{
  "id": 466746,
  "title": "Nilearn, MNE-Python - toolkits for EEG signals data processing ",
  "url": "/competitions/hms-harmful-brain-activity-classification/discussion/466746",
  "author_name": "",
  "post_date": "2024-01-09T20:13:25.097252700Z",
  "votes": 30,
  "comment_count": 1,
  "views": 0,
  "content": "<p><strong>Nilearn</strong>  - <a href=\"https://nilearn.github.io/stable/index.html\" target=\"_blank\">https://nilearn.github.io/stable/index.html</a><br>\nNilearn enables approachable and versatile analyses of brain volumes. It provides statistical and machine-learning tools, with instructive documentation &amp; open community.<br>\nIt supports general linear model (GLM) based analysis and leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.</p>\n<hr>\n<p><strong>MNE-Python</strong> - <a href=\"https://mne.tools/stable/help/index.html\" target=\"_blank\">https://mne.tools/stable/help/index.html</a><br>\nOpen-source Python package for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, NIRS, and more.</p>",
  "messages": [
    {
      "id": "2594344",
      "postDate": "01/09/2024 20:13:25",
      "content": "<p><strong>Nilearn</strong>  - <a href=\"https://nilearn.github.io/stable/index.html\" target=\"_blank\">https://nilearn.github.io/stable/index.html</a><br>\nNilearn enables approachable and versatile analyses of brain volumes. It provides statistical and machine-learning tools, with instructive documentation &amp; open community.<br>\nIt supports general linear model (GLM) based analysis and leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.</p>\n<hr>\n<p><strong>MNE-Python</strong> - <a href=\"https://mne.tools/stable/help/index.html\" target=\"_blank\">https://mne.tools/stable/help/index.html</a><br>\nOpen-source Python package for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, NIRS, and more.</p>",
      "rawMarkdown": "**Nilearn**  - https://nilearn.github.io/stable/index.html\nNilearn enables approachable and versatile analyses of brain volumes. It provides statistical and machine-learning tools, with instructive documentation & open community.\nIt supports general linear model (GLM) based analysis and leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.\n\n***\n\n **MNE-Python** - https://mne.tools/stable/help/index.html\nOpen-source Python package for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, NIRS, and more.",
      "votes": null
    },
    {
      "id": "2632520",
      "postDate": "02/02/2024 12:13:16",
      "content": "<p>Thanks for sharing! i'm looking foward MNE instead of librosa</p>",
      "rawMarkdown": "Thanks for sharing! i'm looking foward MNE instead of librosa",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2632520,
      "author_name": "seoyunje",
      "author_url": "",
      "post_date": "02/02/2024 12:13:16",
      "content": "<p>Thanks for sharing! i'm looking foward MNE instead of librosa</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "2594344": "**Nilearn**  - https://nilearn.github.io/stable/index.html\nNilearn enables approachable and versatile analyses of brain volumes. It provides statistical and machine-learning tools, with instructive documentation & open community.\nIt supports general linear model (GLM) based analysis and leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.\n\n***\n\n **MNE-Python** - https://mne.tools/stable/help/index.html\nOpen-source Python package for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, NIRS, and more.",
    "2632520": "Thanks for sharing! i'm looking foward MNE instead of librosa"
  },
  "source": "meta"
}