DS004350: eeg dataset, 24 subjects#
Executive Functionning Study for Assessing the Effect of Neurofeedback
Citation: Arnaud Delorme, Tracy Brandmeyer (20). Executive Functionning Study for Assessing the Effect of Neurofeedback. 10.18112/openneuro.ds004350.v2.0.0
24-participant EEG dataset — Executive Functionning Study for Assessing the Effect of Neurofeedback.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004350
dataset = DS004350(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004350(cache_dir="./data", subject="01")
Advanced query
dataset = DS004350(
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{ds004350,
title = {Executive Functionning Study for Assessing the Effect of Neurofeedback},
author = {Arnaud Delorme and Tracy Brandmeyer},
doi = {10.18112/openneuro.ds004350.v2.0.0},
url = {https://doi.org/10.18112/openneuro.ds004350.v2.0.0},
}
About This Dataset#
The data of this dataset was collected as part of an executive functioning battery consisting of three separate tasks:
N-Back (NB)
Sustained Attention to Response Task (SART)
Local Global (LG)
The original experiment details in which these tasks were conducted in addition to can be read about here (https://doi.org/10.3389/fnhum.2020.00246).
Experiment Design: Two sessions of each task were conducted on the first and last day of the neurofeedback experiment with 24 participants (mentioned above). [N-Back (NB)] Participants performed a visual sequential letter n-back working memory task, with memory load ranging from 1-back to 3-back. The visual stimuli consisted of a sequence of 4 letters (A, B, C, D) presented black on a gray background. Participants observed stimuli on a visual display and responded using the spacebar on a provided keyboard. In the 1-back condition, the target was any letter identical to the trial immediately preceding one. In the 2-back and 3-back conditions, the target was any letter that was presented two or three trials back, respectively. The stimuli were presented on a screen for a duration of 1 s, after which a fixation cross was presented for 500 ms. Participants responded to each stimulus by pressing the spacebar with their right hand upon target presentation. If no spacebar was pressed within 1500 ms of the stimulus presentation, a new stimulus was presented. Each n-back condition (1, 2, and 3-back) consisted of the presentation of 280 stimuli selected randomly in the 4-letter pool. [Sustained Attention to Response Task (SART)] Participants were presented with a series of single numerical digits (randomly selected from 0 to 9 - the same digit could not be presented twice in a row) and instructed to press the spacebar for each digit, except for when presented with the digit 3. Each number was presented for 400 ms in white on a gray background. The inter-stimulus interval was 2 s irrespective of the button press and a fixation cross was present at all times except for when the digits were presented. Participants performed the SART for approximately 10 minutes corresponding to 250 digit presentations. [Local Global (LG)] Participants were shown large letters (H and T) on a computer screen. The large letters were made up of an aggregate of smaller letters that could be congruent (i.e large H made of small Hs or large T made of small Ts) or incongruent (large H made of small Ts or large T made of small Hs) with respect to the large letter. The small letters were 0.8 cm high and the large letters were 8 cm high on the computer screen. A fixation cross was present at all times except when the stimulus letters were presented. Letters were shown on the computer screen until the subject responded. After each subject’s response, there was a delay of 1 s before the next stimulus was presented. Before each sequence of letters, instructions were shown on a computer screen indicating to participants whether they should respond to the presence of small (local condition) or large (global condition) letters. The participants were instructed to categorize specifically large letters or small letters and to press the letter H or T on the computer keyboard to indicate their choice. Data Processing: Data processing was performed in Matlab and EEGLAB. The EEG data was average referenced and down-sampled from 2048 to 256 Hz. A high-pass filter at 1 HZ using an elliptical non-linear filter was applied and the data was then average referenced. Note: The data files in this dataset were converted into the .set format for EEGLAB. The .bdf files that were converted for each of the tasks can be found in the sourcedata folder. Exclusion Note: The second run of NB in session 1 of sub-11 and the run of SART in session 1 of sub-18 were both excluded due to issues with conversion to .set format. However, the .bdf files of these runs can be found in the sourcedata folder.
Executive Functioning Tasks
Cohort#
Dataset Statistics#
Channel counts: 64 ch (n=240 recordings)
Sampling frequencies: 256.0 Hz (n=240 recordings)
Total recording duration: 41 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · ses-pre · task-LG
Showing one representative recording out of
24 subjects and 240 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 · 64 sensors — 64 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 |
Executive Functionning Study for Assessing the Effect of Neurofeedback |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Arnaud Delorme, Tracy Brandmeyer |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004350,
title = {Executive Functionning Study for Assessing the Effect of Neurofeedback},
author = {Arnaud Delorme and Tracy Brandmeyer},
doi = {10.18112/openneuro.ds004350.v2.0.0},
url = {https://doi.org/10.18112/openneuro.ds004350.v2.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004350 · Delorme2022eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004350(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Executive Functionning Study for Assessing the Effect of Neurofeedback
- Study:
ds004350(OpenNeuro)- Author (year):
Delorme2022- Canonical:
—
Also importable as:
DS004350,Delorme2022.Modality:
eeg. Subjects: 24; recordings: 240; tasks: 5.- 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/ds004350 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004350 DOI: https://doi.org/10.18112/openneuro.ds004350.v2.0.0 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS004350 >>> dataset = DS004350(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/ds004350").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 ds004350 to reproduce the tutorial on this dataset.
Citation
Arnaud Delorme, Tracy Brandmeyer (20). Executive Functionning Study for Assessing the Effect of Neurofeedback. 10.18112/openneuro.ds004350.v2.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004350.v2.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset