Authors: V. Vanitha; P. Krishnan
Addresses: National Centre for Sustainable Coastal Management (NCSCM), Anna University Campus, Chennai, Tamil Nadu, India ' National Centre for Sustainable Coastal Management (NCSCM), Anna University Campus, Chennai, Tamil Nadu, India
Abstract: Emotion detection has crucial role in many domains especially in health and e-learning sector. This study aims to improve the accuracy in detecting emotions using brain activity. It addresses two primary problems associated with current emotion recognition systems. Firstly, these existing systems can classify only small classes of emotion. Secondly, analysis of the EEG is complex due to its non-stationary and non-linear characteristics. We conducted experiments to record EEG of subjects using 14 electrodes attached directly to the scalp based on International 10-20 system. To remove artefacts, raw signals are pre-processed. Emotional patterns associated with EEG are detected on time-frequency domain using Hilbert-Huang Transform (HHT). Multiclass Support Vector Machine classifier (MC-SVM) is used to distinguish emotions from recorded data based on the instantaneous frequency obtained through HHT. The results revealed the effectiveness of the suggested time-frequency-based analysis method to detect wide range of emotions using EEG signals.
Keywords: emotion recognition; brain-computer interface; BCI; EEG signals; electroencephalograms; SVM; support vector machines; Hilbert-Huang transform; HHT; time-frequency analysis; emotion classification; emotion detection; emotions.
International Journal of Biomedical Engineering and Technology, 2017 Vol.23 No.2/3/4, pp.191 - 212
Received: 19 Aug 2016
Accepted: 27 Sep 2016
Published online: 24 Feb 2017 *