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EEG Emotion Recognition Supervised by Temporal Features of Video Stimuli.

Bibliographic Details
Title: EEG Emotion Recognition Supervised by Temporal Features of Video Stimuli.
Authors: Ran S, Zhong W, Duan D, Hu F, Ye L, Zhang Q
Source: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2024 Jul; Vol. 2024, pp. 1-7.
Publication Type: Journal Article; Validation Study
Language: English
Journal Info: Publisher: [IEEE] Country of Publication: United States NLM ID: 101763872 Publication Model: Print Cited Medium: Internet ISSN: 2694-0604 (Electronic) Linking ISSN: 23757477 NLM ISO Abbreviation: Annu Int Conf IEEE Eng Med Biol Soc Subsets: MEDLINE
Imprint Name(s): Original Publication: [Piscataway, NJ] : [IEEE], [2007]-
MeSH Terms: Emotions*/physiology , Electroencephalography* , Models, Neurological* , Pattern Recognition, Physiological* , Signal Processing, Computer-Assisted*, Algorithms ; Humans ; Male ; Female ; Young Adult ; Adult
Abstract: Different types of video stimuli can activate different reactions in the human brain and these signals can be captured and analyzed for emotion recognition applications. However, accurate recognition across subjects is still challenging due to non-stationary and low signal-to-noise ratio of EEG signals. Lying at the intersection of video content analysis, we make an attempt to supervise EEG features with the external evoked video features. An end-to-end framework is proposed by extracting useful emotional representations in EEG signals with the complementarity of video stimuli. For the feature obtaining, an EEG feature extractor and a video feature extractor are combined, along with a cross-modal transformer to align the distributions of the two type features, and then a self-attention mechanism is designed for fusion. The experiments on the subset of DEAP and self-collected datasets validate that the enhancement of EEG features supervised by stimulus information is a reliable solution for subject-independent emotion recognition.
Entry Date(s): Date Created: 20250305 Date Completed: 20250306 Latest Revision: 20250306
Update Code: 20260130
DOI: 10.1109/EMBC53108.2024.10781573
PMID: 40039117
Database: MEDLINE
FullText Text:
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  Data: EEG Emotion Recognition Supervised by Temporal Features of Video Stimuli.
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  Data: <searchLink fieldCode="AU" term="%22Ran+S%22">Ran S</searchLink><br /><searchLink fieldCode="AU" term="%22Zhong+W%22">Zhong W</searchLink><br /><searchLink fieldCode="AU" term="%22Duan+D%22">Duan D</searchLink><br /><searchLink fieldCode="AU" term="%22Hu+F%22">Hu F</searchLink><br /><searchLink fieldCode="AU" term="%22Ye+L%22">Ye L</searchLink><br /><searchLink fieldCode="AU" term="%22Zhang+Q%22">Zhang Q</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22101763872%22">Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference</searchLink> [Annu Int Conf IEEE Eng Med Biol Soc] 2024 Jul; Vol. 2024, pp. 1-7.
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  Data: Journal Article; Validation Study
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22[IEEE]%22">[IEEE] </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101763872 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>2694-0604 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2223757477%22">23757477 </searchLink><i>NLM ISO Abbreviation: </i>Annu Int Conf IEEE Eng Med Biol Soc <i>Subsets: </i>MEDLINE
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  Data: <i>Original Publication</i>: [Piscataway, NJ] : [IEEE], [2007]-
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  Label: MeSH Terms
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  Data: <searchLink fieldCode="MM" term="%22Emotions%22">Emotions*</searchLink>/<searchLink fieldCode="MM" term="%22Emotions+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Electroencephalography%22">Electroencephalography*</searchLink> <br /><searchLink fieldCode="MM" term="%22Models%2C+Neurological%22">Models, Neurological*</searchLink> <br /><searchLink fieldCode="MM" term="%22Pattern+Recognition%2C+Physiological%22">Pattern Recognition, Physiological*</searchLink> <br /><searchLink fieldCode="MM" term="%22Signal+Processing%2C+Computer-Assisted%22">Signal Processing, Computer-Assisted*</searchLink><br /><searchLink fieldCode="MH" term="%22Algorithms%22">Algorithms</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Male%22">Male</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Young+Adult%22">Young Adult</searchLink> ; <searchLink fieldCode="MH" term="%22Adult%22">Adult</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Different types of video stimuli can activate different reactions in the human brain and these signals can be captured and analyzed for emotion recognition applications. However, accurate recognition across subjects is still challenging due to non-stationary and low signal-to-noise ratio of EEG signals. Lying at the intersection of video content analysis, we make an attempt to supervise EEG features with the external evoked video features. An end-to-end framework is proposed by extracting useful emotional representations in EEG signals with the complementarity of video stimuli. For the feature obtaining, an EEG feature extractor and a video feature extractor are combined, along with a cross-modal transformer to align the distributions of the two type features, and then a self-attention mechanism is designed for fusion. The experiments on the subset of DEAP and self-collected datasets validate that the enhancement of EEG features supervised by stimulus information is a reliable solution for subject-independent emotion recognition.
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  Data: <i>Date Created: </i>20250305 <i>Date Completed: </i>20250306 <i>Latest Revision: </i>20250306
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  Data: 10.1109/EMBC53108.2024.10781573
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  Data: 40039117
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=40039117
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1109/EMBC53108.2024.10781573
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 1
    Subjects:
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Humans
        Type: general
      – SubjectFull: Male
        Type: general
      – SubjectFull: Female
        Type: general
      – SubjectFull: Young Adult
        Type: general
      – SubjectFull: Adult
        Type: general
      – SubjectFull: Emotions physiology
        Type: general
      – SubjectFull: Electroencephalography
        Type: general
      – SubjectFull: Models, Neurological
        Type: general
      – SubjectFull: Pattern Recognition, Physiological
        Type: general
      – SubjectFull: Signal Processing, Computer-Assisted
        Type: general
    Titles:
      – TitleFull: EEG Emotion Recognition Supervised by Temporal Features of Video Stimuli.
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            NameFull: Ran S
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            NameFull: Zhong W
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            NameFull: Duan D
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            NameFull: Hu F
      – PersonEntity:
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            NameFull: Ye L
      – PersonEntity:
          Name:
            NameFull: Zhang Q
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            – D: 01
              M: 07
              Text: 2024 Jul
              Type: published
              Y: 2024
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              Value: 2024
          Titles:
            – TitleFull: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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