Steady motion visual evoked potential brain computer interface method based on stochastic resonance enhancement

Abstract

Provided is a steady motion visual evoked potential brain computer interface method based on stochastic resonance enhancement. First, an electrode is arranged on the head of a user, a detected electroencephalogram signal is sent to a computer, then movement stimulating units are presented in front of the user through a computer screen at the same time, the movement stimulating units are shielded by two-dimensional noise points obeying Gaussian distribution, the noise point update frequency is synchronized with the screen refresh rate, and the noise strength is represented by Gaussian distribution standard deviation. After the movement stimulating units are formed, the user watches any one of the movement stimulating units, a computer acquires a stimulation starting flag bit and a stimulation finishing flag bit, a computer signal is acquired through a test electrode, the significance probabilities of different stimulation targets are calculated, the targets are judged and indicated through the screen, and then the next target identification is conducted. The steady motion visual evoked potential brain computer interface method based on stochastic resonance enhancement can remarkably enhance brain response strength of the user, improve precision and efficiency of an existing brain computer interface, and innovatively improve practical level of the brain computer interface technology.

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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