November 17, 2022 Dataset Restricted Access

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

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{
  "description": "<p>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.&nbsp;</p>\n\n<p>Code: <a href=\"https://github.com/rohitgarg025/Decoding_EEG\">https://github.com/rohitgarg025/Decoding_EEG</a></p>", 
  "creator": [
    {
      "affiliation": "UMR8520 Institut d'\u00e9lectronique, de micro\u00e9lectronique et de nanotechnologie (IEMN), France", 
      "@type": "Person", 
      "name": "Garg, Nikhil"
    }, 
    {
      "affiliation": "Birla Institute of Technology and Science, India", 
      "@type": "Person", 
      "name": "Garg, Rohit"
    }, 
    {
      "affiliation": "Birla Institute of Technology and Science, India", 
      "@type": "Person", 
      "name": "Anand, Apoorv"
    }, 
    {
      "affiliation": "Birla Institute of Technology and Science, India", 
      "@type": "Person", 
      "name": "Baths, Veeky"
    }
  ], 
  "url": "https://zenodo.org/record/7332684", 
  "datePublished": "2022-11-17", 
  "version": "1", 
  "keywords": [
    "Electroencephalography (EEG)", 
    "Brain Computer Interface (BCI)", 
    "Machine learning", 
    "Valence", 
    "Arousal", 
    "Emotion", 
    "Feature engineering"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.3389/fnhum.2022.1051463", 
  "@id": "https://doi.org/10.3389/fnhum.2022.1051463", 
  "@type": "Dataset", 
  "name": "OASIS EEG Dataset: Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset"
}