Title: Automated emotion state classification using higher order spectra and interval features of EEG

Authors: Rashima Mahajan

Addresses: Faculty of Engineering and Technology, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India

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.

Keywords: brain computer interface; BCI; electroencephalogram; EEG; emotions; multi-class-SVM; trispectrum; temporal; DEAP.

DOI: 10.1504/IJBET.2020.111475

International Journal of Biomedical Engineering and Technology, 2020 Vol.34 No.3, pp.284 - 304

Received: 25 Apr 2017
Accepted: 30 Nov 2017
Published online: 30 Nov 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article