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 |
|---|---|---|---|---|---|---|---|---|
nm000232 | THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition | nemar | https://openneuro.org/datasets/nm000232 | 10.17605/OSF.IO/3JK45 | CC-BY 4.0 | {
"library": "eegdash",
"class": "EEGDashDataset",
"kwargs": {
"dataset": "nm000232"
}
} | https://huggingface.co/spaces/EEGDash/catalog | huggingface-space/scripts/push_metadata_stubs.py |
THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition
Dataset ID: nm000232
Gifford2019
At a glance: EEG · 10 subjects · 638 recordings · CC-BY 4.0
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="nm000232", cache_dir="./cache")
print(len(ds), "recordings")
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/nm000232")
Dataset metadata
| Subjects | 10 |
| Recordings | 638 |
| Tasks (count) | 5 |
| Channels | 63 (×319) |
| Sampling rate (Hz) | 1000 (×319) |
| Total duration (h) | 87.3 |
| Size on disk | 203.9 GB |
| Recording type | EEG |
| Source | nemar |
| License | CC-BY 4.0 |
Links
- DOI: 10.17605/OSF.IO/3JK45
- NEMAR: nm000232
- 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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