A Brain-Computer Interface Method Using Stochastic Resonance to Enhance EEG Signals

Abstract

The invention discloses a brain-computer interface method for enhancing electroencephalogram signals by using stochastic resonance. This method uses the stochastic resonance phenomenon that has been found in the process of human brain perception, and through the method of stochastic resonance to regulate the synchronization of cortical EEG, it tries to improve the signal-to-noise ratio of EEG signals from the source, reduce the differences between subjects and improve the signal source. stability, giving a new way to enhance the source of EEG signals. The invention models the law of electrophysiological activity in the sensorimotor brain area, the relationship between perception and random noise, constructs an EEG feedback control mode based on classical brain-computer interface technology, and achieves the enhancement of EEG rhythm signals based on scalp EEG For the purpose of characterizing the effects of brain-computer interface technology.

Description

technical field [0001] The invention belongs to the fields of biomedical engineering and medical instruments, and relates to a brain-computer interface method, in particular to a method for stochastic resonance enhancement of brain-computer interface EEG rhythm signals. Background technique [0002] Brain-computer interface technology is a technology that does not rely on the information transmission channels of peripheral nerves and muscles, but directly communicates with the external environment by obtaining brain signals. Clinically, EEG rhythm signals in the sensorimotor cortex area are commonly used as control signal sources, and their applications can be summarized into two types: one is to help disabled people communicate with the external environment, such as motion control of prosthetics and wheelchairs, use of household appliances, etc. Environmental controllers to operate various electrical appliances, etc.; the other is to provide active rehabilitation assistance...

AI Technical Summary

Problems solved by technology

Although these methods have improved the performance of the system, there are still some shortcomings: such as the long training time of the subjects, or even invalid; the signal detection is limited by the low signal-to-noise ratio of the EEG rhythm and other shortcomings.

However, the spontaneous rhythm is easily disturbed, and it often requires long-term training by the user to achieve performance stability.

Images