November 17, 2022 Dataset Restricted Access

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

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  <dc:creator>Garg, Nikhil</dc:creator>
  <dc:creator>Garg, Rohit</dc:creator>
  <dc:creator>Anand, Apoorv</dc:creator>
  <dc:creator>Baths, Veeky</dc:creator>
  <dc:date>2022-11-17</dc:date>
  <dc:description>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. 

Code: https://github.com/rohitgarg025/Decoding_EEG</dc:description>
  <dc:description>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</dc:description>
  <dc:identifier>https://zenodo.org/record/7332684</dc:identifier>
  <dc:identifier>10.3389/fnhum.2022.1051463</dc:identifier>
  <dc:identifier>oai:zenodo.org:7332684</dc:identifier>
  <dc:rights>info:eu-repo/semantics/restrictedAccess</dc:rights>
  <dc:source>Frontiers in Human Neuroscience</dc:source>
  <dc:subject>Electroencephalography (EEG)</dc:subject>
  <dc:subject>Brain Computer Interface (BCI)</dc:subject>
  <dc:subject>Machine learning</dc:subject>
  <dc:subject>Valence</dc:subject>
  <dc:subject>Arousal</dc:subject>
  <dc:subject>Emotion</dc:subject>
  <dc:subject>Feature engineering</dc:subject>
  <dc:title>OASIS EEG Dataset: Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>