November 17, 2022 Dataset Restricted Access

Garg, Nikhil; Garg, Rohit; Anand, Apoorv; Baths, Veeky

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  <identifier identifierType="URL">https://zenodo.org/record/7332684</identifier>
  <creators>
    <creator>
      <creatorName>Garg, Nikhil</creatorName>
      <givenName>Nikhil</givenName>
      <familyName>Garg</familyName>
      <affiliation>UMR8520 Institut d'électronique, de microélectronique et de nanotechnologie (IEMN), France</affiliation>
    </creator>
    <creator>
      <creatorName>Garg, Rohit</creatorName>
      <givenName>Rohit</givenName>
      <familyName>Garg</familyName>
      <affiliation>Birla Institute of Technology and Science, India</affiliation>
    </creator>
    <creator>
      <creatorName>Anand, Apoorv</creatorName>
      <givenName>Apoorv</givenName>
      <familyName>Anand</familyName>
      <affiliation>Birla Institute of Technology and Science, India</affiliation>
    </creator>
    <creator>
      <creatorName>Baths, Veeky</creatorName>
      <givenName>Veeky</givenName>
      <familyName>Baths</familyName>
      <affiliation>Birla Institute of Technology and Science, India</affiliation>
    </creator>
  </creators>
  <titles>
    <title>OASIS EEG Dataset: Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>Electroencephalography (EEG)</subject>
    <subject>Brain Computer Interface (BCI)</subject>
    <subject>Machine learning</subject>
    <subject>Valence</subject>
    <subject>Arousal</subject>
    <subject>Emotion</subject>
    <subject>Feature engineering</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-11-17</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/7332684</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.3389/fnhum.2022.1051463</relatedIdentifier>
  </relatedIdentifiers>
  <version>1</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/restrictedAccess">Restricted Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Emotion classification using electroencephalography (EEG) data and machine learning techniques have been on the rise in the recent past. However, past studies use data from medical-grade EEG setups with long set-up times and environment constraints. The images from the OASIS image dataset were used to elicit valence and arousal emotions, and the EEG data was recorded using the Emotiv Epoc X mobile EEG headset. We propose a novel feature ranking technique and incremental learning approach to analyze performance dependence on the number of participants. The analysis is carried out on publicly available datasets: DEAP and DREAMER for benchmarking. Leave-one-subject-out cross-validation was carried out to identify subject bias in emotion elicitation patterns. The collected dataset and pipeline are made open source.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Code: &lt;a href="https://github.com/rohitgarg025/Decoding_EEG"&gt;https://github.com/rohitgarg025/Decoding_EEG&lt;/a&gt;&lt;/p&gt;</description>
    <description descriptionType="Other">Please cite as:
N. Garg, R. Garg, A. Anand, V. Baths, "Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset," Frontiers in Human Neuroscience, Nov. 2022. Doi: 10.3389/fnhum.2022.1051463</description>
  </descriptions>
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