{
  "id": 492187,
  "title": "Graphs and EEG data",
  "url": "/competitions/hms-harmful-brain-activity-classification/discussion/492187",
  "author_name": "Pavel Snopov",
  "post_date": "2024-04-09T00:00:09.494000",
  "votes": 1,
  "comment_count": 0,
  "views": 0,
  "content": "<p>Hi everyone!</p>\n<p>I wanted to take a moment to extend my heartfelt congratulations to each and every one of you for participating in the EEG data competition!</p>\n<p>While I regret not having much time to fully dive into the competition, I did have an idea for GNN-based EEG classification. Unfortunately, time constraints prevented me from implementing it. However, I didn't want my limited time to go to waste, so I've created a <a href=\"https://www.kaggle.com/code/snopoff/graphs-from-eeg-data/notebook\" target=\"_blank\">kernel</a> that explores several methods for creating brain graphs from EEG data. It's my hope that this kernel serves as a helpful resource for enthusiasts looking to explore this approach further. I welcome your feedback and comments!</p>\n<p>I also had a <a href=\"https://www.kaggle.com/code/snopoff/gnns-meets-hms-only-train/notebook\" target=\"_blank\">kernel</a> devoted to GNN training, check this out if you are also interested in it!</p>\n<p>Once again, congratulations to all participants! I noticed there weren't many GNN solutions among the public kernels, but I'm optimistic that we'll see more of them in the near future!</p>",
  "messages": [
    {
      "id": 2742487,
      "postDate": "2024-04-09T00:00:09.493Z",
      "content": "<p>Hi everyone!</p>\n<p>I wanted to take a moment to extend my heartfelt congratulations to each and every one of you for participating in the EEG data competition!</p>\n<p>While I regret not having much time to fully dive into the competition, I did have an idea for GNN-based EEG classification. Unfortunately, time constraints prevented me from implementing it. However, I didn't want my limited time to go to waste, so I've created a <a href=\"https://www.kaggle.com/code/snopoff/graphs-from-eeg-data/notebook\" target=\"_blank\">kernel</a> that explores several methods for creating brain graphs from EEG data. It's my hope that this kernel serves as a helpful resource for enthusiasts looking to explore this approach further. I welcome your feedback and comments!</p>\n<p>I also had a <a href=\"https://www.kaggle.com/code/snopoff/gnns-meets-hms-only-train/notebook\" target=\"_blank\">kernel</a> devoted to GNN training, check this out if you are also interested in it!</p>\n<p>Once again, congratulations to all participants! I noticed there weren't many GNN solutions among the public kernels, but I'm optimistic that we'll see more of them in the near future!</p>",
      "rawMarkdown": "Hi everyone!\n\nI wanted to take a moment to extend my heartfelt congratulations to each and every one of you for participating in the EEG data competition!\n\nWhile I regret not having much time to fully dive into the competition, I did have an idea for GNN-based EEG classification. Unfortunately, time constraints prevented me from implementing it. However, I didn't want my limited time to go to waste, so I've created a [kernel](https://www.kaggle.com/code/snopoff/graphs-from-eeg-data/notebook) that explores several methods for creating brain graphs from EEG data. It's my hope that this kernel serves as a helpful resource for enthusiasts looking to explore this approach further. I welcome your feedback and comments!\n\nI also had a [kernel](https://www.kaggle.com/code/snopoff/gnns-meets-hms-only-train/notebook) devoted to GNN training, check this out if you are also interested in it!\n\nOnce again, congratulations to all participants! I noticed there weren't many GNN solutions among the public kernels, but I'm optimistic that we'll see more of them in the near future!",
      "votes": 1
    }
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  "comments": [],
  "raw_markdown_by_id": {
    "2742487": "Hi everyone!\n\nI wanted to take a moment to extend my heartfelt congratulations to each and every one of you for participating in the EEG data competition!\n\nWhile I regret not having much time to fully dive into the competition, I did have an idea for GNN-based EEG classification. Unfortunately, time constraints prevented me from implementing it. However, I didn't want my limited time to go to waste, so I've created a [kernel](https://www.kaggle.com/code/snopoff/graphs-from-eeg-data/notebook) that explores several methods for creating brain graphs from EEG data. It's my hope that this kernel serves as a helpful resource for enthusiasts looking to explore this approach further. I welcome your feedback and comments!\n\nI also had a [kernel](https://www.kaggle.com/code/snopoff/gnns-meets-hms-only-train/notebook) devoted to GNN training, check this out if you are also interested in it!\n\nOnce again, congratulations to all participants! I noticed there weren't many GNN solutions among the public kernels, but I'm optimistic that we'll see more of them in the near future!"
  }
}