A Survey on Physiological Signal-Based Emotion Recognition

Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition...

Full description

Saved in:
Bibliographic Details
Published inBioengineering (Basel) Vol. 9; no. 11; p. 688
Main Authors Ahmad, Zeeshan, Khan, Naimul
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2022
MDPI
Subjects
Annotations
Bioengineering
challenges
Classification
Coronaviruses
COVID-19
data annotation
Data processing
data variability
Deep learning
Electrocardiography
Electroencephalography
emotion models
Emotion recognition
Emotions
Feature extraction
Literature reviews
Machine learning
Medical research
Methods
Pandemics
physiological signals
Physiology
review
Signal processing
Workflow
United States
Online AccessGet full text

Cover

Abstract Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.
AbstractList Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.
Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.
Audience Academic
Author Ahmad, Zeeshan
Khan, Naimul
AuthorAffiliation Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
AuthorAffiliation_xml – name: Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Author_xml – sequence: 1
  givenname: Zeeshan
  orcidid: 0000-0003-2950-2840
  surname: Ahmad
  fullname: Ahmad, Zeeshan
– sequence: 2
  givenname: Naimul
  orcidid: 0000-0002-8229-0747
  surname: Khan
  fullname: Khan, Naimul
BookMark eNp9km9rFDEQxoNUbK39CMKCb3yzbTbJ5o-CcJZaCwXF6uuQzU62OXaTmuwV7tub9Sp6pUgCGZLf8wwzmZfoIMQACL1u8CmlCp91PkIYfABIPgyqaTCX8hk6IhTzuqUtO_gnPkQnOa8xxg0lLeHsBTqknJEGS3WE3q2qm026h20VQ_X1dpt9HOPgrRmrGz8EM9YfTYa-upji7AvyDWwcgl_iV-i5M2OGk4fzGP34dPH9_HN9_eXy6nx1XduWqbl2gnTW0l7a3nFJSUdAWalYz0jHiFKcME6caaXFshfQKdVxaR3hWGFFTUeP0dXOt49mre-Sn0za6mi8_n0R06BNmr0dQTtBjRCOglSU4ZKgJ0Q4A0zZrqXOFK8PO6-7TTdBbyHMyYx7pvsvwd_qId5rxaUoXSsGbx8MUvy5gTzryWcL42gCxE3WRFC1bNwW9M0jdB03qbR0oZhcCmTiLzWYUoAPLpa8djHVK8FYIRWmhTp9giqrh8nbMhzOl_s9wfudwKaYcwKnrZ_N8m1F6EfdYL1Mkn5ykoq6faT-06H_634BS-7RKQ
CitedBy_id crossref_primary_10_1007_s43154_023_00107_x
crossref_primary_10_1155_2024_5581443
crossref_primary_10_1016_j_bspc_2024_106353
crossref_primary_10_1109_TIM_2024_3369130
crossref_primary_10_2196_52171
crossref_primary_10_3390_app142310788
crossref_primary_10_1080_10447318_2025_2478265
crossref_primary_10_4108_eetsis_5036
crossref_primary_10_1109_TCSS_2024_3420445
crossref_primary_10_1016_j_compbiomed_2024_108658
crossref_primary_10_1145_3678569
crossref_primary_10_1007_s11042_023_16933_2
crossref_primary_10_3389_fpsyg_2025_1455177
crossref_primary_10_1109_ACCESS_2024_3420103
crossref_primary_10_1016_j_dsm_2024_12_004
crossref_primary_10_3389_fnhum_2024_1347327
crossref_primary_10_3390_sci6010010
crossref_primary_10_3390_s23031743
crossref_primary_10_7717_peerj_cs_2024
crossref_primary_10_1007_s00530_024_01302_2
Cites_doi 10.1109/ACCESS.2019.2914872
10.15439/2016F263
10.1109/ISSC49989.2020.9180193
10.1109/TBME.2011.2112658
10.1109/EMBC44109.2020.9176590
10.1109/SSCI44817.2019.9003164
10.1109/JBHI.2017.2688239
10.1109/EMBC.2015.7318961
10.1109/ACCESS.2021.3097614
10.1111/j.1468-8986.2005.00264.x
10.1002/9781118287798
10.1016/j.measurement.2020.108747
10.1109/TAFFC.2019.2954118
10.1109/ACIIAsia.2018.8470381
10.1080/02699939208411068
10.1109/SSCI.2016.7849931
10.1109/ICRAI47710.2019.8967398
10.1109/CVPR.2016.90
10.1109/TAFFC.2015.2436926
10.1109/ACCESS.2019.2922037
10.1080/00049539908255353
10.1109/TENCON.2005.300986
10.3389/fnhum.2013.00138
10.1109/SYSOSE.2016.7542941
10.1109/ACCESS.2016.2628407
10.1109/ICDIS.2019.00028
10.1109/JIOT.2021.3058587
10.1109/ACCESS.2018.2794346
10.1109/ICACCS51430.2021.9441999
10.1098/rspa.1998.0193
10.1145/1178657.1178661
10.20944/preprints202206.0112.v1
10.1109/ACCESS.2021.3051281
10.1109/ISCID51228.2020.00025
10.1155/2020/8875426
10.1080/03772063.2020.1725663
10.1037/0022-3514.54.6.1063
10.1109/4233.966104
10.3390/s18072074
10.23919/EUSIPCO.2018.8553191
10.1109/AIMS52415.2021.9466092
10.1016/j.clinph.2006.10.019
10.1109/T-AFFC.2011.15
10.1109/JSEN.2018.2883497
10.1109/ACCESS.2019.2944001
10.1109/SMC.2016.7844928
10.1109/HSI.2013.6577877
10.3758/BF03193159
10.3390/bioengineering9010008
10.1109/T-AFFC.2011.25
10.1016/j.specom.2019.12.001
10.1109/T-AFFC.2011.20
10.1080/02699939508408966
10.1511/2001.28.344
10.1109/JBHI.2021.3091187
10.1109/ACII52823.2021.9597442
10.1109/FG47880.2020.00050
10.1109/EMBC.2019.8856895
10.1109/ACIIAsia.2018.8470373
10.1109/HI-POCT45284.2019.8962891
10.1016/j.neucom.2017.08.015
10.1037/h0077714
10.1038/s41598-019-52891-2
10.36227/techrxiv.16689484
10.1016/0005-7916(94)90063-9
10.1159/000104430
10.3758/BF03209415
10.2196/32140
10.1145/3242969.3242985
10.1109/JSEN.2020.3020828
10.1002/9781118910566.ch14
10.1109/IHMSC.2016.66
10.1109/ACCESS.2021.3055933
10.1109/TCAD.2020.3012169
10.1109/TCYB.2020.2987575
10.7551/mitpress/8022.001.0001
10.3758/s13428-017-0915-5
10.1109/EMBC.2019.8857852
10.1109/86.895946
10.1109/WIFS.2010.5711466
10.1109/EMBC44109.2020.9175928
10.1109/ICASSP40776.2020.9053985
10.1109/ICASSP39728.2021.9414709
10.1109/ACCESS.2020.3023871
10.1109/ACII.2013.21
10.1109/ISCAS45731.2020.9180909
10.1037/0022-3514.65.1.45
10.1109/ACCESS.2019.2891579
10.1109/ACII.2019.8925486
10.1145/2818740
10.3390/bioengineering8030035
10.1109/TAFFC.2017.2781732
10.1145/2070942.2070970
10.1109/TAMD.2015.2431497
10.1109/ICPICS47731.2019.8942482
10.1016/j.procs.2017.12.003
10.1145/3524499
10.1109/TAFFC.2018.2874986
10.1109/TAFFC.2016.2625250
10.1016/0092-6566(77)90037-X
10.1109/CSNDSP16145.2010.5580317
10.1109/ACCESS.2019.2904400
10.1109/ACCESS.2021.3131733
10.1109/AICAS.2019.8771622
10.1017/S0954579405050340
ContentType Journal Article
Copyright COPYRIGHT 2022 MDPI AG
2022 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
2022 by the authors. 2022
Copyright_xml – notice: COPYRIGHT 2022 MDPI AG
– notice: 2022 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
– notice: 2022 by the authors. 2022
DBID AAYXX
CITATION
8FE
8FG
8FH
ABJCF
ABUWG
AFKRA
AZQEC
BBNVY
BENPR
BGLVJ
BHPHI
CCPQU
COVID
DWQXO
GNUQQ
HCIFZ
L6V
LK8
M7P
M7S
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PTHSS
7X8
5PM
DOA
DOI 10.3390/bioengineering9110688
DatabaseName CrossRef
ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest Natural Science Collection
Materials Science & Engineering Collection
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central Essentials
Biological Science Database
ProQuest Central
Technology Collection
Natural Science Collection
ProQuest One Community College
Coronavirus Research Database
ProQuest Central Korea
ProQuest Central Student
SciTech Premium Collection
ProQuest Engineering Collection
ProQuest Biological Science Collection
Biological Science Database
Engineering Database
ProQuest Central Premium
ProQuest One Academic
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic
ProQuest One Academic UKI Edition
Engineering Collection
MEDLINE - Academic
PubMed Central (Full Participant titles)
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
Publicly Available Content Database
ProQuest Central Student
Technology Collection
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Natural Science Collection
ProQuest Central
ProQuest One Applied & Life Sciences
ProQuest Engineering Collection
Natural Science Collection
ProQuest Central Korea
Biological Science Collection
ProQuest Central (New)
Engineering Collection
Engineering Database
ProQuest Biological Science Collection
ProQuest One Academic Eastern Edition
Coronavirus Research Database
ProQuest Technology Collection
Biological Science Database
ProQuest SciTech Collection
ProQuest One Academic UKI Edition
Materials Science & Engineering Collection
ProQuest One Academic
ProQuest One Academic (New)
MEDLINE - Academic
DatabaseTitleList Publicly Available Content Database


MEDLINE - Academic

CrossRef
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: 8FG
  name: ProQuest Technology Collection
  url: https://search.proquest.com/technologycollection1
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 2306-5354
ExternalDocumentID oai_doaj_org_article_f73a77f3e8934042bd227fae49cb53fa
PMC9687364
A744274903
10_3390_bioengineering9110688
GeographicLocations United States
GeographicLocations_xml – name: United States
GrantInformation_xml – fundername: SSHRC
  grantid: NFRFR-2021-00343
GroupedDBID 53G
5VS
8FE
8FG
8FH
AAFWJ
AAYXX
ABDBF
ABJCF
ACUHS
ADBBV
AFKRA
AFPKN
ALMA_UNASSIGNED_HOLDINGS
AOIJS
BBNVY
BCNDV
BENPR
BGLVJ
BHPHI
CCPQU
CITATION
GROUPED_DOAJ
HCIFZ
HYE
IAO
IHR
INH
ITC
KQ8
L6V
LK8
M7P
M7S
MODMG
M~E
OK1
PGMZT
PHGZM
PHGZT
PIMPY
PROAC
PTHSS
RPM
PMFND
ABUWG
AZQEC
COVID
DWQXO
GNUQQ
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
7X8
5PM
PUEGO
ID FETCH-LOGICAL-c549t-f72bcc3d8cdf6832b2e9c894d42b429962462fa58c08d7eb99b68cf2609093ab3
IEDL.DBID DOA
ISSN 2306-5354
IngestDate Wed Aug 27 01:24:58 EDT 2025
Thu Aug 21 18:39:29 EDT 2025
Fri Jul 11 16:54:54 EDT 2025
Fri Jul 25 11:49:16 EDT 2025
Tue Jun 17 21:33:53 EDT 2025
Tue Jun 10 20:49:03 EDT 2025
Tue Jul 01 03:12:15 EDT 2025
Thu Apr 24 23:01:37 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 11
Language English
License 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/).
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c549t-f72bcc3d8cdf6832b2e9c894d42b429962462fa58c08d7eb99b68cf2609093ab3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ORCID 0000-0003-2950-2840
0000-0002-8229-0747
OpenAccessLink https://doaj.org/article/f73a77f3e8934042bd227fae49cb53fa
PMID 36421089
PQID 2748260947
PQPubID 2055440
ParticipantIDs doaj_primary_oai_doaj_org_article_f73a77f3e8934042bd227fae49cb53fa
pubmedcentral_primary_oai_pubmedcentral_nih_gov_9687364
proquest_miscellaneous_2739739705
proquest_journals_2748260947
gale_infotracmisc_A744274903
gale_infotracacademiconefile_A744274903
crossref_citationtrail_10_3390_bioengineering9110688
crossref_primary_10_3390_bioengineering9110688
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2022-11-01
PublicationDateYYYYMMDD 2022-11-01
PublicationDate_xml – month: 11
  year: 2022
  text: 2022-11-01
  day: 01
PublicationDecade 2020
PublicationPlace Basel
PublicationPlace_xml – name: Basel
PublicationTitle Bioengineering (Basel)
PublicationYear 2022
Publisher MDPI AG
MDPI
Publisher_xml – name: MDPI AG
– name: MDPI
References ref_93
ref_92
Yang (ref_12) 2018; 125
Cordeiro (ref_77) 2020; 39
ref_90
Siddharth (ref_10) 2019; 9
ref_14
ref_99
ref_97
ref_96
Xing (ref_114) 2019; 7
ref_95
ref_18
ref_17
Chao (ref_106) 2020; 21
Zhang (ref_13) 2016; 4
Wei (ref_80) 2001; 5
Watson (ref_50) 1988; 54
Li (ref_20) 2021; 172
Posner (ref_57) 2005; 17
Muthukumaraswamy (ref_68) 2013; 7
Nagel (ref_60) 2007; 39
Ekman (ref_38) 1992; 6
ref_25
ref_24
ref_23
Cimtay (ref_110) 2020; 8
Romeo (ref_58) 2019; 13
ref_29
ref_28
Croft (ref_72) 2005; 42
ref_27
ref_26
Russell (ref_55) 1977; 11
Gratton (ref_70) 1998; 30
Venkatesan (ref_74) 2018; 6
Alcaraz (ref_75) 2011; 58
ref_79
ref_78
ref_73
Ullah (ref_102) 2019; 7
Abdulhay (ref_98) 2018; 7
Khateeb (ref_112) 2021; 9
ref_82
ref_81
Noroozi (ref_16) 2018; 12
Zheng (ref_48) 2015; 7
ref_89
Fatourechi (ref_69) 2007; 118
ref_87
ref_86
ref_85
ref_84
Suhaimi (ref_21) 2020; 2020
Soleymani (ref_62) 2015; 7
Katsigiannis (ref_45) 2017; 22
Song (ref_47) 2019; 7
Bradley (ref_51) 1994; 25
ref_56
Schubert (ref_61) 1999; 51
ref_53
Ramoser (ref_71) 2000; 8
Ahmad (ref_88) 2021; 9
Soleymani (ref_46) 2011; 3
Wang (ref_11) 2018; 272
Fredrickson (ref_59) 1993; 65
Koelstra (ref_44) 2011; 3
Gross (ref_52) 1995; 9
ref_67
ref_65
ref_63
Giordano (ref_2) 2007; 106
Piana (ref_15) 2016; 6
McKeown (ref_64) 2011; 3
Bota (ref_30) 2019; 7
ref_115
Schluter (ref_3) 2022; 8
ref_36
ref_35
ref_34
Hsu (ref_22) 2017; 11
ref_32
Plutchik (ref_39) 2001; 89
Subramanian (ref_42) 2016; 9
Zhang (ref_101) 2020; 51
ref_111
Saganowski (ref_33) 2022; 1
Huang (ref_83) 1998; 454
ref_113
Muhammad (ref_9) 2021; 8
Gupta (ref_91) 2018; 19
ref_37
Abadi (ref_41) 2018; 12
Russell (ref_40) 1980; 39
Girard (ref_66) 2018; 50
ref_104
ref_103
Dalvi (ref_31) 2021; 9
ref_105
ref_108
ref_107
ref_109
Patro (ref_76) 2022; 68
ref_43
ref_100
(ref_19) 2020; 116
Hssayeni (ref_116) 2021; 9
Althobaiti (ref_54) 2019; 7
ref_49
Phillips (ref_1) 2002; 57
ref_8
Kim (ref_94) 2021; 26
ref_5
ref_4
ref_7
ref_6
References_xml – volume: 7
  start-page: 59844
  year: 2019
  ident: ref_114
  article-title: Exploiting EEG signals and audiovisual feature fusion for video emotion recognition
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2019.2914872
– ident: ref_6
  doi: 10.15439/2016F263
– ident: ref_105
  doi: 10.1109/ISSC49989.2020.9180193
– volume: 58
  start-page: 1441
  year: 2011
  ident: ref_75
  article-title: Classification of paroxysmal and persistent atrial fibrillation in ambulatory ECG recordings
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2011.2112658
– ident: ref_96
  doi: 10.1109/EMBC44109.2020.9176590
– ident: ref_92
  doi: 10.1109/SSCI44817.2019.9003164
– ident: ref_100
– volume: 22
  start-page: 98
  year: 2017
  ident: ref_45
  article-title: DREAMER: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices
  publication-title: IEEE J. Biomed. Health Inform.
