Discrete Wavelet Transform Based Classification of Human Emotions Using Electroencephalogram Signals
Problem statement: The aim of this study was to report the human emotion assessment using Electroencephalogram (EEG). Approach: An audio-visual induction based protocol was designed for inducing five different emotions (happy, surprise, fear, disgust and neutral) on 20 subjects in the age group of 19~39 years. EEG signals are recorded from 64 channels placed over entire scalp according to International 10-10 system. We firstly applied Spatial Filtering technique to remove the noises and artifacts from the EEG signals. Three wavelet functions ("db8", "sym8" and "coif5") were used to decompose the EEG signal into five different frequency bands namely: delta, theta, alpha, beta and gamma. A set of new statistical features related to energy were extracted from the EEG frequency bands to construct the feature vector for classifying the emotions. Two simple linear classifiers (K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA)) were used for mapping the feature vector into corresponding emotions. Furthermore, we compared the efficacy of emotion classification with a reduced set of channels (24 channels) for evaluating the reliability of the emotion recognition system. Results: In this study, 62 channels outperform 24 channels by giving the maximum average classification accuracy of 79.65% using KNN and 78.52% using LDA. Conclusion: In this study we presented an approach to discrete emotion recognition based on the processing of EEG signals. The preliminary results resented in this study address the classifiability of human emotions using original and reduced set of EEG channels. The results presented in this study indicated that, statistical features extracted from time-frequency analysis (wavelet transform) works well in the context of discrete emotion classification.
How to Cite
Rizon, M. (2010). Discrete Wavelet Transform Based Classification of Human Emotions Using Electroencephalogram Signals. American Journal of Applied Sciences, 7(7), 878-885. https://doi.org/10.3844/ajassp.2010.878.885
© 2020 Mohamed Rizon. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- discrete wavelet transform
- k-Nearest Neighbor (kNN)
- Linear Discriminant Analysis (LDA)