Subject independent emotion recognition system for people with facial deformity: an EEG based approach

• Published in: Journal of ambient intelligence and humanized computing Vol. 12; no. 2; pp. 2311 - 2320
• Main Authors: Pandey, Pallavi, Seeja, K. R.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Artificial neural networks; Classification; Computational Intelligence; Continuous wavelet transform; Deformities; Electrodes; Electroencephalography; Emotion recognition; Emotions; Engineering; Fourier transforms; Image processing; Neural networks; Original Research; Performance evaluation; Robotics and Automation; Time series; User Interfaces and Human Computer Interaction; Wavelet transforms; Affective computing; Valence arousal model; Scalogram; Convolutional neural network
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-020-02338-8

Emotion recognition from Electroencephalography (EEG) is a better choice for the people with facial deformity like where facial data is not accurate or not available for example burned or paralyzed faces. This research exploits the image processing capability of convolutional neural network (CNN) and proposes a CNN model to classify different emotions from the scalogram images of EEG data. Scalogram images from EEG obtained by applying continuous wavelet transform used for the study. The proposed model is subject independent where the objective is to extract emotion specific features from EEG data irrespective of the source of the data. The proposed emotion recognition model is evaluated on two benchmark public databases namely DEAP and SEED. In order to show the model as a purely subject independent one, the cross data base criteria is also used for evaluation. The various performance evaluation experiments show that the proposed model is comparable in terms of emotion classification accuracy.