Electroencephalogram emotion recognition method based on DEDBN-ELM

Abstract

The invention relates to an electroencephalogram emotion recognition method based on DEDBN-ELM, and belongs to the field of human-computer interaction. The method comprises the steps that S1, decomposing electroencephalogram signals of all channels through EMD, and obtaining a series of IMF components; S2, selecting an IMF component according to the variance contribution rate; S3, constructing a DBN network for each electroencephalogram channel to extract the selected IMF components, and deep features of each channel are obtained respectively; and S4, taking the deep features of the pluralityof channels as the input of an extreme learning machine (ELM), and carrying out feature learning and classification. According to the method, it can be guaranteed that the recognition rate is high androbustness is good when feature extraction and classification are carried out on the positive emotion state, the negative emotion state and the neutral emotion state in the electroencephalogram dataset.

Description

technical field [0001] The invention belongs to the field of human-computer interaction, and relates to an EEG emotion recognition method based on DEDBN-ELM. Background technique [0002] At present, emotion recognition mainly focuses on two aspects of human non-physiological signals and physiological signals. Emotion recognition based on physiological signals can avoid the camouflage and subjectivity of emotions, so it can better evaluate people's emotional state. In emotion recognition based on physiological signals, since the electroencephalogram (EEG) signal is directly extracted from the brain and can better reflect the activity state of the brain, it has attracted much attention and has become the focus of research in the field of emotion recognition. At present, wavelet transform, wavelet packet transform and empirical mode decomposition (EMD) methods have been proposed. EMD can perform adaptive decomposition according to the characteristics of the signal itself, an...

AI Technical Summary

Problems solved by technology

EMD can perform adaptive decomposition according to the characteristics of the signal itself, and can better deal with nonlinear and non-stationary signals such as EEG signals. Missing or introducing extraneous features

Deep Belief Network (DBN) has great advantages in processing big data, but directly processing raw EEG data can easily cause information redundancy, and the recognition rate needs to be further improved

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