文献类型：期刊文献

英文题名：Performance comparison of machine learning algorithms for EEG-signal-based emotion recognition

作者：Chen, Peng[1]; Zhang, Jianhua[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2017

卷号：10613 LNCS

起止页码：208

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20174704429085)

语种：英文

外文关键词：Classification (of information) - Extraction - Dynamics - Electroencephalography - Nearest neighbor search - Feature extraction - Speech recognition - Clustering algorithms - Learning algorithms - Cluster analysis - Learning systems - Biomedical signal processing - Decision trees - Emotion Recognition

摘要：In this paper, we use the DEAP database to investigate emotion recognition problem. Firstly we use data clustering technique to determine four target classes of human emotional state. Then we compare two different feature extraction methods: one is wavelet transform and another is nonlinear dynamics. Furthermore, we examine the effect of feature reduction on classification performance. Finally, we compare the performance of four different classifiers, including k-nearest neighbor, na?ve Bayesian, support vector machine, and random forest. The results show the effectiveness of Kernel Spectral Regression (KSR) and random forest based classifier for emotion recognition and analysis. ? Springer International Publishing AG 2017.