Open AccessRecognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier
Author(s) -
Fabian Parsia George,
Istiaque Mannafee Shaikat,
Prommy Sultana Hossain,
Mohammad Zavid Parvez,
Jia Uddin
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i2.pp1012-1020
Subject(s) - computer science , electroencephalography , support vector machine , artificial intelligence , speech recognition , pattern recognition (psychology) , classifier (uml) , emotion recognition , emotion classification , facial expression , frequency domain , gesture , psychology , computer vision , psychiatry
The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.