PMID- 37420342
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 1099-4300 (Electronic)
IS  - 1099-4300 (Linking)
VI  - 24
IP  - 10
DP  - 2022 Sep 20
TI  - Automated Emotion Identification Using Fourier-Bessel Domain-Based Entropies.
LID - 10.3390/e24101322 [doi]
LID - 1322
AB  - 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.
FAU - Nalwaya, Aditya
AU  - Nalwaya A
AD  - Department of Electrical Engineering, Indian Institute of Technology Indore, 
      Indore 453552, India.
FAU - Das, Kritiprasanna
AU  - Das K
AUID- ORCID: 0000-0001-6920-4763
AD  - Department of Electrical Engineering, Indian Institute of Technology Indore, 
      Indore 453552, India.
FAU - Pachori, Ram Bilas
AU  - Pachori RB
AD  - Department of Electrical Engineering, Indian Institute of Technology Indore, 
      Indore 453552, India.
LA  - eng
GR  - 22(0851)/20/EMR-II/Council of Scientific & Industrial Research (CSIR), India/
PT  - Journal Article
DEP - 20220920
PL  - Switzerland
TA  - Entropy (Basel)
JT  - Entropy (Basel, Switzerland)
JID - 101243874
PMC - PMC9601119
OTO - NOTNLM
OT  - ECG
OT  - EEG
OT  - FBSE-EWT
OT  - Fourier-Bessel series expansion
OT  - spectral entropy
COIS- The authors declare no conflict of interest.
EDAT- 2023/07/08 10:43
MHDA- 2023/07/08 10:44
PMCR- 2022/09/20
CRDT- 2023/07/08 01:09
PHST- 2022/08/07 00:00 [received]
PHST- 2022/09/09 00:00 [revised]
PHST- 2022/09/16 00:00 [accepted]
PHST- 2023/07/08 10:44 [medline]
PHST- 2023/07/08 10:43 [pubmed]
PHST- 2023/07/08 01:09 [entrez]
PHST- 2022/09/20 00:00 [pmc-release]
AID - e24101322 [pii]
AID - entropy-24-01322 [pii]
AID - 10.3390/e24101322 [doi]
PST - epublish
SO  - Entropy (Basel). 2022 Sep 20;24(10):1322. doi: 10.3390/e24101322.