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

• Published in: Expert systems with applications Vol. 47; pp. 35 - 41
• Main Authors: Atkinson, John, Campos, Daniel
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2016
• Subjects: Brain–Computer Interfaces; Classification; Classifiers; Computation; EEG; Electroencephalography; Emotion classification; Emotion recognition; Emotions; Feature recognition; Feature selection; Recognition; Tasks; Emotion recognition; Feature selection; Brain–Computer Interfaces; EEG; Emotion classification
• DOI: 10.1016/j.eswa.2015.10.049
• ISSN: 0957-4174

•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. Current emotion recognition computational techniques have been successful on associating the emotional changes with the EEG signals, and so they can be identified and classified from EEG signals if appropriate stimuli are applied. However, automatic recognition is usually restricted to a small number of emotions classes mainly due to signal’s features and noise, EEG constraints and subject-dependent issues. In order to address these issues, in this paper a novel feature-based emotion recognition model is proposed for EEG-based Brain–Computer Interfaces. Unlike other approaches, our method explores a wider set of emotion types and incorporates additional features which are relevant for signal pre-processing and recognition classification tasks, based on a dimensional model of emotions: Valenceand Arousal. It aims to improve the accuracy of the emotion classification task by combining mutual information based feature selection methods and kernel classifiers. Experiments using our approach for emotion classification which combines efficient feature selection methods and efficient kernel-based classifiers on standard EEG datasets show the promise of the approach when compared with state-of-the-art computational methods.