Signal analysis method and component of electroencephalogram neural feedback system combined with virtual reality

Abstract

The invention discloses a signal analysis method and component of an electroencephalogram neural feedback system in combination with virtual reality. The method comprises the following steps of: acquiring an electroencephalogram signal of a resting state of a user, and setting a training initial threshold according to an electroencephalogram signal analysis result of the resting state; collectingelectroencephalogram signals generated in each training period and performing filtering processing, then calculating power spectral density by utilizing a pwelch function to obtain an average value ofthe power spectral density of the electroencephalogram signals in the corresponding frequency band, taking the average value as a feedback numerical value of the current training period, and adjusting a threshold value of the next training period according to the feedback numerical value; and calculating a feedback training learning effect index of the current training, and selecting an appropriate scene state by the virtual reality system according to the index for the next training. According to the method, the electroencephalogram signals generated in the training period of the user are processed in real time, the feedback numerical value is given, the threshold value is adjusted in real time according to the feedback numerical value, and the advantage that the task difficulty is dynamically adjusted according to the current cognitive ability of the user is achieved.

Description

technical field

[0001] The invention relates to the technical field of signal data processing, in particular to a signal analysis method and components of an EEG neurofeedback system combined with virtual reality. Background technique

[0002] The existing technology proposes an EEG neurofeedback training system combined with virtual reality, which is a new type of brain function training method, which converts a certain feature of the measured brain function activity signal into a form of visual or auditory signal and presents it to the The subject requires the subject to selectively enhance or suppress the signal characteristics, so as to achieve the purpose of self-regulating brain function activities. Neurofeedback can be used to improve cognitive function and is an effective computer-aided cognitive training method. EEG neurofeedback can be used to improve the cognitive function of healthy people because of its non-invasive and side-effect characteristics, and it has al...

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