基于脑电图的颞卷积神经网络情感识别

Liuqing Yang, Jiwei Liu
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引用次数: 6

摘要

基于生理信号的情绪识别在智能人机界面设计、情绪障碍诊断等方面有着广泛的应用。传统的方法需要先验知识来设计和提取生理信号的一系列特征。传统方法由于缺乏高级特征,泛化能力较差。利用深度学习方法来分析生理信号,如脑电图,在情绪识别中变得越来越有吸引力。本文针对脑电情绪识别中生理信号的时间依赖性,设计了一种基于深度学习的序列模型,利用时序卷积网络(TCN)提取高阶特征。具体来说,我们以秒为单位提取微分熵特征,构造一个固定长度的时间窗数据作为TCN输入的时间序列,然后使用softmax进行分类。此外,为了得到可靠的结果,我们根据试验来划分样本,避免了测试集样本和训练集样本来自同一试验。具体来说,我们首先根据试验将样本划分为测试集和训练集,然后将固定时间窗长度的测试集和训练集中的试验分别分割,以获得更多的样本。为了评估该模型的性能,我们在DEAP数据库上进行了情感分类实验。实验结果表明了该模型在脑电情绪识别中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EEG-Based Emotion Recognition Using Temporal Convolutional Network
Emotion recognition based on physiological signal can be used in many applications such as, intelligent human-computer interface design, emotional disorder diagnoses. For traditional approaches, the prior knowledge is required to design and extract a range of features from physiological signal. The generalization ability of traditional methods is poor because of the lack of high-level features. Using deep-learning methodologies to analyze physiological signal, i.e. eeg, becomes increasingly attractive for recognizing emotions. In this paper, we design a sequence model based on deep-learning that uses Temporal Convolutional Network(TCN) to extract high-level features in consideration of the time dependence of physiological signals for EEG emotion recognition. Specifically, we extract the differential entropy feature in seconds and construct a time series with fixed-length time window data as the input to TCN, and then using softmax to classify. Furthermore, in order to get reliable results, we divide the samples according to the trials, avoiding the testing set samples and training set samples from the same trial. Specifically, we first divide the samples according to the trials as the testing set and the training set, and then segment the trials in the testing set and training set with fixed time window length to obtain more samples respectively. To evaluate the performance of the proposed model, we conduct the emotion classification experiments on DEAP database. The experimental results show the effectiveness of our proposed model for EEG emotion recognition.
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