Emotion cognition method based on electroencephalogram signal feature analysis

Abstract

The invention discloses an emotion cognition method based on electroencephalogram signal feature analysis. The method comprises the following steps of: S1, obtaining a corresponding type of subject, and collecting electroencephalogram signals which are induced by the subject under different emotions and are used for reference and analysis; S2, performing denoising and separating processing on the electroencephalogram signals, and performing feature extraction and analysis based on a method of combining Hilbert transform and information entropy; and S3, calculating the Hilbert spectral entropy of the electroencephalogram signals in different emotional states, and performing statistical analysis. According to the method, the electroencephalogram signals of the subject under different emotions are obtained, then Hilbert transformation and information entropy are combined, the Hilbert spectral entropy of electroencephalogram rhythms of different brain regions and different genders under different emotional states is analyzed, better statistical performance is achieved, changes of time-frequency domain complexity of the electroencephalogram signals are represented, the change rule of the amplitude of the signals along with time and frequency in the whole frequency band is accurately described, the signal analysis efficiency is improved, the Hilbert spectral entropy is more reliable than approximate entropy, and the method is more comprehensive than single time domain analysis and frequency domain analysis.

Description

technical field [0001] The present invention relates to the technical field of EEG signal analysis in the process of emotional cognition, in particular to an emotional cognition method based on the analysis of EEG signal characteristics. Background technique [0002] At present, signal characteristic analysis methods can be divided into three categories: time-domain analysis methods, such as zero-crossing point analysis, histogram analysis, variance analysis, peak detection and waveform parameter analysis, correlation analysis, coherent averaging, waveform identification, etc., these methods They all start with the EEG waveform, and the characteristics of the analysis are basically the geometric properties of the EEG waveform, such as amplitude, frequency, time course, mean, variance, skewness, kurtosis, etc. The analyst does not need knowledge of the EEG waveform With an in-depth understanding, you can intuitively and accurately observe the time-domain characteristic change...

AI Technical Summary

Problems solved by technology

[0003] The shortcomings of the existing technology are that, firstly, the time-domain characteristics of the time-domain analysis come from the whole EEG, which can describe the amplitude and wavelength of a single EEG, but in the time domain, two signals with similar waveforms cannot However, there may be great differences, and only a simple relationship between different brain regions can be obtained through time-domain analysis, and the anti-false ability is very poor when analyzing the transient changes of brain activity

[0004] The main purpose of the frequency domain analysis method is to transform the signal in the time domain into the frequency domain through a certain algorithm to reflect the characteristics of the signal changing with frequency, but it cannot obtain high time domain resolution and frequency domain resolution at the same time. The 'uncertainty principle' of this resolution hinders the study of EEG signals

[0005] The time-frequency analysis method has opened up a new way to study the activity and mechanism of the brain, but the traditional time-frequency analysis method has shortcomings. Ensure accurate classification of input samples

Images