Animal-emotion electroencephalogram analysis device

Abstract

The invention belongs to the field of brain-computer interface devices, and particularly relates to an animal-emotion electroencephalogram analysis device. The animal-emotion electroencephalogram analysis device comprises an animal electroencephalogram collection module, a preprocessing module, a feature classification module and an analysis-result output module. By means of the animal-emotion electroencephalogram analysis device, eye electrical noise, drifting noise and high-frequency noise in single-electrode original electroencephalogram signals can be removed, and according to feature classification based on machine learning, current-emotion state values of animals are obtained, broadcasted and displayed. Electroencephalogram is collected through a claw-type dry electrode, and can penetrate through hair to be in contact with the cortex, and is combined with the preprocessing module, and it can be guaranteed that the signal to noise ratio is high, and wearing is more convenient; classification is completed through machine learning, and the effectiveness is guaranteed; meanwhile, the time resolution ratio can be increased, and finer emotional state changes can be caught. The animal-emotion electroencephalogram analysis device has the advantages of being fast and convenient to wear and high in sensitivity and accuracy rate, and is suitable for application and popularization.

Description

technical field [0001] The invention belongs to the field of brain-computer interface equipment, in particular to an animal emotion EEG analysis equipment. Background technique [0002] In the field of brain-computer interface technology, the existing technology uses human as the object to design wearable devices for brain wave analysis. Chinese patent document CN 105011932 A discloses a fatigue driving EEG monitoring method based on meditation degree and concentration, using the concentration parameters and meditation degree parameters output by the TGAM chip, combined with the average value of the original EEG signal, the driver's Fatigue EEG signals were monitored. However, in the prior art, the concentration parameters and meditation parameters output by the TGAM chip can only calculate brainwave parameters at a frequency of 1 Hz, and changes in brainwave states such as emotions are often fleeting, requiring higher Only time resolution can be detected. [0003] In add...

AI Technical Summary

Problems solved by technology

However, in the prior art, the concentration parameters and meditation parameters output by the TGAM chip can only calculate brainwave parameters at a frequency of 1 Hz, and changes in brainwave states such as emotions are often fleeting, requiring higher time resolution to detect

[0003] In addition, in the prior art, the oculoelectric noise in the original signal is not removed, the signal-to-noise ratio of the data is low, and the reliability is poor, and the removal of oculoelectric noise in other existing technologies relies on principal component analysis and independent component analysis methods, which requires Separately set the electro-oculogram collecting electrodes located around the eyes, which increases the complexity of the equipment. At the same time, these two denoising methods require a long time, which is difficult to meet the real-time requirements

In the classification method, the use of PCA dimensionality reduction often leads to the loss of detailed information in the signal, making it difficult to capture subtle emotional changes

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