  doi: 10.1109/JBHI.2017.2688239
– ident: ref_86
  doi: 10.1109/EMBC.2015.7318961
– volume: 9
  start-page: 100615
  year: 2021
  ident: ref_88
  article-title: ECG heartbeat classification using multimodal fusion
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3097614
– ident: ref_65
– volume: 42
  start-page: 16
  year: 2005
  ident: ref_72
  article-title: EOG correction: A comparison of four methods
  publication-title: Psychophysiology
  doi: 10.1111/j.1468-8986.2005.00264.x
– ident: ref_85
  doi: 10.1002/9781118287798
– volume: 172
  start-page: 108747
  year: 2021
  ident: ref_20
  article-title: Physiological-signal-based emotion recognition: An odyssey from methodology to philosophy
  publication-title: Measurement
  doi: 10.1016/j.measurement.2020.108747
– volume: 13
  start-page: 389
  year: 2019
  ident: ref_58
  article-title: Multiple instance learning for emotion recognition using physiological signals
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/TAFFC.2019.2954118
– ident: ref_111
  doi: 10.1109/ACIIAsia.2018.8470381
– volume: 6
  start-page: 169
  year: 1992
  ident: ref_38
  article-title: An argument for basic emotions
  publication-title: Cogn. Emot.
  doi: 10.1080/02699939208411068
– ident: ref_43
  doi: 10.1109/SSCI.2016.7849931
– ident: ref_89
  doi: 10.1109/ICRAI47710.2019.8967398
– ident: ref_97
  doi: 10.1109/CVPR.2016.90
– volume: 7
  start-page: 17
  year: 2015
  ident: ref_62
  article-title: Analysis of EEG signals and facial expressions for continuous emotion detection
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/TAFFC.2015.2436926
– volume: 7
  start-page: 77857
  year: 2019
  ident: ref_54
  article-title: Examining human-horse interaction by means of affect recognition via physiological signals
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2019.2922037
– volume: 51
  start-page: 154
  year: 1999
  ident: ref_61
  article-title: Measuring emotion continuously: Validity and reliability of the two-dimensional emotion-space
  publication-title: Aust. J. Psychol.
  doi: 10.1080/00049539908255353
– ident: ref_79
  doi: 10.1109/TENCON.2005.300986
– volume: 7
  start-page: 138
  year: 2013
  ident: ref_68
  article-title: High-frequency brain activity and muscle artifacts in MEG/EEG: A review and recommendations
  publication-title: Front. Hum. Neurosci.
  doi: 10.3389/fnhum.2013.00138
– ident: ref_4
  doi: 10.1109/SYSOSE.2016.7542941
– volume: 4
  start-page: 8375
  year: 2016
  ident: ref_13
  article-title: Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2016.2628407
– ident: ref_56
  doi: 10.1109/ICDIS.2019.00028
– volume: 8
  start-page: 16894
  year: 2021
  ident: ref_9
  article-title: Emotion recognition for cognitive edge computing using deep learning
  publication-title: IEEE Internet Things J.
  doi: 10.1109/JIOT.2021.3058587
– ident: ref_53
– volume: 6
  start-page: 9767
  year: 2018
  ident: ref_74
  article-title: ECG signal pre-processing and SVM classifier-based abnormality detection in remote healthcare applications
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2018.2794346
– ident: ref_28
  doi: 10.1109/ICACCS51430.2021.9441999
– volume: 454
  start-page: 903
  year: 1998
  ident: ref_83
  article-title: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
  publication-title: Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci.
