The automatic assessment of emotional states has important applications in human-computer interfaces and marketing. Several approaches use a dimensional characterization of emotional states along with features extracted from physiological signals to classify emotions elicited from complex audiovisual stimuli; however, the classification accuracy remains low. Here, we develop an emotion assessment approach using a variability-based ranking scheme to reveal relevant coherence features from electroencephalography (EEG) signals. Our method achieves higher classification accuracies than comparable state-of-the-art methods and almost matches the performance of multimodal strategies that require information from several physiological signals.
CITATION STYLE
De La Pava, I., Álvarez-Meza, A., & Orozco, A. A. (2018). Emotion assessment by variability-based ranking of coherence features from EEG. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10657 LNCS, pp. 203–211). Springer Verlag. https://doi.org/10.1007/978-3-319-75193-1_25
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