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Feature extraction and selection for emotion recognition from EEG

Jenke, Robert; Peer, Angelika; Buss, Martin


Robert Jenke

Angelika Peer

Martin Buss


© 2014 IEEE. Emotion recognition from EEG signals allows the direct assessment of the 'inner' state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes.


Jenke, R., Peer, A., & Buss, M. (2014). Feature extraction and selection for emotion recognition from EEG. IEEE Transactions on Affective Computing, 5(3), 327-339.

Journal Article Type Article
Publication Date Jul 1, 2014
Journal IEEE Transactions on Affective Computing
Print ISSN 1949-3045
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 5
Issue 3
Pages 327-339
Keywords feature extraction, feature selection, emotion recognition, EEG
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