November 17, 2022 Dataset Restricted Access

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

JSON Export

{
  "owners": [
    438651
  ], 
  "doi": "10.3389/fnhum.2022.1051463", 
  "stats": {
    "version_unique_downloads": 7.0, 
    "unique_views": 177.0, 
    "views": 224.0, 
    "version_views": 224.0, 
    "unique_downloads": 7.0, 
    "version_unique_views": 177.0, 
    "volume": 65350620.0, 
    "version_downloads": 48.0, 
    "downloads": 48.0, 
    "version_volume": 65350620.0
  }, 
  "links": {
    "latest_html": "https://zenodo.org/record/7332684", 
    "doi": "https://doi.org/10.3389/fnhum.2022.1051463", 
    "badge": "https://zenodo.org/badge/doi/10.3389/fnhum.2022.1051463.svg", 
    "html": "https://zenodo.org/record/7332684", 
    "latest": "https://zenodo.org/api/records/7332684"
  }, 
  "created": "2022-11-18T03:40:30.635555+00:00", 
  "updated": "2022-11-18T14:26:35.660808+00:00", 
  "conceptrecid": "7332683", 
  "revision": 2, 
  "id": 7332684, 
  "metadata": {
    "access_right_category": "danger", 
    "doi": "10.3389/fnhum.2022.1051463", 
    "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>", 
    "title": "OASIS EEG Dataset: Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset", 
    "journal": {
      "title": "Frontiers in Human Neuroscience"
    }, 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "7332683"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "7332684"
          }
        }
      ]
    }, 
    "access_right": "restricted", 
    "access_conditions": "<p>Please state your name, contact details (e-mail), institution, position, as well as the reason for requesting access to the DREAMER database.</p>\n\n<p>For additional info contact:</p>\n\n<p>Nikhil.Garg [-at-] Usherbrooke.ca</p>\n\n<p>f20180193 [-at-]goa.bits-pilani.ac.in</p>\n\n<p>Veeky [-at-] goa.bits-pilani.ac.in</p>", 
    "version": "1", 
    "keywords": [
      "Electroencephalography (EEG)", 
      "Brain Computer Interface (BCI)", 
      "Machine learning", 
      "Valence", 
      "Arousal", 
      "Emotion", 
      "Feature engineering"
    ], 
    "publication_date": "2022-11-17", 
    "creators": [
      {
        "affiliation": "UMR8520 Institut d'\u00e9lectronique, de micro\u00e9lectronique et de nanotechnologie (IEMN), France", 
        "name": "Garg, Nikhil"
      }, 
      {
        "affiliation": "Birla Institute of Technology and Science, India", 
        "name": "Garg, Rohit"
      }, 
      {
        "affiliation": "Birla Institute of Technology and Science, India", 
        "name": "Anand, Apoorv"
      }, 
      {
        "affiliation": "Birla Institute of Technology and Science, India", 
        "name": "Baths, Veeky"
      }
    ], 
    "notes": "Please cite as:\nN. 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", 
    "resource_type": {
      "type": "dataset", 
      "title": "Dataset"
    }
  }
}