Multimodal Emotion Recognition Based on EEG and EOG Signals Evoked by the Video-Odor Stimuli

Affective data is the basis of emotion recognition, which is mainly acquired through extrinsic elicitation. To investigate the enhancing effects of multi-sensory stimuli on emotion elicitation and emotion recognition, we designed an experimental paradigm involving visual, auditory, and olfactory sen...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 3496 - 3505
Main Authors Wu, Minchao, Teng, Wei, Fan, Cunhang, Pei, Shengbing, Li, Ping, Pei, Guanxiong, Li, Taihao, Liang, Wen, Lv, Zhao
Format Journal Article
LanguageEnglish
Published United States IEEE 2024
Subjects
Adult
Algorithms
Electrodes
Electroencephalogram (EEG)
Electroencephalography
Electroencephalography - methods
electrooculogram (EOG)
Electrooculography
Electrooculography - methods
Emotion recognition
Emotions - physiology
Feature extraction
Female
Healthy Volunteers
Humans
Male
multi-modal fusion
Odorants
Photic Stimulation
Physiology
Reproducibility of Results
video-odor stimuli
Videos
Young Adult
Online AccessGet full text
ISSN1534-4320
1558-0210
1558-0210
DOI10.1109/TNSRE.2024.3457580

Cover

Abstract Affective data is the basis of emotion recognition, which is mainly acquired through extrinsic elicitation. To investigate the enhancing effects of multi-sensory stimuli on emotion elicitation and emotion recognition, we designed an experimental paradigm involving visual, auditory, and olfactory senses. A multimodal emotional dataset (OVPD-II) that employed the video-only or video-odor patterns as the stimuli materials, and recorded the electroencephalogram (EEG) and electrooculogram (EOG) signals, was created. The feedback results reported by subjects after each trial demonstrated that the video-odor pattern outperformed the video-only pattern in evoking individuals' emotions. To further validate the efficiency of the video-odor pattern, the transformer was employed to perform the emotion recognition task, where the highest accuracy reached 86.65% (66.12%) for EEG (EOG) modality with the video-odor pattern, which improved by 1.42% (3.43%) compared with the video-only pattern. What's more, the hybrid fusion (HF) method combined with the transformer and joint training was developed to improve the performance of the emotion recognition task, which achieved classify accuracies of 89.50% and 88.47% for the video-odor and video-only patterns, respectively.
AbstractList Affective data is the basis of emotion recognition, which is mainly acquired through extrinsic elicitation. To investigate the enhancing effects of multi-sensory stimuli on emotion elicitation and emotion recognition, we designed an experimental paradigm involving visual, auditory, and olfactory senses. A multimodal emotional dataset (OVPD-II) that employed the video-only or video-odor patterns as the stimuli materials, and recorded the electroencephalogram (EEG) and electrooculogram (EOG) signals, was created. The feedback results reported by subjects after each trial demonstrated that the video-odor pattern outperformed the video-only pattern in evoking individuals' emotions. To further validate the efficiency of the video-odor pattern, the transformer was employed to perform the emotion recognition task, where the highest accuracy reached 86.65% (66.12%) for EEG (EOG) modality with the video-odor pattern, which improved by 1.42% (3.43%) compared with the video-only pattern. What's more, the hybrid fusion (HF) method combined with the transformer and joint training was developed to improve the performance of the emotion recognition task, which achieved classify accuracies of 89.50% and 88.47% for the video-odor and video-only patterns, respectively.
Affective data is the basis of emotion recognition, which is mainly acquired through extrinsic elicitation. To investigate the enhancing effects of multi-sensory stimuli on emotion elicitation and emotion recognition, we designed an experimental paradigm involving visual, auditory, and olfactory senses. A multimodal emotional dataset (OVPD-II) that employed the video-only or video-odor patterns as the stimuli materials, and recorded the electroencephalogram (EEG) and electrooculogram (EOG) signals, was created. The feedback results reported by subjects after each trial demonstrated that the video-odor pattern outperformed the video-only pattern in evoking individuals' emotions. To further validate the efficiency of the video-odor pattern, the transformer was employed to perform the emotion recognition task, where the highest accuracy reached 86.65% (66.12%) for EEG (EOG) modality with the video-odor pattern, which improved by 1.42% (3.43%) compared with the video-only pattern. What's more, the hybrid fusion (HF) method combined with the transformer and joint training was developed to improve the performance of the emotion recognition task, which achieved classify accuracies of 89.50% and 88.47% for the video-odor and video-only patterns, respectively.Affective data is the basis of emotion recognition, which is mainly acquired through extrinsic elicitation. To investigate the enhancing effects of multi-sensory stimuli on emotion elicitation and emotion recognition, we designed an experimental paradigm involving visual, auditory, and olfactory senses. A multimodal emotional dataset (OVPD-II) that employed the video-only or video-odor patterns as the stimuli materials, and recorded the electroencephalogram (EEG) and electrooculogram (EOG) signals, was created. The feedback results reported by subjects after each trial demonstrated that the video-odor pattern outperformed the video-only pattern in evoking individuals' emotions. To further validate the efficiency of the video-odor pattern, the transformer was employed to perform the emotion recognition task, where the highest accuracy reached 86.65% (66.12%) for EEG (EOG) modality with the video-odor pattern, which improved by 1.42% (3.43%) compared with the video-only pattern. What's more, the hybrid fusion (HF) method combined with the transformer and joint training was developed to improve the performance of the emotion recognition task, which achieved classify accuracies of 89.50% and 88.47% for the video-odor and video-only patterns, respectively.
Author Lv, Zhao
Li, Taihao
Liang, Wen
Li, Ping
Wu, Minchao
Pei, Guanxiong
Pei, Shengbing
Teng, Wei
Fan, Cunhang
Author_xml – sequence: 1
  givenname: Minchao
  orcidid: 0000-0002-8103-4709
  surname: Wu
  fullname: Wu, Minchao
  email: wu_min_chao@hotmail.com
  organization: Anhui Province Key Laboratory of Multimodal Cognitive Computation and the School of Computer Science and Technology, Anhui University, Hefei, China
– sequence: 2
  givenname: Wei
  orcidid: 0000-0003-0057-0807
  surname: Teng
  fullname: Teng, Wei
  email: tengwei1998@outlook.com
  organization: Anhui Province Key Laboratory of Multimodal Cognitive Computation and the School of Computer Science and Technology, Anhui University, Hefei, China
– sequence: 3
  givenname: Cunhang
  orcidid: 0000-0001-6318-8803
  surname: Fan
  fullname: Fan, Cunhang
  email: cunhang.fan@ahu.edu.cn
  organization: Anhui Province Key Laboratory of Multimodal Cognitive Computation and the School of Computer Science and Technology, Anhui University, Hefei, China
– sequence: 4
  givenname: Shengbing
  orcidid: 0000-0002-7629-7459
  surname: Pei
  fullname: Pei, Shengbing
  email: shengbingpei@ahu.edu.cn
  organization: Anhui Province Key Laboratory of Multimodal Cognitive Computation and the School of Computer Science and Technology, Anhui University, Hefei, China
– sequence: 5
  givenname: Ping
  orcidid: 0000-0003-1343-8768
  surname: Li
  fullname: Li, Ping
  email: pingli0112@gmail.com
  organization: Anhui Province Key Laboratory of Multimodal Cognitive Computation and the School of Computer Science and Technology, Anhui University, Hefei, China
– sequence: 6
  givenname: Guanxiong
  orcidid: 0000-0001-8064-9341
  surname: Pei
  fullname: Pei, Guanxiong
  email: pgx@zhejianglab.com
  organization: Zhejiang Laboratory, Institute of Artificial Intelligence, Hangzhou, China
– sequence: 7
  givenname: Taihao
  surname: Li
  fullname: Li, Taihao
  email: lith@zhejjianglab.com
  organization: Zhejiang Laboratory, Institute of Artificial Intelligence, Hangzhou, China
– sequence: 8
  givenname: Wen
  orcidid: 0009-0006-5646-2214
  surname: Liang
  fullname: Liang, Wen
  organization: Google Inc., Mountain View, CA, USA
– sequence: 9
  givenname: Zhao
  orcidid: 0000-0001-9727-366X
  surname: Lv
  fullname: Lv, Zhao
  email: kjlz@ahu.edu.cn
  organization: Zhejiang Laboratory, Institute of Artificial Intelligence, Hangzhou, China
BackLink https://www.ncbi.nlm.nih.gov/pubmed/39255190$$D View this record in MEDLINE/PubMed
BookMark eNplkVtv1DAQhS3Uil7gDyCE_MhLtr4mziNUYVupdKVu4QnJsp3J4pLE21xA--_rbJYKlacZ2-d8Hp05Q0dtaAGhd5QsKCX5xf3t-q5YMMLEgguZSUVeoVMqpUoIo-Ro6rlIBGfkBJ31_QMhNEtl9hqd8JxJSXNyin58HevBN6E0NS6aMPjQ4jtwYdP6ff_Z9FDi2BTFEpu2xMVqidd-05q6x8Xv8Cu-2h0efgL-7ksIyaoMHV5H5Fj7N-i4ijp4e6jn6NuX4v7yKrlZLa8vP90kjis5JHFMRw23KbdM5IqlWZY6TrlMgVSEOEZFqqyZTipVNBOUMyuJTa3MVaz8HF3P3DKYB73tfGO6nQ7G6_1F6DbadIN3NWjnKmUpWKO4E8RQqygIRqxgFeNQ5ZHFZ9bYbs3uj6nrZyAlespdD23fgZ5y14fco-vj7Np24XGEftCN7x3UtWkhjL3mlDCVZVJM0g8H6WgbKJ_pf3cSBWwWuC708afqvwH2i385wPvZ5AHgH0OaRWrOnwAz7qTC
CODEN ITNSB3
Cites_doi 10.1016/j.jneumeth.2020.108599
10.3389/fpsyg.2013.00819
10.1017/CBO9780511546389.016
10.1109/T-AFFC.2011.15
10.1109/ACII.2009.5349479
10.1109/EMBC.2014.6944757
10.1016/j.compbiomed.2019.103469
10.1109/TNNLS.2017.2777489
10.1145/2816454
10.1109/IPACT.2017.8245212
10.1007/s11042-018-6907-3
10.1080/02699939508408966
10.1109/IEMBS.2009.5334139
10.1109/NER.2011.5910636
10.3389/fnsys.2013.00066
10.1109/NER.2013.6695876
10.1109/TAMD.2015.2431497
10.1037/0278-7393.3.1.52
10.1080/2326263X.2014.912881
10.1109/TITB.2009.2034649
10.1016/j.ijhcs.2018.11.011
10.1080/02699930903274322
10.1109/T-AFFC.2010.1
10.1109/TIM.2022.3149116
10.1145/2637293
10.1109/TBME.2010.2048568
10.1016/j.image.2012.01.016
10.1109/T-AFFC.2011.25
10.1109/TAFFC.2015.2496310
10.3389/fnhum.2014.00893
10.1109/TNNLS.2015.2498149
10.1109/TIM.2017.2759398
10.48550/ARXIV.1706.03762
10.3390/s18072074
10.1109/TCYB.2018.2797176
10.1109/ICMI.2002.1167043
10.1145/2637287
10.1007/978-1-4612-2836-3_15
ContentType Journal Article
DBID 97E
ESBDL
RIA
RIE
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
7X8
ADTOC
UNPAY
DOA
DOI 10.1109/TNSRE.2024.3457580
DatabaseName IEEE All-Society Periodicals Package (ASPP) 2005–Present
IEEE Xplore Open Access Journals
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Xplore
CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
MEDLINE - Academic
Unpaywall for CDI: Periodical Content
Unpaywall
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
MEDLINE - Academic
DatabaseTitleList MEDLINE
MEDLINE - Academic
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 3
  dbid: EIF
  name: MEDLINE
  url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search
  sourceTypes: Index Database
– sequence: 4
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/
  sourceTypes: Publisher
– sequence: 5
  dbid: UNPAY
  name: Unpaywall
  url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/
  sourceTypes: Open Access Repository
DeliveryMethod fulltext_linktorsrc
Discipline Occupational Therapy & Rehabilitation
EISSN 1558-0210
EndPage 3505
ExternalDocumentID oai_doaj_org_article_ccf8b1eba83c40a1b81e420b42f23ef9
10.1109/tnsre.2024.3457580
39255190
10_1109_TNSRE_2024_3457580
10672559
Genre orig-research
Research Support, Non-U.S. Gov't
Journal Article
GrantInformation_xml – fundername: Open Research Projects of Zhejiang Laboratory
  grantid: 2021KH0AB06
– fundername: Open Projects Program of National Laboratory of Pattern Recognition
  grantid: 202200014
– fundername: Cloud Ginger
  grantid: XR-1
– fundername: Excellent Youth Foundation of Anhui Scientific Committee
  grantid: 2208085J05
– fundername: National Key Research and Development Program of China
  grantid: 2021ZD0201502
– fundername: Open Fund Project of Key Laboratory of Civil Aviation Flight Technology and Flight Safety
  grantid: FZ2022KF15
– fundername: Special Fund for Key Program of Science and Technology of Anhui Province
  grantid: 202203a07020008
– fundername: Natural Science Foundation of Anhui Province
  grantid: 2108085MF207
  funderid: 10.13039/501100003995
– fundername: National Natural Science Foundation of China
  grantid: 62476004
  funderid: 10.13039/501100001809
– fundername: Cloud Ginger XR-1
  grantid: FZ2022KF15
GroupedDBID ---
-~X
0R~
29I
4.4
53G
5GY
5VS
6IK
97E
AAFWJ
AAJGR
AASAJ
AAWTH
ABAZT
ABVLG
ACGFO
ACGFS
ACIWK
ACPRK
AENEX
AETIX
AFPKN
AFRAH
AGSQL
AIBXA
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
DU5
EBS
EJD
ESBDL
F5P
GROUPED_DOAJ
HZ~
H~9
IFIPE
IPLJI
JAVBF
LAI
M43
O9-
OCL
OK1
P2P
RIA
RIE
RNS
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
RIG
7X8
ADTOC
UNPAY
ID FETCH-LOGICAL-c385t-153c1a3b63b249826776c31356e0f00c21468ba6e0f868174132b50b6b59850b3
IEDL.DBID UNPAY
ISSN 1534-4320
1558-0210
IngestDate Fri Oct 03 12:51:50 EDT 2025
Sun Sep 07 11:26:23 EDT 2025
Thu Jul 10 17:01:57 EDT 2025
Wed Feb 19 02:00:34 EST 2025
Wed Oct 01 00:21:32 EDT 2025
Wed Aug 27 01:53:13 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Language English
License https://creativecommons.org/licenses/by-nc-nd/4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c385t-153c1a3b63b249826776c31356e0f00c21468ba6e0f868174132b50b6b59850b3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ORCID 0009-0006-5646-2214
0000-0001-6318-8803
0000-0002-7629-7459
0000-0002-8103-4709
0000-0003-1343-8768
0000-0003-0057-0807
0000-0001-8064-9341
0000-0001-9727-366X
OpenAccessLink https://proxy.k.utb.cz/login?url=https://doi.org/10.1109/tnsre.2024.3457580
PMID 39255190
PQID 3102877540
PQPubID 23479
PageCount 10
ParticipantIDs proquest_miscellaneous_3102877540
doaj_primary_oai_doaj_org_article_ccf8b1eba83c40a1b81e420b42f23ef9
pubmed_primary_39255190
ieee_primary_10672559
unpaywall_primary_10_1109_tnsre_2024_3457580
crossref_primary_10_1109_TNSRE_2024_3457580
