Emotion Recognition System based on EEG signal: A Comparative Study of Different Features and Classifiers

M. K. Ahirwal, M. Kose
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引用次数: 18

Abstract

In this paper, emotion recognition system based on electroencephalogram (EEG) signals has been implemented. The new technology of emotion recognition using physiological signal is based on pattern recognition and classification problem that is well described in this paper. A very famous dataset of EEG signals for emotion recognition known as DEAP dataset is used and three categories of features, time domain features, frequency domain features and entropy based features has been extracted. Classification through support vector machine (SVM), artificial neural networks (ANN) and Naïve bayes (NB) has been done. Performance of system is observed by the parameters like, classification accuracy, precision and recall. After performance observation, it is found that ANN gives the best performance with all types of features. Highest classification accuracy achieved by ANN for said dataset, entropy based features and implementation is 93.75 percent.
基于脑电信号的情绪识别系统:不同特征和分类器的比较研究
本文实现了一种基于脑电图信号的情绪识别系统。基于生理信号的情绪识别新技术是基于模式识别和分类问题的。使用了一个非常著名的用于情绪识别的脑电信号数据集——DEAP数据集,并提取了时域特征、频域特征和基于熵的特征三大类特征。通过支持向量机(SVM)、人工神经网络(ANN)和Naïve贝叶斯(NB)进行了分类。通过分类准确率、精密度和召回率等参数来观察系统的性能。经过性能观察,发现在所有类型的特征下,人工神经网络的性能都是最好的。对于上述数据集,基于熵的特征和实现,人工神经网络实现的最高分类准确率为93.75%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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