{
  "fetched_at": "2026-10-20T03:48:55Z",
  "source_url": "https://www.kaggle.com/competitions/compomicssummer2018/data",
  "pages": [
    {
      "name": "rules",
      "content": "\n\t<!-- Begin Base Rules -->\n    <h3>One account per participant</h3>\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\n    <h3>No private sharing outside teams</h3>\n    <p>\n        Privately sharing code or data outside of teams is not permitted.\n        It's okay to share code if made available to all participants on the forums.\n    </p>\n    <h3>Team Mergers</h3>\n    <p>Team mergers are not allowed in this competition.</p>\n\n    <h3>Team Limits</h3>\n    <p>There is no maximum team size.</p>\n    <h3>Submission Limits</h3>\n    <p>You may submit a maximum of 3 entries per day.</p>\n    <p>You may select up to 2 final submissions for judging.</p>\n    <p>End Date: <strong>30/9/2018 12:00 AM UTC</strong></p>\n\t<!-- End Base Rules -->\n<p>The rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.</p>",
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    {
      "name": "data-description",
      "content": "<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sample_submission.csv</strong> - an example of a submission file</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>AF3,F7,F3,FC5,T7,P7,O1,02,P8,T8,FC6,F4,F8,AF4</strong> - raw measurements for 14 sensors on the head</li>\n<li><strong>eyeDetection</strong> - the class</li>\n</ul>\n\nThe test set contains a column called \"<strong>index</strong>\" that should be used in the submission file.",
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    {
      "name": "Description",
      "content": "<p>Eye state detection is the task of predicting the state of eye whether it is open or\nclosed. To achieve this task, a new trend of using brain activity signals by the mean\nof electroencephalography (EEG) measures for the training and testing of various\nmachine learning classification algorithms was investigated by many researchers.</p>\n\n<p>The task of predicting human actions via brain signals takes high\nimportance and usability in various fields such as computer games, health\ncare and bio-medical systems, emotion tracking, smart home device\ncontrolling and internet of things, military, and detection of car driving drowsiness.</p>\n\n<p>\nAn <a href=\"https://www.emotiv.com/epoc/\">Emotiv headset</a> device with 14 sensors has been used to record brain signals.\nThe duration time of each recording was 117 seconds. Then, the different eye states\nobserved during each recording were manually added. Each data point consists of 14 EEG\nfeatures and an eye-state class (either 0 for open, or 1 for closed). </p>\n<p>\nThe dataset was\ncreated by Rösler and Suendermann, which was firstly used by them in\n\n\"O. Rösler and D. Suendermann, A first step towards eye state prediction using EEG, Proc.\nof the AIHLS, 2013\"\n</p>",
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    {
      "name": "Evaluation",
      "content": "The task is to predict the probability of a blinking eye given 14 EEG sensor measurements.\nYou will be evaluated using the <a href=\"http://fastml.com/what-you-wanted-to-know-about-auc/\">AUC metric</a>.",
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