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 20240000
2024-00-00
20240101
2024-01-01
PublicationDateYYYYMMDD 2024-01-01
PublicationDate_xml – year: 2024
  text: 20240000
PublicationDecade 2020
PublicationPlace United States
PublicationPlace_xml – name: United States
PublicationTitle IEEE transactions on neural systems and rehabilitation engineering
PublicationTitleAbbrev TNSRE
PublicationTitleAlternate IEEE Trans Neural Syst Rehabil Eng
PublicationYear 2024
Publisher IEEE
Publisher_xml – name: IEEE
References ref13
ref35
ref12
ref15
ref37
ref36
ref31
ref30
ref11
ref33
ref32
ref2
ref1
Lu (ref6)
ref17
ref39
ref16
ref38
ref19
ref18
Tomono (ref43)
ref24
ref23
ref45
ref26
Yuen (ref4) 2009; 1
ref20
ref42
ref41
ref22
Murray (ref14) 2014; 10
ref21
Singh (ref25) 2014; 8
ref28
ref27
ref29
ref8
Wang (ref10) 2016
ref7
ref9
ref3
Bos (ref34) 2016; 56
ref5
ref40
Spence (ref44) 2002
References_xml – ident: ref24
  doi: 10.1016/j.jneumeth.2020.108599
– ident: ref41
  doi: 10.3389/fpsyg.2013.00819
– ident: ref45
  doi: 10.1017/CBO9780511546389.016
– year: 2016
  ident: ref10
  article-title: Iterative views agreement: An iterative low-rank based structured optimization method to multi-view spectral clustering
  publication-title: arXiv:1608.05560
– volume: 1
  start-page: 1
  issue: 3
  year: 2009
  ident: ref4
  article-title: Classification of human emotions from eeg signals using statistical features and neural network
  publication-title: Int. J. Integr. Eng.
– ident: ref21
  doi: 10.1109/T-AFFC.2011.15
– start-page: 249
  volume-title: Proc. Human Interface Symp.
  ident: ref43
  article-title: Effect that the image media with scent gives to contents understanding
– ident: ref19
  doi: 10.1109/ACII.2009.5349479
– ident: ref12
  doi: 10.1109/EMBC.2014.6944757
– ident: ref31
  doi: 10.1016/j.compbiomed.2019.103469
– ident: ref11
  doi: 10.1109/TNNLS.2017.2777489
– volume: 56
  start-page: 1
  issue: 3
  year: 2016
  ident: ref34
  article-title: EEG-based emotion recognition
  publication-title: Influence Vis. Auditory Stimuli
– start-page: 1170
  volume-title: Proc. 24th Int. Joint Conf. Artif. Intell.
  ident: ref6
  article-title: Combining eye movements and EEG to enhance emotion recognition
– ident: ref37
  doi: 10.1145/2816454
– ident: ref7
  doi: 10.1109/IPACT.2017.8245212
– ident: ref26
  doi: 10.1007/s11042-018-6907-3
– ident: ref36
  doi: 10.1080/02699939508408966
– ident: ref2
  doi: 10.1109/IEMBS.2009.5334139
– ident: ref5
  doi: 10.1109/NER.2011.5910636
– ident: ref17
  doi: 10.3389/fnsys.2013.00066
– ident: ref39
  doi: 10.1109/NER.2013.6695876
– ident: ref1
  doi: 10.1109/TAMD.2015.2431497
– ident: ref15
  doi: 10.1037/0278-7393.3.1.52
– ident: ref20
  doi: 10.1080/2326263X.2014.912881
– ident: ref3
  doi: 10.1109/TITB.2009.2034649
– ident: ref27
  doi: 10.1016/j.ijhcs.2018.11.011
– ident: ref35
  doi: 10.1080/02699930903274322
– ident: ref13
  doi: 10.1109/T-AFFC.2010.1
– ident: ref29
  doi: 10.1109/TIM.2022.3149116
– ident: ref30
  doi: 10.1145/2637293
– ident: ref32
  doi: 10.1109/TBME.2010.2048568
– ident: ref18
  doi: 10.1016/j.image.2012.01.016
– ident: ref22
  doi: 10.1109/T-AFFC.2011.25
– ident: ref33
  doi: 10.1109/TAFFC.2015.2496310
– volume: 10
  start-page: 1
  issue: 1
  year: 2014
  ident: ref14
  article-title: User-profile-based perceived olfactory and visual media synchronization
  publication-title: ACM Trans. Multimedia Comput., Commun., Appl.
– volume: 8
  start-page: 893
  year: 2014
  ident: ref25
  article-title: The brains response to pleasant touch: An EEG investigation of tactile caressing
  publication-title: Frontiers Hum. Neurosci.
  doi: 10.3389/fnhum.2014.00893
– ident: ref9
  doi: 10.1109/TNNLS.2015.2498149
– ident: ref38
  doi: 10.1109/TIM.2017.2759398
– ident: ref40
  doi: 10.48550/ARXIV.1706.03762
– ident: ref8
  doi: 10.3390/s18072074
– ident: ref23
  doi: 10.1109/TCYB.2018.2797176
– ident: ref42
  doi: 10.1109/ICMI.2002.1167043
– volume-title: The ICI report on the secret of the senses
  year: 2002
  ident: ref44
– ident: ref28
  doi: 10.1145/2637287
– ident: ref16
  doi: 10.1007/978-1-4612-2836-3_15
SSID ssj0017657
Score 2.4353614
Snippet Affective data is the basis of emotion recognition, which is mainly acquired through extrinsic elicitation. To investigate the enhancing effects of...
SourceID doaj
unpaywall
proquest
pubmed
crossref
ieee
SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
Publisher
StartPage 3496
SubjectTerms Adult
Algorithms
Electrodes
Electroencephalogram (EEG)
Electroencephalography
Electroencephalography - methods
electrooculogram (EOG)
Electrooculography
Electrooculography - methods
Emotion recognition
Emotions - physiology
Feature extraction
Female
Healthy Volunteers
Humans
Male
multi-modal fusion
Odorants
Photic Stimulation
Physiology
Reproducibility of Results
video-odor stimuli
Videos
Young Adult
SummonAdditionalLinks – databaseName: DOAJ Directory of Open Access Journals
  dbid: DOA
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3fT9UwFG4IL-CDAUG9KKYk6otOurXrj0cwA0IiJPdeDA8mTdt15MbrRvBeDf-9p912M6KJLzytW7uu7Tlbv7P2fAeht74yhhOjkhB6MWGUm8TStEykN4JmymYicnd-ueBnV-z8Or8ehPoKe8JaeuB24A6dq6RNvTWSOkZMamXqWUYsy6qM-iq67hGpemOqWz8QPHJ8wuvMoAEZ6d1liDqcXkzGBRiGGftEGYCVQAg5mJIic38XauVfqPMJ2ljWt-b-t5nPBzPRyRZ62kFIfNQ2fRut-foZejekC8bTlisAv8fjB0zcO-hb9Lj90ZRQqmhD-OBxv4kI0scwq5UYEkVxik1d4uLyFE9mN4FmGRe_mu-Qa-8xwEb8dVb6Jrksmzs8gSqX89kuujoppp_Pki7CQuKozBcJjI9LDbWcWjDDwNIQgjua0px7UhHiQtRvaU04k1yC8QK2q82J5TZXEo70OVqvm9q_RDiFD6V1rJKsFME91iplMqieCQeQU4gR-tAPsr5tiTR0NECI0lEkOohEdyIZoeMgh1XJQIIdL4Bq6E419P9UY4R2gxQHj-MiWFAjdNCLVcPLFFZITO2b5U9NA9wKnIDQgBetvFd3A5AEdKkg5-NKAf7qyaKGDj7oyd5j9OQV2gx1tr9-XqP1xd3S7wMYWtg3Ue__AEpTAH4
  priority: 102
  providerName: Directory of Open Access Journals
– databaseName: IEEE Xplore
  dbid: RIE
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwELZoL8CBZ4HwkpGAC2TrxI7tHClKWyGxlXa3qAcky3YctOo2qdoEVH49YydZbXlInOIkTpzJjONvYs83CL12ldac6Dz2qRdjRrmODU3KWDotaJqbVATuzs9TfnjMPp1kJ0OweoiFcc6FxWdu4othLr9sbOd_le16ujMPgbfQlpC8D9ZaTxkIHmg9oQczaDMlY4QMyXcX0_msAF8wZRPKAJ9In_8NgAGgBf8t3hiQAm__kGjlb5jzNrrZ1ef66oderTbGof27aDpK0C8_OZ10rZnYn7-RO_63iPfQnQGR4g-9Cd1HN1z9AL3ZZB_Gi556AL_Fs2vE3g_R1xDAe9aUUKvoMwLh2bgmCcp7MEiWGApFcYB1XeLi6ADPl988azMuvjencNZcYUCh-MuydE18VDYXeA637FbLHXS8Xyw-HsZDwobYUpm1Mbx7m2hqODXg1YHjIgS3NKEZd6QixPok4tJovye5BF8IXGGTEcNNlkvY0kdou25q9wThBL67xrJKslL4aFuT5zqF2zNhAcEKEaF3o9bUec_LoYI_Q3IV1K28utWg7gjtecWua3pO7XAA3r0auqiytpImcUZLaIboxMjEsZQYllYpdVUeoR2vr43melVF6NVoJwr6pp9w0bVruktFPXrzFIPwAI97A1pfPZpfhN6vLeoPSdoaBLwmydN_PMQzdMtX638OPUfb7UXnXgBcas3L0E1-AUv1CzA
  priority: 102
  providerName: IEEE
Title Multimodal Emotion Recognition Based on EEG and EOG Signals Evoked by the Video-Odor Stimuli
URI https://ieeexplore.ieee.org/document/10672559
https://www.ncbi.nlm.nih.gov/pubmed/39255190
https://www.proquest.com/docview/3102877540
https://doi.org/10.1109/tnsre.2024.3457580
https://doaj.org/article/ccf8b1eba83c40a1b81e420b42f23ef9
UnpaywallVersion publishedVersion
Volume 32
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  customDbUrl:
  eissn: 1558-0210
  dateEnd: 99991231
  omitProxy: true
  ssIdentifier: ssj0017657
  issn: 1558-0210
  databaseCode: DOA
  dateStart: 20210101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVIEE
  databaseName: IEEE Electronic Library (IEL)
  customDbUrl:
  eissn: 1558-0210
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0017657
  issn: 1558-0210
  databaseCode: RIE
  dateStart: 20010101
  isFulltext: true
  titleUrlDefault: https://ieeexplore.ieee.org/
  providerName: IEEE
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1db9MwFLVQ94B4gA0GKxqVkYAXSHFixx-P25RtQqJD_UBDQorsxEHVSjJtCWj8-l07adWOPcBTktq1a_s695wm91yE3thCa060ClzqxYBRrgNDwzyQVgsaKRMJr935ecRPZ-zTeXzeyeS4WJj15_chUR_rEjwF0LiIDSkDaCGBnm_xGHB3D23NRl8OvrWCqAz68RqM4B9l4IjMMkLm3kY2vJAX6--yq9wHNB-hh015qW9-68VizfkcP2mzGF17zUL3zsnFsKnNMPtzR9Hx38a1jR53GBQftEazgx7Y8il6u643jKet2AB-h8cbUt7P0HcfsvuzyqFW0uYAwuPlW0hwfghuMcdwkiQnWJc5Ts5O8GT-w-k04-RXdQGl5gYD7sRf57mtgrO8usITaLJZzHfR7DiZHp0GXYqGIKMyrgOY-SzU1HBqgMcBVRGCZzSkMbekICRzacOl0e5KcgnsB8iviYnhJlYSjvQ56pVVafcQDuFOazJWSJYLF19rlNIRNM9EBphViD56v1yy9LJV4kg9gyEqnY4m4yR1s5l2s9lHh25VVzWdirb_ABYh7TZlmmWFNKE1WkI3RIdGhpZFxLCoiKgtVB_tOptY644LR8H66PXSSFLYje4Riy5t1Vyn1OE1JyoIP-BFaz2rbwMSBXiqoOTDypz-Gom3i42RvPy_6vuoV1819hVApdoM_F8MAx_VOOj2yy28pQtO
