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 |
|---|---|---|---|---|---|---|---|---|
ds004603 | Visual Attribute-Specific Contextual Trajectory Paradigm | openneuro | https://openneuro.org/datasets/ds004603 | 10.18112/openneuro.ds004603.v1.1.0 | CC0 | {
"library": "eegdash",
"class": "EEGDashDataset",
"kwargs": {
"dataset": "ds004603"
}
} | https://huggingface.co/spaces/EEGDash/catalog | huggingface-space/scripts/push_metadata_stubs.py |
Visual Attribute-Specific Contextual Trajectory Paradigm
Dataset ID: ds004603
Lowe2023
Canonical aliases: VisualContextTrajectory
At a glance: EEG · 37 subjects · 37 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="ds004603", 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 VisualContextTrajectory
ds = VisualContextTrajectory(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/ds004603")
Dataset metadata
| Subjects | 37 |
| Recordings | 37 |
| Tasks (count) | 1 |
| Channels | 65 (×37) |
| Sampling rate (Hz) | 1024 (×37) |
| Total duration (h) | 30.7 |
| Size on disk | 27.4 GB |
| Recording type | EEG |
| Source | openneuro |
| License | CC0 |
| NEMAR citations | 1.0 |
Links
- DOI: 10.18112/openneuro.ds004603.v1.1.0
- OpenNeuro: ds004603
- 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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