  doi: 10.1098/rspa.1998.0193
– ident: ref_67
  doi: 10.1145/1178657.1178661
– ident: ref_35
  doi: 10.20944/preprints202206.0112.v1
– volume: 9
  start-page: 12134
  year: 2021
  ident: ref_112
  article-title: Multi-domain feature fusion for emotion classification using DEAP dataset
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3051281
– ident: ref_29
  doi: 10.1109/ISCID51228.2020.00025
– volume: 2020
  start-page: 8875426
  year: 2020
  ident: ref_21
  article-title: EEG-based emotion recognition: A state-of-the-art review of current trends and opportunities
  publication-title: Comput. Intell. Neurosci.
  doi: 10.1155/2020/8875426
– volume: 68
  start-page: 2743
  year: 2022
  ident: ref_76
  article-title: An efficient optimized feature selection with machine learning approach for ECG biometric recognition
  publication-title: IETE J. Res.
  doi: 10.1080/03772063.2020.1725663
– volume: 54
  start-page: 1063
  year: 1988
  ident: ref_50
  article-title: Development and validation of brief measures of positive and negative affect: The PANAS scales
  publication-title: J. Personal. Soc. Psychol.
  doi: 10.1037/0022-3514.54.6.1063
– volume: 5
  start-page: 290
  year: 2001
  ident: ref_80
  article-title: ECG data compression using truncated singular value decomposition
  publication-title: IEEE Trans. Inf. Technol. Biomed.
  doi: 10.1109/4233.966104
– ident: ref_26
  doi: 10.3390/s18072074
– ident: ref_84
  doi: 10.23919/EUSIPCO.2018.8553191
– ident: ref_27
  doi: 10.1109/AIMS52415.2021.9466092
– volume: 118
  start-page: 480
  year: 2007
  ident: ref_69
  article-title: EMG and EOG artifacts in brain computer interface systems: A survey
  publication-title: Clin. Neurophysiol.
  doi: 10.1016/j.clinph.2006.10.019
– volume: 3
  start-page: 18
  year: 2011
  ident: ref_44
  article-title: Deap: A database for emotion analysis; using physiological signals
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/T-AFFC.2011.15
– volume: 19
  start-page: 2266
  year: 2018
  ident: ref_91
  article-title: Cross-subject emotion recognition using flexible analytic wavelet transform from EEG signals
  publication-title: IEEE Sens. J.
  doi: 10.1109/JSEN.2018.2883497
– volume: 7
  start-page: 140990
  year: 2019
  ident: ref_30
  article-title: A review, current challenges, and future possibilities on emotion recognition using machine learning and physiological signals
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2019.2944001
– ident: ref_73
  doi: 10.1109/SMC.2016.7844928
– ident: ref_7
  doi: 10.1109/HSI.2013.6577877
– volume: 39
  start-page: 283
  year: 2007
  ident: ref_60
  article-title: EMuJoy: Software for continuous measurement of perceived emotions in music
  publication-title: Behav. Res. Methods
  doi: 10.3758/BF03193159
– ident: ref_36
  doi: 10.3390/bioengineering9010008
– volume: 3
  start-page: 42
  year: 2011
  ident: ref_46
  article-title: A multimodal database for affect recognition and implicit tagging
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/T-AFFC.2011.25
– volume: 116
  start-page: 56
  year: 2020
  ident: ref_19
  article-title: Speech emotion recognition: Emotional models, databases, features, pre-processing methods, supporting modalities, and classifiers
  publication-title: Speech Commun.
  doi: 10.1016/j.specom.2019.12.001
– volume: 3
  start-page: 5
  year: 2011
  ident: ref_64
  article-title: The semaine database: Annotated multimodal records of emotionally colored conversations between a person and a limited agent
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/T-AFFC.2011.20
– volume: 9
  start-page: 87
  year: 1995
  ident: ref_52
  article-title: Emotion elicitation using films
  publication-title: Cogn. Emot.
  doi: 10.1080/02699939508408966
– ident: ref_78
– volume: 89
  start-page: 344
  year: 2001
  ident: ref_39
  article-title: The nature of emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice
  publication-title: Am. Sci.
  doi: 10.1511/2001.28.344
– ident: ref_49
– ident: ref_32
– volume: 12
  start-page: 479
  year: 2018
  ident: ref_41
  article-title: Amigos: A dataset for affect, personality and mood research on individuals and groups
  publication-title: IEEE Trans. Affect. Comput.
– volume: 26
  start-page: 264
  year: 2021
  ident: ref_94
  article-title: Wedea: A new eeg-based framework for emotion recognition
  publication-title: IEEE J. Biomed. Health Inform.
  doi: 10.1109/JBHI.2021.3091187
– ident: ref_90
  doi: 10.1109/ACII52823.2021.9597442
– ident: ref_109
  doi: 10.1109/FG47880.2020.00050
– ident: ref_115
  doi: 10.1109/EMBC.2019.8856895
– ident: ref_107
  doi: 10.1109/ACIIAsia.2018.8470373
– volume: 57
  start-page: P526
  year: 2002
  ident: ref_1
  article-title: Age and the understanding of emotions: Neuropsychological and sociocognitive perspectives
  publication-title: J. Gerontol. Ser. B Psychol. Sci. Soc. Sci.
