DS004350#

Executive Functionning Study for Assessing the Effect of Neurofeedback

Access recordings and metadata through EEGDash.

Citation: Arnaud Delorme, Tracy Brandmeyer (2022). Executive Functionning Study for Assessing the Effect of Neurofeedback. 10.18112/openneuro.ds004350.v2.0.0

Modality: eeg Subjects: 24 Recordings: 1062 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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#

Executive Functioning Tasks

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.

Dataset Information#

Dataset ID	`DS004350`
Title	Executive Functionning Study for Assessing the Effect of Neurofeedback
Year	2022
Authors	Arnaud Delorme, Tracy Brandmeyer
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004350.v2.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 24
Recordings: 1062
Tasks: 5

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 256.0
Duration (hours): 0.0

API Reference#

Use the DS004350 class to access this dataset programmatically.

class eegdash.dataset.DS004350(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004350. Modality: eeg; Experiment type: Memory; Subject type: Healthy. 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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None