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Merge branch 'develop' of https://github.com/sccn/EEG-Dash-Data into develop
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‎README.md

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## Data source
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The data in EEG-DaSh originates from a collaboration involving 25 laboratories, encompassing 27,053 participants. This extensive collection includes MEEG data, which is a combination of EEG and MEG signals. The data is sourced from various studies conducted by these labs, involving both healthy subjects and clinical populations with conditions such as ADHD, depression, schizophrenia, dementia, autism, and psychosis. Additionally, data spans different mental states like sleep, meditation, and cognitive tasks. In addition, EEG-DaSh will incorporate a subset of the data converted from NEMAR, which includes 330 MEEG BIDS-formatted datasets, further expanding the archive with well-curated, standardized neuroelectromagnetic data.
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## Available data
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## Featured data
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The following datasets are currently available on EEGDash.
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The following HBN datasets are currently featured on EEGDash. Documentation about these datasets is available [here](https://neuromechanist.github.io/data/hbn/).
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| DatasetID | Participants | Files | Sessions | Population | Channels | Is 10-20? | Modality | Size |
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|---|---|---|---|---|---|---|---|---|
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| [ds002181](https://nemar.org/dataexplorer/detail?dataset_id=ds002181) | 20 | 949 | 1 | Healthy | 63 | 10-20 | Visual | 0.163 GB |
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| [ds002578](https://nemar.org/dataexplorer/detail?dataset_id=ds002578) | 2 | 22 | 1 | Healthy | 256 | 10-20 | Visual | 0.001 TB |
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| [ds002680](https://nemar.org/dataexplorer/detail?dataset_id=ds002680) | 14 | 4977 | 2 | Healthy | 0 | 10-20 | Visual | 0.01 TB |
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| [ds002691](https://nemar.org/dataexplorer/detail?dataset_id=ds002691) | 20 | 146 | 1 | Healthy | 32 | other | Visual | 0.001 TB |
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| [ds002718](https://nemar.org/dataexplorer/detail?dataset_id=ds002718) | 18 | 582 | 1 | Healthy | 70 | other | Visual | 0.005 TB |
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| [ds003061](https://nemar.org/dataexplorer/detail?dataset_id=ds003061) | 13 | 282 | 1 | Not specified | 64 | 10-20 | Auditory | 0.002 TB |
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| [ds003690](https://nemar.org/dataexplorer/detail?dataset_id=ds003690) | 75 | 2630 | 1 | Healthy | 61 | 10-20 | Auditory | 0.023 TB |
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| [ds003805](https://nemar.org/dataexplorer/detail?dataset_id=ds003805) | 1 | 10 | 1 | Healthy | 19 | 10-20 | Multisensory | 0 TB |
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| [ds003838](https://nemar.org/dataexplorer/detail?dataset_id=ds003838) | 65 | 947 | 1 | Healthy | 63 | 10-20 | Auditory | 100.2 GB |
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| [ds004010](https://nemar.org/dataexplorer/detail?dataset_id=ds004010) | 24 | 102 | 1 | Healthy | 64 | other | Multisensory | 0.025 TB |
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| [ds004040](https://nemar.org/dataexplorer/detail?dataset_id=ds004040) | 13 | 160 | 2 | Healthy | 64 | 10-20 | Auditory | 0.012 TB |
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| [ds004350](https://nemar.org/dataexplorer/detail?dataset_id=ds004350) | 24 | 960 | 2 | Healthy | 64 | other | Visual | 0.023 TB |
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| [ds004362](https://nemar.org/dataexplorer/detail?dataset_id=ds004362) | 109 | 9162 | 1 | Healthy | 64 | 10-20 | Visual | 0.008 TB |
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| [ds004504](https://nemar.org/dataexplorer/detail?dataset_id=ds004504) | 88 | 269 | 1 | Dementia | 19 | 10-20 | Resting State | 2.6 GB |
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| [ds004554](https://nemar.org/dataexplorer/detail?dataset_id=ds004554) | 16 | 101 | 1 | Healthy | 99 | 10-20 | Visual | 0.009 TB |
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| [ds004635](https://nemar.org/dataexplorer/detail?dataset_id=ds004635) | 48 | 292 | 1 | Healthy | 129 | other | Multisensory | 26.1 GB |
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| [ds004657](https://nemar.org/dataexplorer/detail?dataset_id=ds004657) | 24 | 838 | 6 | Not specified | 64 | 10-20 | Motor | 43.1 GB |
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| [ds004660](https://nemar.org/dataexplorer/detail?dataset_id=ds004660) | 21 | 299 | 1 | Healthy | 32 | 10-20 | Multisensory | 7.2 GB |
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| [ds004661](https://nemar.org/dataexplorer/detail?dataset_id=ds004661) | 17 | 90 | 1 | Not specified | 64 | 10-20 | Multisensory | 1.4 GB |
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| [ds004745](https://nemar.org/dataexplorer/detail?dataset_id=ds004745) | 52 | 762 | 1 | Healthy | 64 | ? | Auditory | 0 TB |
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| [ds004785](https://nemar.org/dataexplorer/detail?dataset_id=ds004785) | 17 | 74 | 1 | Healthy | 32 | ? | Motor | 0 TB |
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| [ds004841](https://nemar.org/dataexplorer/detail?dataset_id=ds004841) | 20 | 1034 | 2 | Not specified | 64 | 10-20 | Multisensory | 7.3 GB |
