Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition
Electroencephalogram (EEG) based emotion recognition has received considerable attention as it is a non-invasive method of acquiring physiological signals from the brain and it could directly reflect emotional states. However, the challenging issues regarding EEG-based emotional state recognition is...
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QA71-90 Instruments and machines Olakunle, Oyenuga Wasiu Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition |
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Electroencephalogram (EEG) based emotion recognition has received considerable attention as it is a non-invasive method of acquiring physiological signals from the brain and it could directly reflect emotional states. However, the challenging issues regarding EEG-based emotional state recognition is that it requires well-designed methods and algorithms to extract necessary features from the complex, chaotic, and multichannel EEG signal in order to achieve optimum classification performance. The aim of this study is to discover the feature extraction method and the combination of electrode channels that optimally implements EEG-based valencearousal emotion recognition. Based on this, two emotion recognition experiments
were performed 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) as a feature extraction method. The second experiment was aimed at identifying the combination of electrode channels that optimally recognize emotions based on the valence-arousal model in EEG
emotion recognition. In order to evaluate the results of this study, a benchmark EEG dataset was used to implement the emotion classification. In the first experiment, the
entropy features of the theta, alpha, beta, and gamma bands through the 10 EEG channels Fp1, Fp2, F3, F4, T7, T8, P3, P4, O1, and O2 were extracted using DWPT and Radial Basis Function-Support Vector Machine (RBF-SVM) was used as the
classifier. In the second experiment, the classification experiments were repeated
using the 4 EEG frontal channels Fp1, Fp2, F3, and F4. The result of the first experiment showed that entropy features extracted using DWPT are better than bandpower features. While the result of the second classification experiment shows that the combination of the 4 frontal channels is more significant than the
combination of the 10 channels |
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Olakunle, Oyenuga Wasiu |
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Olakunle, Oyenuga Wasiu |
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Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition |
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Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition |
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Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition |
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Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition |
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Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition |
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discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition |
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Awang Had Salleh Graduate School of Arts & Sciences |
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my-uum-etd.52812021-03-18T08:37:28Z Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition 2015 Olakunle, Oyenuga Wasiu Kabir Ahmad, Farzana Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA71-90 Instruments and machines Electroencephalogram (EEG) based emotion recognition has received considerable attention as it is a non-invasive method of acquiring physiological signals from the brain and it could directly reflect emotional states. However, the challenging issues regarding EEG-based emotional state recognition is that it requires well-designed methods and algorithms to extract necessary features from the complex, chaotic, and multichannel EEG signal in order to achieve optimum classification performance. The aim of this study is to discover the feature extraction method and the combination of electrode channels that optimally implements EEG-based valencearousal emotion recognition. Based on this, two emotion recognition experiments were performed 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) as a feature extraction method. The second experiment was aimed at identifying the combination of electrode channels that optimally recognize emotions based on the valence-arousal model in EEG emotion recognition. In order to evaluate the results of this study, a benchmark EEG dataset was used to implement the emotion classification. In the first experiment, the entropy features of the theta, alpha, beta, and gamma bands through the 10 EEG channels Fp1, Fp2, F3, F4, T7, T8, P3, P4, O1, and O2 were extracted using DWPT and Radial Basis Function-Support Vector Machine (RBF-SVM) was used as the classifier. In the second experiment, the classification experiments were repeated using the 4 EEG frontal channels Fp1, Fp2, F3, and F4. The result of the first experiment showed that entropy features extracted using DWPT are better than bandpower features. While the result of the second classification experiment shows that the combination of the 4 frontal channels is more significant than the combination of the 10 channels 2015 Thesis https://etd.uum.edu.my/5281/ https://etd.uum.edu.my/5281/1/s815456.pdf text eng public https://etd.uum.edu.my/5281/2/s815456_abstract.pdf text eng public masters masters Universiti Utara Malaysia Aftanas, L. I., Reva, N. V., Varlamov, A. A., Pavlov, S. V., & Makhnev, V. P. (2004). 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