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Emotion Identification From TQWT-Based EEG Rhythms

Emotion Identification From TQWT-Based EEG Rhythms

Aditya Nalwaya, Kritiprasanna Das, Ram Bilas Pachori
Copyright: © 2022 |Pages: 22
ISBN13: 9781668439470|ISBN10: 1668439476|EISBN13: 9781668439487
DOI: 10.4018/978-1-6684-3947-0.ch011
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MLA

Nalwaya, Aditya, et al. "Emotion Identification From TQWT-Based EEG Rhythms." AI-Enabled Smart Healthcare Using Biomedical Signals, edited by Rahul Kumar Chaurasiya, et al., IGI Global, 2022, pp. 195-216. https://doi.org/10.4018/978-1-6684-3947-0.ch011

APA

Nalwaya, A., Das, K., & Pachori, R. B. (2022). Emotion Identification From TQWT-Based EEG Rhythms. In R. Chaurasiya, D. Agrawal, & R. Pachori (Eds.), AI-Enabled Smart Healthcare Using Biomedical Signals (pp. 195-216). IGI Global. https://doi.org/10.4018/978-1-6684-3947-0.ch011

Chicago

Nalwaya, Aditya, Kritiprasanna Das, and Ram Bilas Pachori. "Emotion Identification From TQWT-Based EEG Rhythms." In AI-Enabled Smart Healthcare Using Biomedical Signals, edited by Rahul Kumar Chaurasiya, Dheeraj Agrawal, and Ram Bilas Pachori, 195-216. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-3947-0.ch011

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Abstract

Electroencephalogram (EEG) signals are the recording of brain electrical activity, commonly used for emotion recognition. Different EEG rhythms carry different neural dynamics. EEG rhythms are separated using tunable Q-factor wavelet transform (TQWT). Several features like mean, standard deviation, information potential are extracted from the TQWT-based EEG rhythms. Machine learning classifiers are used to differentiate various emotional states automatically. The authors have validated the proposed model using a publicly available database. Obtained classification accuracy of 92.9% proves the candidature of the proposed method for emotion identification.

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