EEG relaxation recognition method and device based on Kalman filter and wavelet transform

Abstract

The invention discloses a method for identifying the relaxation degree of EEG based on Kalman filter and wavelet transform, which includes: performing Kalman filter on the EEG sequence signal to be processed to obtain the first signal of each EEG; according to the frequency range of EEG, Perform wavelet transformation on the received EEG sequence signal to be processed to obtain a second signal of each EEG; based on the Kalman residual of the first signal, obtain a first weighting factor corresponding to the first signal; based on the quality of the second signal index to obtain a second weighting factor corresponding to the second signal; calculate and obtain a third signal according to the first signal, the first weighting factor, the second signal and the second weighting factor; perform feature extraction on the third signal of each brain wave , and classify and identify according to the feature quantity, and obtain the EEG relaxation degree. The present invention also provides an EEG relaxation degree recognition device based on Kalman filter and wavelet transform, which can accurately extract brain waves, thereby realizing accurate EEG relaxation degree recognition.

Description

technical field [0001] The invention relates to the field of relaxation therapy, in particular to a method and device for identifying the degree of relaxation of brain electricity based on Kalman filter and wavelet transform. Background technique [0002] Relaxation training is one of the most widely used techniques in behavior therapy. It is a consultation and treatment method established and developed on the basis of psychological experiments. Good curative effects have been achieved in menopausal syndrome and changing bad behavior patterns. [0003] Existing relaxation training mainly includes recording guidance, oral guidance and biofeedback guidance. Among them, the recording instruction method is rigid and unchanged, and cannot change the content according to the state of the trainee; the oral instruction requires high requirements for the object of the oral instruction, and is limited by time and place; the biofeedback instruction is mainly based on EEG feedback, whi...

AI Technical Summary

Problems solved by technology

[0005] Existing methods generally directly use a single filtering method to extract EEG, but the extraction effect of this extraction method is unstable, and it is easily affected by external factors and the performance or volatility of the filter itself, which in turn affects the final EEG. Recognition Accuracy of Relaxation

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