融合微分熵的高泛化能力脑电情绪识别模型

doi:10.11896/jsjkx.231200066

Abstract

Abstract: With the advent of deep learning,the study of EEG signals has been further developed,and the commonly used me-thods for classification of emotions based on deep learning include artificial neural network(ANN) and deep learning(DL).How-ever,EEG signals are limited sample data,and for networks such as deep learning,which require a large amount of data-driven training to complete classification tasks,how to improve the effect and generalization performance of classification tasks with a limited amount of data is a research focus.In order to solve the problem of the influence of the real environment on the EEG signal and the generalization of the neural network model in EEG research,this paper fully excavates the information contained in the EEG signal,proposes a deep learning model that considers both the original EEG signal and the DE feature,and designs the data acquisition process and processing process of the experiment.Experiments are carried out on DEAP dataset,SEED dataset and experimental data to evaluate the performance effect and generalization ability of the built network,and to explore the correlation between deep learning networks in emotion classification on EEG signals.The network model and feature processing method constructed in this paper obtain an accuracy of 85.62% in the sentiment tri-classification on the SEED dataset.The accuracy of 59.38% and 61.70% is obtained in the emotional binary classification of the two dimensions of valence and arousal of the original EEG on the DEAP dataset,respectively.

Key words: Emotion recognition, Emotion classification, Electroencephalogram(EEG) signals, Differential entropy(DE), Deep learning

CLC Number:

TP183

LI Zhengping, LI Hanwen, WANG Lijun. High-generalization Ability EEG Emotion Recognition Model with Differential Entropy[J].Computer Science, 2024, 51(11A): 231200066-7.

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High-generalization Ability EEG Emotion Recognition Model with Differential Entropy

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