基于EEG和DE-CNN-GRU的情绪识别

近年,情绪识别研究已经不再局限于面部和语音识别,基于脑电等生理信号的情绪识别日趋火热.但由于特征信息提取不完整或者分类模型不适应等问题,使得情绪识别分类效果不佳.基于此,本文提出一种微分熵(DE)、卷积神经网络(CNN)和门控循环单元(GRU)结合的混合模型(DE-CNN-GRU)进行基于脑电的情绪识别研究.将预处理后的脑电信号分成5个频带,分别提取它们的DE特征作为初步特征,输入到CNN-GRU模型中进行深度特征提取,并结合Softmax进行分类.在SEED数据集上进行验证,该混合模型得到的平均准确率比单独使用CNN或GRU算法的平均准确率分别高出5.57%与13.82%.

Emotion Recognition Based on EEG and DE-CNN-GRU

In recent years, research on emotion recognition has no longer only focused on facial and voice recognition, and emotion recognition according to electroencephalogram (EEG)-based physiological signals is becoming increasingly popular. However, due to the incomplete extraction of feature information or the maladjustment of classification models, the classification effect of emotion recognition is poor. Therefore, this study proposes a hybrid model combining differential entropy (DE), convolutional neural network (CNN), and gated recurrent unit (GRU), namely, DE-CNN-GRU, to study EEG-based emotion recognition. The pre-processed EEG signals are divided into five frequency bands, and their DE features are extracted as preliminary features, which are then input to the CNN-GRU model for deep feature extraction and further classified by using Softmax. The hybrid model is tested on the SEED dataset. The result shows that the average accuracy obtained by the hybrid model is 5.57% and 13.82% higher than that obtained by using the CNN or GRU algorithm, respectively.