dataset_id stringclasses 1
value | title stringclasses 1
value | source stringclasses 1
value | source_url stringclasses 1
value | doi stringclasses 1
value | license stringclasses 1
value | loader dict | catalog stringclasses 1
value | generated_by stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|
ds005857 | ltpDelayRepFRReadOnly | openneuro | https://openneuro.org/datasets/ds005857 | 10.18112/openneuro.ds005857.v1.0.0 | CC0 | {
"library": "eegdash",
"class": "EEGDashDataset",
"kwargs": {
"dataset": "ds005857"
}
} | https://huggingface.co/spaces/EEGDash/catalog | huggingface-space/scripts/push_metadata_stubs.py |
ltpDelayRepFRReadOnly
Dataset ID: ds005857
Broitman2025
Canonical aliases: Broitman2019
At a glance: EEG · Visual memory · unknown · 29 subjects · 110 recordings · CC0
Load this dataset
This repo is a pointer. The raw EEG data lives at its canonical source (OpenNeuro / NEMAR); EEGDash streams it on demand and returns a PyTorch / braindecode dataset.
# pip install eegdash
from eegdash import EEGDashDataset
ds = EEGDashDataset(dataset="ds005857", cache_dir="./cache")
print(len(ds), "recordings")
You can also load it by canonical alias — these are registered classes in eegdash.dataset:
from eegdash.dataset import Broitman2019
ds = Broitman2019(cache_dir="./cache")
If the dataset has been mirrored to the HF Hub in braindecode's Zarr layout, you can also pull it directly:
from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/ds005857")
Dataset metadata
| Subjects | 29 |
| Recordings | 110 |
| Tasks (count) | 1 |
| Channels | 137 (×110) |
| Sampling rate (Hz) | 2048 (×110) |
| Total duration (h) | 101.0 |
| Size on disk | 284.4 GB |
| Recording type | EEG |
| Experimental modality | Visual |
| Paradigm type | Memory |
| Population | Unknown |
| Source | openneuro |
| License | CC0 |
Links
- DOI: 10.18112/openneuro.ds005857.v1.0.0
- OpenNeuro: ds005857
- Browse 700+ datasets: EEGDash catalog
- Docs: https://eegdash.org
- Code: https://github.com/eegdash/EEGDash
Auto-generated from dataset_summary.csv and the EEGDash API. Do not edit this file by hand — update the upstream source and re-run scripts/push_metadata_stubs.py.
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