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Neural Computing and Applications

Erschienen in:

13.01.2016 | Original Article

Wavelet-based emotion recognition system using EEG signal

verfasst von: Zeynab Mohammadi, Javad Frounchi, Mahmood Amiri

Erschienen in: Neural Computing and Applications | Ausgabe 8/2017

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Abstract

In this research, emotional states in arousal/valence dimensions have been classified using minimum number of channels and frequency bands of EEG signal. Using the discrete wavelet transforms, EEG signals have been decomposed to corresponding frequency bands and then several features have been extracted. The support vector machine and K-nearest neighbor classifiers have been used to detect the emotional states from the extracted features. For the recorded 10-channel EEG signal, results illustrate the classification accuracy of 86.75 % for arousal level and 84.05 % for valence level. Moreover, using the high-frequency bands, specifically gamma band, yields higher accuracy compared to using low-frequency bands of EEG signal. All of these support to the development of a real-time emotion classification system.

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Titel: Wavelet-based emotion recognition system using EEG signal
verfasst von: Zeynab Mohammadi
Javad Frounchi
Mahmood Amiri
Publikationsdatum: 13.01.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 8/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-2149-8

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