DS005960: eeg dataset, 41 subjects#
General Info: inst-comp-eeg
Citation: Pena, P., Palenciano, A.F., González-García, C., Ruz, M. (—). General Info: inst-comp-eeg. 10.18112/openneuro.ds005960.v1.0.0
41-participant EEG dataset — General Info: inst-comp-eeg.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005960
dataset = DS005960(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005960(cache_dir="./data", subject="01")
Advanced query
dataset = DS005960(
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{ds005960,
title = {General Info: inst-comp-eeg},
author = {Pena, P. and Palenciano, A.F. and González-García, C. and Ruz, M.},
doi = {10.18112/openneuro.ds005960.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005960.v1.0.0},
}
About This Dataset#
The experiment consisted of two tasks: the main instruction-following task and an additional localizer task. The data of each participant was recorded in one session.
For the main instruction-following task, participants saw four sequential screens -screen display of 200 ms and 800 ms as interscreen interval- that contained the full instruction, after a pretarget interval, they were presented with the target images -two images framed by a colored shape, on display for 200 ms-.
They had to respond if the instruction was fulfilled or not by the targets.
The first two screens of the instruction indicated if the participant had to pay attention to both images -integration- or to just one -selection-, and which specific images were set to appear -animate or inanimate images per trial-. The third instruction refered to the relevant feature they had to pay attention to, either the color or the shape surrounding the image.
The last instruction indicated the key to press if the instruction was fulfilled by the target images -either “A” or “L”-. Each trial consisted of a novel combination of the instruction components.
Additional catch trials were added, to ensure that participants were maintaining all information. If any of the target images was different from the ones previously instructed, the participant had to indicate it by pressing both “A” and “L” simultaneously.
The localizer task was a 1-back task. Participants saw one target image per trial, and they had to indicate with a keypress -“A” and “L”- if the image was from the same subcategory as the image from the previous trial. Each block of the main instruction-following task consisted of 32 trials, with a total of 16 blocks.
All the conditions were fully counterbalanced to ensure no statistical dependencies within the blocks. Each of the 8 localizer blocks consisted of 40 trials.
To counterbalance the presentation of the blocks for the whole experiment session, the blocks of the main task were further divided according to the features -blocks of features 1 and blocks of features 2-,and then the sequence of main task and localizer blocks was counterbalanced.
Cohort#
Dataset Statistics#
Age distribution by gender (n=41, range 18–27 yr, mean 21.9 yr)
Sex composition
Channel counts: 63 ch (n=41 recordings)
Sampling frequencies: 1000.0 Hz (n=41 recordings)
Total recording duration: 66 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-INSTCOMP
Showing one representative recording out of
41 subjects and 41 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 · 61 sensors — 61 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 |
General Info: inst-comp-eeg |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Pena, P., Palenciano, A.F., González-García, C., Ruz, M. |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005960,
title = {General Info: inst-comp-eeg},
author = {Pena, P. and Palenciano, A.F. and González-García, C. and Ruz, M.},
doi = {10.18112/openneuro.ds005960.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005960.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005960 · Pena2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005960(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
General Info: inst-comp-eeg
- Study:
ds005960(OpenNeuro)- Author (year):
Pena2025- Canonical:
—
Also importable as:
DS005960,Pena2025.Modality:
eeg; Experiment type:Attention; Subject type:Healthy. Subjects: 41; recordings: 41; 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/ds005960 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005960 DOI: https://doi.org/10.18112/openneuro.ds005960.v1.0.0
Examples
>>> from eegdash.dataset import DS005960 >>> dataset = DS005960(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/ds005960").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 ds005960 to reproduce the tutorial on this dataset.
Citation
Pena, P., Palenciano, A.F., González-García, C., Ruz, M. (n.d.). General Info: inst-comp-eeg. 10.18112/openneuro.ds005960.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005960.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset