Brain wave relaxation degree identification method and device based on time domain and frequency domain space

Abstract

The invention discloses a brain wave relaxation degree identification method and device based on time-domain and frequency domain space. The method comprises the following steps of performing filtration on a brain wave sequence signal to be treated, extracting, and obtaining signal waves; separating each signal wave into segments, calculating the maximum projected value and the minimum projected value on the y axis of the corresponding segments to the signal waves, defining at least two equal-length intervals according the maximum value and the minimum value; also obtaining the grid projection variability of the signal waves and the characteristic quantity in the time domain space according to the projection amounts at the intervals; calculating the energy of the signal waves, according to the frequency range of each brave wave and the energy of the signal waves, calculating the energy ratio of any two signal wave, and obtaining the characteristic quantity in the frequency domain space; classifying and identifying the characteristic quantity on the time domain space and the characteristic quantity on the frequency domain space, and obtaining the brain wave relaxation degree. The method can extract the characteristic quantities from the brain waves on different dimensions and in space, thereby precisely identifying the brain wave relaxation degree.

Description

technical field [0001] The present invention relates to the field of relaxation therapy, in particular to a method and device for identifying EEG relaxation based on time domain and frequency domain space. 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, which can Combining th...

AI Technical Summary

Problems solved by technology

[0005] The existing feature extraction methods generally can only extract the features of brain waves from a single angle, and the evaluation method is single, which cannot guarantee the accuracy of the classification results

Moreover, the calculation and processing process of the existing feature extraction algorithm is complicated. On the one hand, it increases the requirements for hardware. On the other hand, due to the complicated calculation, the classification result cannot be obtained in time, which affects the effect of relaxation therapy.

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