Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

Wei-Long Zheng, Bao-Liang Lu
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引用次数: 1089

Abstract

To investigate critical frequency bands and channels, this paper introduces deep belief networks (DBNs) to constructing EEG-based emotion recognition models for three emotions: positive, neutral and negative. We develop an EEG dataset acquired from 15 subjects. Each subject performs the experiments twice at the interval of a few days. DBNs are trained with differential entropy features extracted from multichannel EEG data. We examine the weights of the trained DBNs and investigate the critical frequency bands and channels. Four different profiles of 4, 6, 9, and 12 channels are selected. The recognition accuracies of these four profiles are relatively stable with the best accuracy of 86.65%, which is even better than that of the original 62 channels. The critical frequency bands and channels determined by using the weights of trained DBNs are consistent with the existing observations. In addition, our experiment results show that neural signatures associated with different emotions do exist and they share commonality across sessions and individuals. We compare the performance of deep models with shallow models. The average accuracies of DBN, SVM, LR, and KNN are 86.08%, 83.99%, 82.70%, and 72.60%, respectively.
基于脑电图的深度神经网络情感识别关键频带和通道研究
为了研究关键频带和通道,本文引入深度信念网络(dbn)构建了基于脑电图的积极、中性和消极三种情绪的情绪识别模型。我们开发了一个来自15个受试者的脑电图数据集。每名受试者每隔几天做两次实验。利用从多通道脑电数据中提取的差分熵特征对脑电网络进行训练。我们检查了训练dbn的权重,并研究了关键频段和信道。选择4、6、9和12通道四种不同的配置文件。这四种剖面的识别精度相对稳定,最高准确率为86.65%,甚至优于原来62个通道的识别精度。利用训练dbn的权值确定的临界频带和信道与已有观测值一致。此外,我们的实验结果表明,与不同情绪相关的神经特征确实存在,它们在不同的会话和个体之间具有共性。我们比较了深层模型和浅层模型的性能。DBN、SVM、LR和KNN的平均准确率分别为86.08%、83.99%、82.70%和72.60%。
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来源期刊
IEEE Transactions on Autonomous Mental Development
IEEE Transactions on Autonomous Mental Development COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ROBOTICS
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