Cargando…
Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies
Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotio...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601119/ https://www.ncbi.nlm.nih.gov/pubmed/37420342 http://dx.doi.org/10.3390/e24101322 |
_version_ | 1784816978484002816 |
---|---|
author | Nalwaya, Aditya Das, Kritiprasanna Pachori, Ram Bilas |
author_facet | Nalwaya, Aditya Das, Kritiprasanna Pachori, Ram Bilas |
author_sort | Nalwaya, Aditya |
collection | PubMed |
description | Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotion recognition system is required. Physiological signals, specifically, electrocardiogram (ECG) and electroencephalogram (EEG), were studied here for the purpose of emotion recognition. This paper proposes novel entropy-based features in the Fourier–Bessel domain instead of the Fourier domain, where frequency resolution is twice that of the latter. Further, to represent such non-stationary signals, the Fourier–Bessel series expansion (FBSE) is used, which has non-stationary basis functions, making it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes using FBSE-based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector, which are further used to develop machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals. |
format | Online Article Text |
id | pubmed-9601119 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96011192022-10-27 Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies Nalwaya, Aditya Das, Kritiprasanna Pachori, Ram Bilas Entropy (Basel) Article Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotion recognition system is required. Physiological signals, specifically, electrocardiogram (ECG) and electroencephalogram (EEG), were studied here for the purpose of emotion recognition. This paper proposes novel entropy-based features in the Fourier–Bessel domain instead of the Fourier domain, where frequency resolution is twice that of the latter. Further, to represent such non-stationary signals, the Fourier–Bessel series expansion (FBSE) is used, which has non-stationary basis functions, making it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes using FBSE-based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector, which are further used to develop machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals. MDPI 2022-09-20 /pmc/articles/PMC9601119/ /pubmed/37420342 http://dx.doi.org/10.3390/e24101322 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nalwaya, Aditya Das, Kritiprasanna Pachori, Ram Bilas Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies |
title | Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies |
title_full | Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies |
title_fullStr | Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies |
title_full_unstemmed | Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies |
title_short | Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies |
title_sort | automated emotion identification using fourier–bessel domain-based entropies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601119/ https://www.ncbi.nlm.nih.gov/pubmed/37420342 http://dx.doi.org/10.3390/e24101322 |
work_keys_str_mv | AT nalwayaaditya automatedemotionidentificationusingfourierbesseldomainbasedentropies AT daskritiprasanna automatedemotionidentificationusingfourierbesseldomainbasedentropies AT pachorirambilas automatedemotionidentificationusingfourierbesseldomainbasedentropies |