DS002680: eeg dataset, 14 subjects#
Go-nogo categorization and detection task
Citation: Arnaud Delorme (20). Go-nogo categorization and detection task. 10.18112/openneuro.ds002680.v1.2.0
14-participant EEG dataset — Go-nogo categorization and detection task.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS002680
dataset = DS002680(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS002680(cache_dir="./data", subject="01")
Advanced query
dataset = DS002680(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{ds002680,
title = {Go-nogo categorization and detection task},
author = {Arnaud Delorme},
doi = {10.18112/openneuro.ds002680.v1.2.0},
url = {https://doi.org/10.18112/openneuro.ds002680.v1.2.0},
}
About This Dataset#
Participants seated in a dimly lit room at 110 cm from a computer screen piloted from a PC computer. Two tasks alternated: a categorization task and a recognition task. In both tasks, target images and non-target images were equally likely presented. Participants were tested in two recording phases. The first day was composed of 13 series, the second day of 12 series, with 100 images per series (see details of the series below). To start a series, subjects had to press a touch-sensitive button. A small fixation point (smaller than 0.1 degree of visual angle) was drawn in the middle of a black screen. Then, an 8 bit color vertical photograph (256 pixels wide by 384 pixels high which roughly correspond to 4.5 degree of visual angle in width and 6.5 degree in height) was flashed for 20 ms (2 frames of a 100 Hz SVGA screen) using a programmable graphic board (VSG 2.1, Cambridge Research Systems). This short presentation time avoid that subjects use exploratory eye movement to respond. Participants gave their responses following a go/nogo paradigm. For each target, they had to lift their finger from the button as quickly and accurately as possible (releasing the button restored a focused light beam between an optic fiber led and its receiver; the response latency of this apparatus was under 1 ms). Participants were given 1000 ms to respond, after what any response was considered as a nogo response. The stimulus onset asynchrony (SOA) was 2000 ms plus or minus a random delay of 200 ms. For each distractor, participants had to keep pressing the button during at least 1000 ms (nogo response).
More specifically, in the animal categorization task, participants had to respond whenever there was an animal in the picture. In the recognition task, the session started with a learning phase. A probe image was flashed 15 times during 20 ms intermixed with two presentations of 1000 ms after the fifth and the tenth flashes, allowing an ocular exploration of the image; with an inter-stimulus of 1000 ms. Participants were instructed to carefully examine and learn the probe image in order to recognize it in the following series. The test phase started immediately after the learning phase. The probe image constituted the unique target of the series. Both tasks were organized in series of 100 images; 50 targets images were mixed with 50 non-targets in the animal categorization task; 50 copies of an unique photographs were mixed at random with 50 non-targets in the recognition task.
Cohort#
Dataset Statistics#
Sex composition
Channel counts: 31 ch (n=350 recordings)
Sampling frequencies: 1000.0 Hz (n=350 recordings)
Total recording duration: 20 h 48 min
Signal · Electrodes & live trace#
Live trace viewer — sub-010 · ses-02 · task-gonogo · run-4
Showing one representative recording out of
14 subjects and 350 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 31 sensors — 31 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Go-nogo categorization and detection task |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Arnaud Delorme |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds002680,
title = {Go-nogo categorization and detection task},
author = {Arnaud Delorme},
doi = {10.18112/openneuro.ds002680.v1.2.0},
url = {https://doi.org/10.18112/openneuro.ds002680.v1.2.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS002680 · Delorme2020_Go_nogo_categorizationeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS002680(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Go-nogo categorization and detection task
- Study:
ds002680(OpenNeuro)- Author (year):
Delorme2020_Go_nogo_categorization- Canonical:
—
Also importable as:
DS002680,Delorme2020_Go_nogo_categorization.Modality:
eeg. Subjects: 14; recordings: 350; tasks: 1.- Parameters:
cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key
dataset.s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to
EEGDashDataset.
- data_dir#
Local dataset cache directory (
cache_dir / dataset_id).- Type:
Path
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds002680 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002680 DOI: https://doi.org/10.18112/openneuro.ds002680.v1.2.0 NEMAR citation count: 5
Examples
>>> from eegdash.dataset import DS002680 >>> dataset = DS002680(cache_dir="./data") >>> recording = dataset[0] >>> raw = recording.load()
- __init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
- save(path: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds002680").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds002680 to reproduce the tutorial on this dataset.
Citation
Arnaud Delorme (20). Go-nogo categorization and detection task. 10.18112/openneuro.ds002680.v1.2.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds002680.v1.2.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset