Discrete wavelet packet transform for electroencephalogram-based emotion recognition in the valence-arousal space

• Main Authors: Ahmad, Farzana Kabir, Olakunle, Oyenuga Wasiu
• Format: Conference or Workshop Item
• Language: English
• Published: 2015
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/17009/1/30.pdf; http://repo.uum.edu.my/17009/; http://aics2015.com/

Human emotion recognition is the key step toward innovative human-computer interactions.The advanced in computational algorithms and techniques has recently offered the promising results in recognizing human emotion.Recently, Electroencephalogram (EEG) has been shown as an effective way in identifying human emotion since it records the brain activity of human and can hardly be deceived by voluntary control.However, due to the non-linearity, non-stationary, and chaotic nature of the EEG signals, it is difficult to be examined and has been an extensive research area in the present years. Moreover, the high dimensional of the feature vectors has make the analysis task more challenging. In this research, two emotion recognition experiments were performed in order to classify human emotional states into high/low valence or high/low arousal. The first experiment was aimed to evaluate the performance of Discrete Wavelet Packet Transform (DWPT) in extracting relevant features, while the second experiment was conducted to identify the combination of electrode channels that optimally recognize emotions based on the valence-arousal model. Additionally, in this study, a leave-one-out cross validation was performed using Radial Basis Function-Support Vector Machines (RBF-SVM) as the classifier on a public ally available data set. The experimental results have shown that an average accuracy of 68.83% with average F1-score of 0.666 for valence and average accuracy of 68.83% with F1-score of 0.633 for arousal were achieved for 32 subjects. Furthermore, four frontal channels which include Fpl, Fp2, F3, and, F4 were identified significant whereas remaining 6 channels namely T7, T8, P3, P4, 01, and 02 are irrelevant for EEG-based emotion recognition in the valence-arousal space.