Affiliations: [a] Department of Electronics and Communication Engineering, Annamalai University, Chidambaram, Chidambaram, Tamil Nadu, India | [b] Department of Electronics and Communication Engineering, (Deputed from Annamalai University) Government College of Engineering, Chidambaram, Trichy, India | [c] Department of Electronics and Communication Engineering, Sagi Rama Krishnam Raju Engineering College, Bhimavaram, Andhra Pradesh, India
Correspondence:
[*]
Corresponding author: V. Satyanarayana Naga Kanuboyina, Department of Electronics and Communication Engineering, Annamalai University, Chidambaram, Chidambaram, Tamil Nadu 608002, India. E-mail: [email protected].
Abstract: In recent decades, the automatic emotion state classification is an important technology for human-machine interactions. In Electroencephalography (EEG) based emotion classification, most of the existing methodologies cannot capture the context information of the EEG signal and ignore the correlation information between dissimilar EEG channels. Therefore, in this study, a deep learning based automatic method is proposed for effective emotion state classification. Firstly, the EEG signals were acquired from the real time and databases for emotion analysis using physiological signals (DEAP), and further, the band-pass filter from 0.3 Hz to 45 Hz is utilized to eliminate both high and low-frequency noise. Next, two feature extraction techniques power spectral density and differential entropy were employed for extracting active feature values, which effectively learn the contextual and spatial information of EEG signals. Finally, principal component analysis and artificial neural network were developed for feature dimensionality reduction and emotion state classification. The experimental evaluation showed that the proposed method achieved 96.38% and 97.36% of accuracy on DEAP, and 92.33% and 89.37% of accuracy on a real-time database for arousal and valence emotion states. The achieved recognition accuracy is higher compared to the support vector machine on both databases.
Keywords: Artificial neural network, differential entropy, emotion state classification, power spectral density, principal component analysis
