Automated emotion state classification using higher order spectra and interval features of EEG
by Rashima Mahajan
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 34, No. 3, 2020

Abstract: Automated analysis of electroencephalogram signals for emotion state analysis has been emerging progressively towards the development of affective brain computer interfaces. However, conventional EEG signal analysis techniques such as event related potential (ERP) and power spectrum estimation fail to provide high emotion state classification rates due to Fourier phase suppression. A four-dimensional emotional model in terms of arousal, valence, liking and dominance is proposed using emotion specific EEG signals from DEAP dataset. A compact set of temporal peak/interval related features and spectral features have been extracted to map the feature space. Through the feature map, a multiclass-support vector machine (SVM) based classifier using one-against-one algorithm is configured to yield a maximum classification accuracy of 81.6% while classifying four emotional states. A comparison of multi-class-SVM with other classifiers such as feed forward neural network and radial basis function network has been made. Significant improvement using a proposed compact hybrid EEG feature set and a multi-class-SVM has been achieved for automated emotion state classification.

Online publication date: Mon, 30-Nov-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com