithms,the Tikhonov regularized common spatial pattern and the common spatialspectral pattern,a Tikhonov regularized common spatial-spectral pattern was proposed.In addition,a probability distribution-based algorithm was proposed.The fusion decision method is to fuse the labels output by the MI and SSVEP classifiers to obtain the final classification result of the hybrid system.Online experimental results in healthy subjects and stroke patients validated the feasibility of the hybrid paradigm.The recognition efficiency of high-frequency SSVEP is low.In high-frequency SSVEP-BCI,a trade-off between the data length and the number of targets is usually required to ensure the classification accuracy.Therefore,the information transfer rate of the current high-frequency SSVEP-BCI system still has a big gap compared with that of medium-and low-frequency SSVEP.Therefore,this paper introduced the joint frequency-phase modulation method to encode 16 high-frequency targets,and then used task discriminant component analysis(TDCA)to decode high-frequency SSVEP for the first time.At the same time,it combined four wrist movements to construct a hybrid BCI system of electromyography(EMG)and high-frequency SSVEP,expanding the highfrequency SSVEP system instruction set to 64.The hybrid system achieved an average correct rate of 88.07±4.51%and an average information transfer rate in 159.12±13.63 bits/min,outperforming the currently known high-frequency SSVEP-based BCI systems.Compared with existing related hybrid BCI studies,the above two hybrid BCI systems combined with high-frequency SSVEP have been improved in terms of performance and comfort,and their practicability and universality have also been enhanced.The experimental results demonstrated the feasibility and effectiveness of the two systems in healthy subjects,and the potential of applying them to exercise recovery or daily communication in patients or disabled individuals is also worthy of further exploration.