– ident: ref_93
  doi: 10.1109/HI-POCT45284.2019.8962891
– volume: 272
  start-page: 668
  year: 2018
  ident: ref_11
  article-title: Intelligent facial emotion recognition based on stationary wavelet entropy and Jaya algorithm
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2017.08.015
– volume: 39
  start-page: 1161
  year: 1980
  ident: ref_40
  article-title: A circumplex model of affect
  publication-title: J. Personal. Soc. Psychol.
  doi: 10.1037/h0077714
– volume: 9
  start-page: 16295
  year: 2019
  ident: ref_10
  article-title: Impact of affective multimedia content on the electroencephalogram and facial expressions
  publication-title: Sci. Rep.
  doi: 10.1038/s41598-019-52891-2
– ident: ref_18
  doi: 10.36227/techrxiv.16689484
– volume: 25
  start-page: 49
  year: 1994
  ident: ref_51
  article-title: Measuring emotion: The self-assessment manikin and the semantic differential
  publication-title: J. Behav. Ther. Exp. Psychiatry
  doi: 10.1016/0005-7916(94)90063-9
– volume: 106
  start-page: c187
  year: 2007
  ident: ref_2
  article-title: Screening of Depressive Symptoms in Young–Old Hemodialysis Patients: Relationship between Beck Depression Inventory and 15-Item Geriatric Depression Scale
  publication-title: Nephron Clin. Pract.
  doi: 10.1159/000104430
– ident: ref_8
– volume: 30
  start-page: 44
  year: 1998
  ident: ref_70
  article-title: Dealing with artifacts: The EOG contamination of the event-related brain potential
  publication-title: Behav. Res. Methods Instrum. Comput.
  doi: 10.3758/BF03209415
– volume: 8
  start-page: e32140
  year: 2022
  ident: ref_3
  article-title: Patterns of suicide ideation across eight countries in four continents during the COVID-19 pandemic era: Repeated cross-sectional study
  publication-title: JMIR Public Health Surveill.
  doi: 10.2196/32140
– ident: ref_95
  doi: 10.1145/3242969.3242985
– volume: 21
  start-page: 2024
  year: 2020
  ident: ref_106
  article-title: Emotion recognition using three-dimensional feature and convolutional neural network from multichannel EEG signals
  publication-title: IEEE Sens. J.
  doi: 10.1109/JSEN.2020.3020828
– ident: ref_14
  doi: 10.1002/9781118910566.ch14
– ident: ref_24
  doi: 10.1109/IHMSC.2016.66
– volume: 9
  start-page: 21642
  year: 2021
  ident: ref_116
  article-title: Multi-modal physiological data fusion for affect estimation using deep learning
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3055933
– volume: 39
  start-page: 3373
  year: 2020
  ident: ref_77
  article-title: Ecg-based authentication using timing-aware domain-specific architecture
  publication-title: IEEE Trans.-Comput.-Aided Des. Integr. Syst.
  doi: 10.1109/TCAD.2020.3012169
– volume: 51
  start-page: 4386
  year: 2020
  ident: ref_101
  article-title: Emotion recognition from multimodal physiological signals using a regularized deep fusion of kernel machine
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2020.2987575
– ident: ref_25
  doi: 10.7551/mitpress/8022.001.0001
– volume: 50
  start-page: 902
  year: 2018
  ident: ref_66
  article-title: DARMA: Software for dual axis rating and media annotation
  publication-title: Behav. Res. Methods
  doi: 10.3758/s13428-017-0915-5
– ident: ref_113
  doi: 10.1109/EMBC.2019.8857852
– volume: 8
  start-page: 441
  year: 2000
  ident: ref_71
  article-title: Optimal spatial filtering of single trial EEG during imagined hand movement
  publication-title: IEEE Trans. Rehabil. Eng.
  doi: 10.1109/86.895946
– ident: ref_82
  doi: 10.1109/WIFS.2010.5711466
– ident: ref_99
  doi: 10.1109/EMBC44109.2020.9175928
– ident: ref_23
  doi: 10.1109/ICASSP40776.2020.9053985
– ident: ref_87
  doi: 10.1109/ICASSP39728.2021.9414709
– volume: 8
  start-page: 168865
  year: 2020
  ident: ref_110
  article-title: Cross-subject multimodal emotion recognition based on hybrid fusion
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2020.3023871
– ident: ref_63
  doi: 10.1109/ACII.2013.21
– ident: ref_104
  doi: 10.1109/ISCAS45731.2020.9180909
– volume: 65
  start-page: 45
  year: 1993
  ident: ref_59
  article-title: Duration neglect in retrospective evaluations of affective episodes
  publication-title: J. Personal. Soc. Psychol.
  doi: 10.1037/0022-3514.65.1.45
– volume: 7
  start-page: 12177
  year: 2019
  ident: ref_47
  article-title: MPED: A multi-modal physiological emotion database for discrete emotion recognition
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2019.2891579
– ident: ref_108
  doi: 10.1109/ACII.2019.8925486
– volume: 6
  start-page: 1
  year: 2016
  ident: ref_15
  article-title: Adaptive body gesture representation for automatic emotion recognition
  publication-title: ACM Trans. Interact. Intell. Syst. TiiS
  doi: 10.1145/2818740
– ident: ref_37
  doi: 10.3390/bioengineering8030035
– volume: 11
  start-page: 85
  year: 2017
  ident: ref_22
  article-title: Automatic ECG-based emotion recognition in music listening
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/TAFFC.2017.2781732
– ident: ref_5
  doi: 10.1145/2070942.2070970
– volume: 7
  start-page: 162
  year: 2015
  ident: ref_48
  article-title: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks
  publication-title: IEEE Trans. Auton. Ment. Dev.
