DS005960#

General Info: inst-comp-eeg

Access recordings and metadata through EEGDash.

Citation: Pena, P., Palenciano, A.F., González-García, C., Ruz, M. (2025). General Info: inst-comp-eeg. 10.18112/openneuro.ds005960.v1.0.0

Modality: eeg Subjects: 41 Recordings: 210 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.

Dataset Information#

Dataset ID	`DS005960`
Title	General Info: inst-comp-eeg
Year	2025
Authors	Pena, P., Palenciano, A.F., González-García, C., Ruz, M.
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005960.v1.0.0
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: 41
Recordings: 210
Tasks: 1

Channels & sampling rate

Channels: 63 (41), 61 (41)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS005960 class to access this dataset programmatically.

class eegdash.dataset.DS005960(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005960. 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

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/ds005960 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005960

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, 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