DS004661#

ANDI

Access recordings and metadata through EEGDash.

Citation: Tony Johnson, Stephen Gordon, Jon Touryan, Kevin King (2023). ANDI. 10.18112/openneuro.ds004661.v1.1.0

Modality: eeg Subjects: 17 Recordings: 90 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004661

dataset = DS004661(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 = DS004661(cache_dir="./data", subject="01")

Advanced query

dataset = DS004661(
    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{ds004661,
  title = {ANDI},
  author = {Tony Johnson and Stephen Gordon and Jon Touryan and Kevin King},
  doi = {10.18112/openneuro.ds004661.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds004661.v1.1.0},
}

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.

Dataset Information#

Dataset ID	`DS004661`
Title	ANDI
Year	2023
Authors	Tony Johnson, Stephen Gordon, Jon Touryan, Kevin King
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004661.v1.1.0
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: 17
Recordings: 90
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 128.0
Duration (hours): 0.0

API Reference#

Use the DS004661 class to access this dataset programmatically.

class eegdash.dataset.DS004661(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004661. Modality: eeg; Experiment type: Memory. 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

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/ds004661 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004661

Examples

>>> from eegdash.dataset import DS004661
>>> dataset = DS004661(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