ON004661: eeg dataset, 17 subjects#
ANDI
Citation: Tony Johnson, Stephen Gordon, Jon Touryan, Kevin King (20). ANDI. 10.82901/nemar.on004661
17-participant EEG dataset — ANDI.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON004661
dataset = ON004661(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON004661(cache_dir="./data", subject="01")
Advanced query
dataset = ON004661(
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{on004661,
title = {ANDI},
author = {Tony Johnson and Stephen Gordon and Jon Touryan and Kevin King},
doi = {10.82901/nemar.on004661},
url = {https://doi.org/10.82901/nemar.on004661},
}
About This Dataset#
Participants (N=17, all males) with an average age of 32.8 years performed a guided visual search task in parallel with a second binaurally presented auditory task (Ries, et al., 2016). EEG data from each participant were recorded using a 64-channel BioSemi ActiveTwo system digitized at 512 Hz. Four external electrodes were used to record bipolar horizontal and vertical EOG signals, and a single external electrode was placed on each of the left and right mastoids to provide the reference signals. Fourteen participants were included in the original study, with three additional participants later added, resulting in 17 participants.
The visual search task for this experiment required participants to follow a red annulus around the screen and press a button if the annulus stopped at a prespecified target. The auditory task for this experiment was an N-back matching task in which participants listened to a string of numbers presented at approximately 2 second intervals and were required to indicate whether the current number matched a previously presented number. For the N=0, this would be the number immediately prior. For N=1 this would be the number one level before that, and so on. In the example string “1”, “1”, “2”, “1”, “3”, “2”, the second “1” should generate a match in the N=0 condition, the third “1” should generate a match in the N=1 condition, and the second “2” should generate a match in the N=2 condition. The task was composed of a baseline condition in which participants were presented with both visual and auditory stimuli but were instructed to ignore the auditory component. Next, were three dual-task conditions with N-back levels of N=0, N=1, and N=2.
Cohort#
Dataset Statistics#
Channel counts: 64 ch (n=17 recordings)
Sampling frequencies: 128.0 Hz (n=17 recordings)
Total recording duration: 10 h 8 min
Signal · Electrodes & live trace#
Live trace viewer — sub-001 · task-nback
Showing one representative recording out of
17 subjects and 17 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.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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 |
ANDI |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Tony Johnson, Stephen Gordon, Jon Touryan, Kevin King |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on004661,
title = {ANDI},
author = {Tony Johnson and Stephen Gordon and Jon Touryan and Kevin King},
doi = {10.82901/nemar.on004661},
url = {https://doi.org/10.82901/nemar.on004661},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON004661(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
ANDI
- Study:
on004661(NeMAR)- Author (year):
nan- Canonical:
—
Also importable as:
ON004661,nan.Modality:
eeg. Subjects: 17; recordings: 17; 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/on004661 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on004661 DOI: https://doi.org/10.82901/nemar.on004661
Examples
>>> from eegdash.dataset import ON004661 >>> dataset = ON004661(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.pytorch
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 on004661 to reproduce the tutorial on this dataset.
Citation
Tony Johnson, Stephen Gordon, Jon Touryan, Kevin King (20). ANDI. 10.82901/nemar.on004661
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on004661.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset