Steady-state motor visual evoked potential brain-computer interface method based on stochastic resonance enhancement

Abstract

Steady-state motor visual evoked potential brain-computer interface method based on stochastic resonance enhancement. Electrodes are placed on the user's head first, and the measured EEG signals are sent to the computer, and then the motion stimulation unit is simultaneously presented to the user through the computer screen. The motion stimulation unit is covered with two-dimensional noise points that obey the Gaussian distribution. The update frequency of the noise point is synchronized with the screen refresh rate. The noise intensity is characterized by the standard deviation of the Gaussian distribution. After the motion stimulation unit is formed, the user looks at any one of the motion stimulation units. The computer collects the stimulation start and end flags synchronously, and collects EEG signals through the test electrodes, calculates the significance probability of different stimulation targets, judges and indicates the targets through the screen, and then performs the next target recognition. The present invention can significantly enhance the use of The strength of the brain response of the patient, the accuracy and efficiency of the existing brain-computer interface are improved, and the practical level of the brain-computer interface technology is innovatively improved.

Description

technical field [0001] The invention relates to the technical fields of neuroengineering and brain-computer interface in biomedical engineering, in particular to a brain-computer interface method based on stochastic resonance enhanced steady-state motion visual evoked potential. Background technique [0002] Brain-computer interface is the abbreviation of human brain-computer interface. As an important information carrier, the steady-state visual evoked potential signal has the characteristics of strong anti-interference ability, high information transmission rate and strong response for all users without training. , so it is the most practical signal type in brain-computer interface applications. In view of its induced light flicker that requires a higher light intensity, it is easy to cause discomfort to the user, especially at a lower stimulation frequency, the light flicker cycle is longer and the brightness changes significantly within a single cycle, which is more like...

AI Technical Summary

Problems solved by technology

However, the brain-computer interface based on the steady-state motor visual evoked potential is still affected by the individual differences of users and physiological noise. It relies on the improvement of specific software algorithms to achieve system performance improvement, and the performance expansion space is small, which is not conducive to the practicality of brain-computer interface technology. development

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