Wearable emotional state recognition device and method based on electroencephalogram signals

Abstract

The invention discloses a wearable emotional state recognition device and method based on electroencephalogram signals, relates to the technical field of electroencephalogram signal analysis, and aims to improve the accuracy of electroencephalogram signal acquisition and analysis. The wearable emotional state recognition device based on the electroencephalogram signals comprises a collection electrode module with a headband as a carrier, a host module, an upper computer and an emotion indication module, wherein the host module is used for carrying out preprocessing and communication on the electroencephalogram signals; and the upper computer is used for analyzing the electroencephalogram signals. When the wearable emotional state recognition device based on the electroencephalogram signals is in a user wearing state, the eight collection electrodes make contact with two positions of the frontal lobe, four positions of the temporal lobe and two positions of the occipital lobe of the head of the human body in a one-to-one correspondence mode. The wearable emotional state recognition device based on the electroencephalogram signals provided by the technical scheme is applied to the emotion classification method.

Description

technical field [0001] The present invention relates to the technical field of emotion classification based on EEG signals, in particular to a wearable emotional state recognition device and method based on EEG signals. Background technique [0002] Among the many means of studying emotions, EEG signals have attracted the attention of many researchers due to their high time resolution and non-disguised features, and are one of the commonly used methods in emotion research. Emotion-related brain regions are widely distributed, and different brain regions have certain differences in response to different emotions. Based on multi-channel EEG data and comprehensive EEG characteristics of various brain regions, scholars from various countries have achieved many valuable results. [0003] At present, in the case of using multi-channel EEG emotion classification, such as using 32 electrode channels, although the signal sampling rate is high, there are many problems. For example, ...

AI Technical Summary

Problems solved by technology

For example, the cost of manufacturing and using multi-channel EEG signal acquisition devices is high, requiring as many as 32 acquisition electrodes, and professional personnel are also required to assist in the installation and signal acquisition of debugging devices

In addition, from the point of view of the accuracy of the signal, the acquisition electrodes generate signal interference with each other, so that the contribution of a large number of acquisition electrodes in the signal acquisition process is low, and the effectiveness of the collected EEG signals is not high.

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