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 |
|---|---|---|---|---|---|---|---|---|
ds005946 | ERC_CoG PROMENADE - WP2 - MetaImagery (Metaphor and Mental Imagery) | openneuro | https://openneuro.org/datasets/ds005946 | 10.18112/openneuro.ds005946.v1.0.1 | CC0 | {
"library": "eegdash",
"class": "EEGDashDataset",
"kwargs": {
"dataset": "ds005946"
}
} | https://huggingface.co/spaces/EEGDash/catalog | huggingface-space/scripts/push_metadata_stubs.py |
ERC_CoG PROMENADE - WP2 - MetaImagery (Metaphor and Mental Imagery)
Dataset ID: ds005946
Frau2025
Canonical aliases: PROMENADE
At a glance: EEG · Visual perception · healthy · 39 subjects · 39 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="ds005946", 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 PROMENADE
ds = PROMENADE(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/ds005946")
Dataset metadata
| Subjects | 39 |
| Recordings | 39 |
| Tasks (count) | 1 |
| Channels | 60 (×39) |
| Sampling rate (Hz) | 1000 (×39) |
| Total duration (h) | 18.3 |
| Size on disk | 14.8 GB |
| Recording type | EEG |
| Experimental modality | Visual |
| Paradigm type | Perception |
| Population | Healthy |
| Source | openneuro |
| License | CC0 |
Links
- DOI: 10.18112/openneuro.ds005946.v1.0.1
- OpenNeuro: ds005946
- 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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