  doi: 10.1109/TAMD.2015.2431497
– ident: ref_17
  doi: 10.1109/ICPICS47731.2019.8942482
– volume: 125
  start-page: 2
  year: 2018
  ident: ref_12
  article-title: An emotion recognition model based on facial recognition in virtual learning environment
  publication-title: Procedia Comput. Sci.
  doi: 10.1016/j.procs.2017.12.003
– ident: ref_34
  doi: 10.1145/3524499
– volume: 12
  start-page: 505
  year: 2018
  ident: ref_16
  article-title: Survey on emotional body gesture recognition
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/TAFFC.2018.2874986
– volume: 9
  start-page: 147
  year: 2016
  ident: ref_42
  article-title: ASCERTAIN: Emotion and personality recognition using commercial sensors
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/TAFFC.2016.2625250
– volume: 11
  start-page: 273
  year: 1977
  ident: ref_55
  article-title: Evidence for a three-factor theory of emotions
  publication-title: J. Res. Personal.
  doi: 10.1016/0092-6566(77)90037-X
– ident: ref_81
  doi: 10.1109/CSNDSP16145.2010.5580317
– volume: 1
  start-page: 1
  year: 2022
  ident: ref_33
  article-title: Emotion Recognition for Everyday Life Using Physiological Signals from Wearables: A Systematic Literature Review
  publication-title: IEEE Trans. Affect. Comput.
– volume: 7
  start-page: 40144
  year: 2019
  ident: ref_102
  article-title: Internal emotion classification using EEG signal with sparse discriminative ensemble
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2019.2904400
– volume: 9
  start-page: 165806
  year: 2021
  ident: ref_31
  article-title: A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2021.3131733
– ident: ref_103
  doi: 10.1109/AICAS.2019.8771622
– volume: 17
  start-page: 715
  year: 2005
  ident: ref_57
  article-title: The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology
  publication-title: Dev. Psychopathol.
  doi: 10.1017/S0954579405050340
– volume: 7
  start-page: 57
  year: 2018
  ident: ref_98
  article-title: Using deep convolutional neural network for emotion detection on a physiological signals dataset (AMIGOS)
  publication-title: IEEE Access
SSID ssj0001325264
Score 2.345678
Snippet Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review...
SourceID doaj
pubmedcentral
proquest
gale
crossref
SourceType Open Website
Open Access Repository
Aggregation Database
Enrichment Source
Index Database
StartPage 688
SubjectTerms Annotations
Bioengineering
challenges
Classification
Coronaviruses
COVID-19
data annotation
Data processing
data variability
Deep learning
Electrocardiography
Electroencephalography
emotion models
Emotion recognition
Emotions
Feature extraction
Literature reviews
Machine learning
Medical research
Methods
Pandemics
physiological signals
Physiology
review
Signal processing
Workflow
SummonAdditionalLinks – databaseName: ProQuest Central
  dbid: BENPR
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwhV3di9QwEB9070UfxE-snlJB8CleTdKk8UV2ZY9D8JA7D-6tNF93C9Kee7uC_70z3XT3qqLQlzYZ2ibzmUzmB_C6IaA3tMOsdNEyGX3FrDPvWBNLAkBSxgTa0f18rI7O5Kfz8jwtuF2ntMpBJ_aK2neO1sgPMHpCTxiDEf3h6jsj1CjaXU0QGrdhD1VwVU1gbzY__nKyW2URvESTvzm6IzC-P7CLLuwq_aGoJ9SVnVHqa_f_qaF_z5q8YYYO78O95D_m082EP4BboX0Id29UFXwE76f56Xr5I_zMuzbvMzwHBZefLi6Qms3QdPl8vkHwyU-GHKKufQxnh_OvH49YgkhgDgO7FYuaW-cEQRBFhcJpeTCuMtJLbsnSKC4Vj01ZuaLyOlhjrKpcpIEsjGiseAKTtmvDU8hVLAz3sZReU5CiGm_wXtsYeYyx9BnIYYxql-qHE4zFtxrjCBra-q9Dm8HbLdnVpoDG_whmNAHbzlT_un_QLS_qJE511KLROoqA7pZEvWM95zo2QRpnSxGbDN7Q9NUkpfiRrkmHDfBXqd5VPdVSIkeZQmSwP-qJ0uXGzQMD1Em6r-sdL2bwattMlJSx1oZuTX3Q08OrKDPQI8YZ_dm4pV1c9hW-jaq0UPLZv1_-HO5wOozRn4zch8lquQ4v0EVa2ZdJDn4B4EAVeQ
