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 |
|---|---|---|---|---|---|---|---|---|
ds005494 | Cued Recall of Paired Associates with Open-Loop Stimulation at Encoding or Retrieval | openneuro | https://openneuro.org/datasets/ds005494 | 10.18112/openneuro.ds005494.v1.0.1 | CC0 | {
"library": "eegdash",
"class": "EEGDashDataset",
"kwargs": {
"dataset": "ds005494"
}
} | https://huggingface.co/spaces/EEGDash/catalog | huggingface-space/scripts/push_metadata_stubs.py |
Cued Recall of Paired Associates with Open-Loop Stimulation at Encoding or Retrieval
Dataset ID: ds005494
Herrema2024_Cued
Canonical aliases: Herrema2024
At a glance: IEEG · Visual clinical/intervention · unknown · 20 subjects · 51 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="ds005494", 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 Herrema2024
ds = Herrema2024(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/ds005494")
Dataset metadata
| Subjects | 20 |
| Recordings | 51 |
| Tasks (count) | 1 |
| Channels | 100 (×4), 88 (×4), 68 (×3), 128 (×3), 177 (×3), 72 (×3), 141 (×2), 112 (×2), 64 (×2), 114 (×2), 14 (×2), 85 (×2), 16 (×2), 84 (×1), 111 (×1), 93 (×1), 122 (×1), 124 (×1), 95 (×1), 107 (×1), 102 (×1), 86 (×1), 110 (×1), 96 (×1), 146 (×1), 104 (×1), 119 (×1), 121 (×1), 138 (×1), 106 (×1) |
| Sampling rate (Hz) | 500 (×35), 1000 (×16) |
| Total duration (h) | 55.1 |
| Size on disk | 26.3 GB |
| Recording type | IEEG |
| Experimental modality | Visual |
| Paradigm type | Clinical/Intervention |
| Population | Unknown |
| Source | openneuro |
| License | CC0 |
| NEMAR citations | 0.0 |
Links
- DOI: 10.18112/openneuro.ds005494.v1.0.1
- OpenNeuro: ds005494
- 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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