Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers

作者：

Highlights：

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• A feature-based emotion recognition model is proposed for EEG-based BCI.

• The approach combines statistical-based feature selection methods and SVM emotion classifiers.

• The model is based on Valence/Arousal dimensions for emotion classification.

• Our combined approach outperformed other recognition methods.

摘要

•A feature-based emotion recognition model is proposed for EEG-based BCI.•The approach combines statistical-based feature selection methods and SVM emotion classifiers.•The model is based on Valence/Arousal dimensions for emotion classification.•Our combined approach outperformed other recognition methods.

论文关键词：Emotion recognition,Brain–Computer Interfaces,EEG,Feature selection,Emotion classification

论文评审过程：Received 31 March 2015, Revised 30 October 2015, Accepted 31 October 2015, Available online 26 November 2015, Version of Record 4 December 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.10.049