  priority: 102
  providerName: ProQuest
Title A Survey on Physiological Signal-Based Emotion Recognition
URI https://www.proquest.com/docview/2748260947
https://www.proquest.com/docview/2739739705
https://pubmed.ncbi.nlm.nih.gov/PMC9687364
https://doaj.org/article/f73a77f3e8934042bd227fae49cb53fa
Volume 9
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3da9RAEB-0vuhDqV8YrUcEwafYuJ_Zvl30ziJYpLXQtyW72a0Hkkh7J_S_70ySu15U6IsQCMnswO7szM5OMvsbgLcVFXpDP5xJH10mYl1kzpsPWRUlFUBSxgT6o_v1WB2diS_n8nyr1BflhPXwwL3gDqLmldaRB3SsAjXM1YzpWAVhvJM8dlsj9HlbwVT3dYUzia6-P7LDMa4_cIs23CL8oYkP1VZunVGH2f_3yvxntuSW-5nvwe6wb0ynfX8fw73QPIFHW2iCT-Fwmp6uLn-H67Rt0i6zc72wpaeLC-TOSnRZdTrrK_ekJ-vcobZ5Bmfz2fePR9lQGiHzGNAts6iZ855T6aGo0CgdC8YXRtQoIPIwignFYiULnxe1Ds4YpwofMXgxueGV489hp2mb8AJSFXPD6ihFrSk4UVVt8Fm7GFmMUdYJiLWMrB9ww6l8xU-L8QOJ1v5TtAm837D96oEz7mIoaQI2jQn3unuB2mAHbbB3aUMC72j6LFkndtJXwyEDHCrhXNmpFgLjcJPzBPZHLdGq_Ji8VgA7WPWVRVJBIhQ6gTcbMnFSploT2hW1wR0eXrlMQI8UZzSyMaVZ_OiQvY0qNFfi5f8QxSt4yOioRnduch92lper8Bo3UEs3gfvF_PMEHkzLT-Uc7-Xs-NvJpLOgG_P0IP0
linkProvider Directory of Open Access Journals
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VcgAOiKcIFAgSiFNocBw7RkJoC1229HGgrdSbG7_KSigp211Q_xS_kZk8druA4FQpl8R2nNjjedgz8wE8LwnoDeVwkttgEh5ckRirXidlyAkASSjl6UR3d0-MDvmno_xoBX72sTDkVtnzxIZRu9rSHvk6Wk-oCaMxIt-dfksINYpOV3sIjZYstv35DzTZzt5ufcD5fcHYcPPg_SjpUAUSi7bQNAmSGWszQu0JAunZMK9sobjjzBBzFowLFsq8sGnhpDdKGVHYQH2j9V-aDN97Ba7yDCU5RaYPPy72dDKWo4LRBgphebpuxrVf5BVExtJhvCxEYIMU8Kc8-N1H84LQG96Cm522Gg9a8roNK766Azcu5DC8C28G8f5s8t2fx3UVN_6kPTuN98cn2DrZQEHp4s0WLyj-3Hss1dU9OLyUobsPq1Vd-QcQi5Aq5kLOnSSTSJRO4b00IbAQQu4i4P0YadtlKyfQjK8arRYaWv3XoY3g1bzZaZuu438NNmgC5pUp23bzoJ6c6G7x6iCzUsqQeVTuOHI54xiTofRcWZNnoYzgJU2fJp6AH2nLLrQBf5Wya-mB5BzpV6VZBGtLNXEt2-XingB0x0vO9ILyI3g2L6aW5B9X-XpGdZAa8UrzCOQS4Sz92XJJNf7S5BNXopCZ4A__3flTuDY62N3RO1t724_gOqMwkCYmcw1Wp5OZf4zK2dQ8aVZEDMeXvQR_AfN-UQk
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwED-NTkLwgPgUgQFBAvEUGmwnjpEQalmrjUE1bUzam4kde1RCyeha0P41_jru0qRdAMHTpLwktvNh36dzdz-AZzkBvaEejhLrTSR8kUXGqldR7hMCQEqVcvRH9-Mk3TkS74-T4w342ebCUFhlKxNrQV1UlvbI--g9oSWMzojs-yYsYn97_Pb0W0QIUvSntYXTWJLInjv_ge7b2ZvdbVzr54yNR5_e7UQNwkBk0S-aR14yYy0nBB-fIm0b5pTNlCgEMySoUyZS5vMks3FWSGeUMmlmPb1HrHhuON73CmxK8op6sDkcTfYP1js8nCVobizThjhXcd9MK7euMohipkF8WSvEGjfgT-3we8TmBRU4vgk3Gts1HCyJ7RZsuPI2XL9Q0fAOvB6Eh4vZd3ceVmVYR5e2wjU8nJ7g6GiIarMIR0v0oPCgjV-qyrtwdCmTdw96ZVW6-xCmPlas8IkoJDlIaV4oPJfGe-a9T4oARDtH2ja1ywlC46tGH4amVv91agN4uRp2uize8b8BQ1qAVWeqvV1fqGYnumFl7SXPpfTcoaknUOaZgjHpcyeUNQn3eQAvaPk0SQh8SZs3iQ74qVRrSw-kEEjNKuYBbHV6ImfbbnNLALqRLGd6zQcBPF0100iKlitdtaA-aGXiEScByA7hdL6s21JOv9TVxVWaSZ6KB_9--BO4iuynP-xO9h7CNUY5IXWC5hb05rOFe4SW2tw8blgihM-XzYW_APRUVps
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Survey+on+Physiological+Signal-Based+Emotion+Recognition&rft.jtitle=Bioengineering+%28Basel%29&rft.au=Ahmad%2C+Zeeshan&rft.au=Khan%2C+Naimul&rft.date=2022-11-01&rft.pub=MDPI&rft.eissn=2306-5354&rft.volume=9&rft.issue=11&rft_id=info:doi/10.3390%2Fbioengineering9110688&rft_id=info%3Apmid%2F36421089&rft.externalDocID=PMC9687364
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2306-5354&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2306-5354&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2306-5354&client=summon