| --- |
| pretty_name: "Reliability-Dubois2024" |
| license: cc0-1.0 |
| tags: |
| - eeg |
| - neuroscience |
| - eegdash |
| - brain-computer-interface |
| - pytorch |
| - auditory |
| - unknown |
| size_categories: |
| - n<1K |
| task_categories: |
| - other |
| --- |
| |
| # Reliability-Dubois2024 |
|
|
| **Dataset ID:** `ds006545` |
|
|
| _ReliabilityDubois2024_ |
|
|
| **Canonical aliases:** `Dubois2024` |
|
|
| > **At a glance:** FNIRS · Auditory unknown · unknown · 49 subjects · 98 recordings · CC0 |
|
|
| ## Load this dataset |
|
|
| This repo is a **pointer**. The raw EEG data lives at its canonical source |
| (OpenNeuro / NEMAR); [EEGDash](https://github.com/eegdash/EEGDash) streams it |
| on demand and returns a PyTorch / braindecode dataset. |
|
|
| ```python |
| # pip install eegdash |
| from eegdash import EEGDashDataset |
| |
| ds = EEGDashDataset(dataset="ds006545", cache_dir="./cache") |
| print(len(ds), "recordings") |
| ``` |
|
|
| You can also load it by canonical alias — these are registered classes in `eegdash.dataset`: |
|
|
| ```python |
| from eegdash.dataset import Dubois2024 |
| ds = Dubois2024(cache_dir="./cache") |
| ``` |
|
|
| If the dataset has been mirrored to the HF Hub in braindecode's Zarr layout, |
| you can also pull it directly: |
|
|
| ```python |
| from braindecode.datasets import BaseConcatDataset |
| ds = BaseConcatDataset.pull_from_hub("EEGDash/ds006545") |
| ``` |
|
|
|
|
| ## Dataset metadata |
|
|
| | | | |
| |---|---| |
| | **Subjects** | 49 | |
| | **Recordings** | 98 | |
| | **Tasks (count)** | 1 | |
| | **Channels** | 6180 (×2), 6498 (×2), 8340 (×2), 3678 (×2), 6708 (×2), 12180 (×2), 6990 (×1), 6696 (×1), 5400 (×1), 6282 (×1), 4614 (×1), 6432 (×1), 11682 (×1), 4170 (×1), 9558 (×1), 5640 (×1), 7890 (×1), 4752 (×1), 7794 (×1), 5076 (×1), 12300 (×1), 5676 (×1), 3552 (×1), 12738 (×1), 8730 (×1), 9012 (×1), 5280 (×1), 14520 (×1), 7524 (×1), 16266 (×1), 14592 (×1), 15288 (×1), 9966 (×1), 8874 (×1), 11094 (×1), 5568 (×1), 9276 (×1), 3630 (×1), 13014 (×1), 7932 (×1), 3570 (×1), 4278 (×1), 5256 (×1), 7464 (×1), 6060 (×1), 11142 (×1), 6126 (×1), 12468 (×1), 4194 (×1), 16086 (×1), 6768 (×1), 6744 (×1), 15354 (×1), 5190 (×1), 10224 (×1), 6930 (×1), 14820 (×1), 5862 (×1), 13494 (×1), 8250 (×1), 4866 (×1), 5130 (×1), 4986 (×1), 7332 (×1), 4626 (×1), 3792 (×1), 10458 (×1), 4530 (×1), 6522 (×1), 14142 (×1), 8646 (×1), 4062 (×1), 4122 (×1), 8082 (×1), 4734 (×1), 7596 (×1), 16122 (×1), 7044 (×1), 16464 (×1), 5766 (×1), 8832 (×1), 4116 (×1), 4098 (×1), 8592 (×1), 3900 (×1), 4764 (×1), 5082 (×1), 7800 (×1), 4308 (×1), 9180 (×1), 10254 (×1), 9426 (×1) | |