linkProvider Unpaywall
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB5BORQOPAuEp5GAC2SbxE7iHClKu0C7lXa3VQ9Ilu04aNUlqUoCKr-esZOstjwkTnGezmTG9jex5xuAl6aUMglk5tvUiz6jifQVDQufG5nSKFNR6rg7DybJ-Ih9PIlP-mB1FwtjjHGLz8zIFt1cflHr1v4q27Z0ZxYCX4VrMWMs7sK1VpMGaeKIPbENM6w1CoYYmSDbnk9m0xy9wYiNKEOEwm0GOIQGiBdsb7w2JDnm_j7Vyt9Q5w3YbKszefFDLpdrI9HuLZgMMnQLUE5HbaNG-udv9I7_LeRtuNljUvKuM6I7cMVUd-HVOv8wmXfkA-Q1mV6i9r4Hn10I79e6wKvyLicQmQ6rkrC8g8NkQbCQ53tEVgXJD_fIbPHF8jaT_Ht9imfVBUEcSo4Xhan9w6I-JzN8ZLtcbMHRbj5_P_b7lA2-pjxufPz2OpRUJVShX4euS5ommoY0TkxQBoG2acS5knaPJxy9IXSGVRyoRMUZxy29DxtVXZmHQELseZVmJWdFauNtVZbJCB_PUo0YNk09eDNoTZx1zBzCeTRBJpy6hVW36NXtwY5V7OpKy6rtDuC3F30jFVqXXIVGSY7VBDJUPDQsChSLyoiaMvNgy-prrbpOVR68GOxEYOu0Uy6yMnX7TVCL3yzJIL7Ag86AVncP5ufB25VF_SFJU6GAlyR59I-XeA6b4_nBvtj_MPn0GK7bW7pfRU9gozlvzVMET4165prML2wvDn0
linkToUnpaywall http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bb9MwFLZQ94B44Dqg00BGAl7AxYkdXx43lG1CokO9oCEhRbbjoGolmbYENH49x0latbAHeIoTO3ZsH-d8nxJ_B6GXvjBGUKNJCL1IOBOGWBblRHkjWaxtLFvtzo9jcTLnH86Ss14mJ-yF2fx-H1H9ri7BUwCNi_mIcYAWCuj5jkgAdw_Qznz86eBLJ4jKoZ1WgxH8oyKByKx2yNxYyZYXasX6--gqNwHNO-h2U16Y659mudxwPkf3uihGV61mYfjn5HzU1Hbkfv2h6Phv_bqP7vYYFB90RvMA3fLlQ_RqU28YzzqxAfwaT7akvB-hr-2W3e9VDqXSLgYQnqz-QoL0IbjFHEMiTY-xKXOcnh7j6eJb0GnG6Y_qHHLtNQbciT8vcl-R07y6xFOoslkudtH8KJ29PyF9iAbimEpqAiPvIsOsYBZ4HFAVKYVjEUuEpwWlLoQNV9aEMyUUsB8gvzahVthEKziyx2hQVqV_inAEb1rreKF4LsP-Wqu1iaF6Lh1gVimH6M1qyrKLTokjaxkM1dlsPJ2kWRjNrB_NIToMs7ouGVS02wswCVm_KDPnCmUjb42CZqiJrIo8j6nlcREzX-gh2g02sdGckIGCDdGLlZFksBrDJxZT-qq5yljAa0FUEB7gSWc967sBiQI81ZDzdm1Of_WktYutnuz9X_F9NKgvG_8MoFJtn_dr5DdGXwlY
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=Multimodal+Emotion+Recognition+Based+on+EEG+and+EOG+Signals+Evoked+by+the+Video-Odor+Stimuli&rft.jtitle=IEEE+transactions+on+neural+systems+and+rehabilitation+engineering&rft.au=Wu%2C+Minchao&rft.au=Teng%2C+Wei&rft.au=Fan%2C+Cunhang&rft.au=Pei%2C+Shengbing&rft.date=2024&rft.eissn=1558-0210&rft.volume=32&rft.spage=3496&rft_id=info:doi/10.1109%2FTNSRE.2024.3457580&rft_id=info%3Apmid%2F39255190&rft.externalDocID=39255190
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1534-4320&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1534-4320&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1534-4320&client=summon