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| [ds004842](https://nemar.org/dataexplorer/detail?dataset_id=ds004842) | 14 | 719 | 2 | Not specified | 64 | ? | Multisensory | 5.2 GB |
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| [ds004843](https://nemar.org/dataexplorer/detail?dataset_id=ds004843) | 14 | 649 | 1 | Not specified | 64 | ? | Visual | 7.7 GB |
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| [ds004844](https://nemar.org/dataexplorer/detail?dataset_id=ds004844) | 17 | 481 | 4 | Not specified | 64 | ? | Multisensory | 22.3 GB |
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| [ds004849](https://nemar.org/dataexplorer/detail?dataset_id=ds004849) | 17 | 481 | 4 | Not specified | 64 | ? | Multisensory | 0.077 GB |
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| [ds004850](https://nemar.org/dataexplorer/detail?dataset_id=ds004850) | 17 | 481 | 4 | Not specified | 64 | ? | Multisensory | 0.077 GB |
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| [ds004851](https://nemar.org/dataexplorer/detail?dataset_id=ds004851) | 17 | 481 | 4 | Not specified | 64 | ? | Multisensory | 0.077 GB |
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| [ds004852](https://nemar.org/dataexplorer/detail?dataset_id=ds004852) | 17 | 481 | 4 | Not specified | 64 | ? | Multisensory | 0.077 GB |
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| [ds004853](https://nemar.org/dataexplorer/detail?dataset_id=ds004853) | 17 | 481 | 4 | Not specified | 64 | ? | Multisensory | 0.077 GB |
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| [ds004854](https://nemar.org/dataexplorer/detail?dataset_id=ds004854) | 17 | 481 | 4 | Not specified | 64 | ? | Multisensory | 0.077 GB |
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| [ds004855](https://nemar.org/dataexplorer/detail?dataset_id=ds004855) | 17 | 481 | 4 | Not specified | 64 | ? | Multisensory | 0.077 GB |
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| [ds005034](https://nemar.org/dataexplorer/detail?dataset_id=ds005034) | 25 | 406 | 2 | Healthy | 129 | ? | Visual | 61.4 GB |
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| [ds005079](https://nemar.org/dataexplorer/detail?dataset_id=ds005079) | 1 | 210 | 12 | Healthy | 64 | ? | Multisensory | 1.7 GB |
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| [ds005342](https://nemar.org/dataexplorer/detail?dataset_id=ds005342) | 32 | 134 | 1 | Healthy | 17 | ? | Visual | 2 GB |
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| [ds005410](https://nemar.org/dataexplorer/detail?dataset_id=ds005410) | 81 | 492 | 1 | Healthy | 63 | ? | ? | 19.8 GB |
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| [ds005505](https://nemar.org/dataexplorer/detail?dataset_id=ds005505) | 136 | 5393 | 1 | Healthy | 129 | other | Visual | 103 GB |
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| [ds005506](https://nemar.org/dataexplorer/detail?dataset_id=ds005506) | 150 | 5645 | 1 | Healthy | 129 | other | Visual | 112 GB |
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| [ds005507](https://nemar.org/dataexplorer/detail?dataset_id=ds005507) | 184 | 7273 | 1 | Healthy | 129 | other | Visual | 140 GB |
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| [ds005508](https://nemar.org/dataexplorer/detail?dataset_id=ds005508) | 324 | 13393 | 1 | Healthy | 129 | other | Visual | 230 GB |
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| [ds005509](https://nemar.org/dataexplorer/detail?dataset_id=ds005509) | 330 | 19980 | 1 | Healthy | 129 | other | Visual | 224 GB |
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| [ds005510](https://nemar.org/dataexplorer/detail?dataset_id=ds005510) | 135 | 4933 | 1 | Healthy | 129 | other | Visual | 91 GB |
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| [ds005511](https://nemar.org/dataexplorer/detail?dataset_id=ds005511) | 381 | 18604 | 1 | Healthy | 129 | other | Visual | 245 GB |
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| [ds005512](https://nemar.org/dataexplorer/detail?dataset_id=ds005512) | 257 | 9305 | 1 | Healthy | 129 | other | Visual | 157 GB |
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| [ds005514](https://nemar.org/dataexplorer/detail?dataset_id=ds005514) | 295 | 11565 | 1 | Healthy | 129 | other | Visual | 185 GB |
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| [ds005672](https://nemar.org/dataexplorer/detail?dataset_id=ds005672) | 3 | 18 | 1 | Healthy | 64 | 10-20 | Visual | 4.2 GB |
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| [ds005697](https://nemar.org/dataexplorer/detail?dataset_id=ds005697) | 52 | 210 | 1 | Healthy | 64 | 10-20 | Visual | 67 GB |
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| [ds005787](https://nemar.org/dataexplorer/detail?dataset_id=ds005787) | 30 | ? | 4 | Healthy | 64 | 10-20 | Visual | 185 GB |
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A total of [246 other datasets](datasets.md) are also available through EEGDash.
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## Data format
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EEGDash queries return a **Pytorch Dataset** formatted to facilitate machine learning (ML) and deep learning (DL) applications. PyTorch Datasets are the best format for EEGDash queries because they provide an efficient, scalable, and flexible structure for machine learning (ML) and deep learning (DL) applications. They allow seamless integration with PyTorch’s DataLoader, enabling efficient batching, shuffling, and parallel data loading, which is essential for training deep learning models on large EEG datasets.

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