| | **Sampling rate (Hz)** | 3.7593757537230927 (×1), 3.759380549030849 (×1), 3.759383854917815 (×1), 3.7593844649490076 (×1), 3.7593369412842033 (×1), 3.759382548055328 (×1), 3.759381637968117 (×1), 3.7593335689320417 (×1), 3.7593809119130897 (×1), 3.7593790288058946 (×1), 3.7593368622422507 (×1), 3.759384668748248 (×1), 3.75938652671013 (×1), 3.759327715365909 (×1), 3.759378131656764 (×1), 3.7593917495093443 (×1), 3.759335491657337 (×1), 3.7593770035234657 (×1), 3.759381147438357 (×1), 3.759382410127993 (×1), 3.7593847313363185 (×1), 3.759326944731349 (×1), 3.7593764115478234 (×1), 3.7593815029752466 (×1), 3.759380476653631 (×1), 3.7593798802765264 (×1), 3.7593841548655034 (×1), 3.7593343198689566 (×1), 3.7593316689597076 (×1), 3.75938158151899 (×1), 3.7593827348988054 (×1), 3.759335334223433 (×1), 3.7593859458888867 (×1), 3.7593821349246923 (×1), 3.7593764941046097 (×1), 3.7593750038748928 (×1), 3.759382593611545 (×1), 3.7593818001216643 (×1), 3.759380541825277 (×1), 3.759340320968606 (×1), 3.759327770404511 (×1), 3.7593764966001504 (×1), 3.759382926882352 (×1), 3.759380897280349 (×1), 3.759385538565235 (×1), 3.759336320191231 (×1), 3.759384688523407 (×1), 3.7593320784412283 (×1), 3.7593804486200146 (×1), 3.759336916674929 (×1), 3.759376802130892 (×1), 3.7593834552836913 (×1), 3.7593794232712234 (×1), 3.7593266384012547 (×1), 3.7593813477897906 (×1), 3.759383655551909 (×1), 3.7593783750690566 (×1), 3.759379675664703 (×1), 3.7593859613989697 (×1), 3.7593797563033773 (×1), 3.759332720066484 (×1), 3.7593852258423093 (×1), 3.759381014194889 (×1), 3.7593815330436198 (×1), 3.7593816783733027 (×1), 3.759377394526281 (×1), 3.7593787725752463 (×1), 3.759384908721897 (×1), 3.7593360211640108 (×1), 3.7593806230201263 (×1), 3.7593790725510097 (×1), 3.7593852959156377 (×1), 3.75933410440123 (×1), 3.7593801964283244 (×1), 3.7593830794615157 (×1), 3.759380220764679 (×1), 3.7593374155646906 (×1), 3.75933672882927 (×1), 3.759382867121934 (×1), 3.7593800192877977 (×1), 3.759381561915346 (×1), 3.7593808053564546 (×1), 3.759384261106816 (×1), 3.759384299582689 (×1), 3.7593826417073126 (×1), 3.759332685108552 (×1), 3.7593841728783493 (×1), 3.7593851070356754 (×1), 3.759331427389511 (×1), 3.7593278601126636 (×1), 3.759384944435528 (×1), 3.7593821400544667 (×1), 3.759377231180893 (×1), 3.7593400623176056 (×1), 3.7593792061899447 (×1), 3.759337444344509 (×1), 3.759389442742258 (×1), 3.7593814407919455 (×1) | |
| | **Size on disk** | 46.7 GB | |
| | **Recording type** | FNIRS | |
| | **Experimental modality** | Auditory | |
| | **Paradigm type** | Unknown | |
| | **Population** | Unknown | |
| | **Source** | openneuro | |
| | **License** | CC0 | |
|
|
| ## Links |
|
|
| - **DOI:** [10.18112/openneuro.ds006545.v1.0.0](https://doi.org/10.18112/openneuro.ds006545.v1.0.0) |
| - **OpenNeuro:** [ds006545](https://openneuro.org/datasets/ds006545) |
| - **Browse 700+ datasets:** [EEGDash catalog](https://huggingface.co/spaces/EEGDash/catalog) |
| - **Docs:** <https://eegdash.org> |
| - **Code:** <https://github.com/eegdash/EEGDash> |
|
|
| --- |
|
|
| _Auto-generated from [dataset_summary.csv](https://github.com/eegdash/EEGDash/blob/main/eegdash/dataset/dataset_summary.csv) and the [EEGDash API](https://data.eegdash.org/api/eegdash/datasets/summary/ds006545). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._ |
|
|
