DS003987#

EEG: Amphetamine trials 5CCPT and Probabilistic Learning

Access recordings and metadata through EEGDash.

Citation: James F Cavanagh, Greg Light, Neal Swerdlow, Jonathan Brigman, Jared Young (2022). EEG: Amphetamine trials 5CCPT and Probabilistic Learning. 10.18112/openneuro.ds003987.v1.0.0

Modality: eeg Subjects: 23 Recordings: 557 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003987

dataset = DS003987(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 = DS003987(cache_dir="./data", subject="01")

Advanced query

dataset = DS003987(
    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{ds003987,
  title = {EEG: Amphetamine trials 5CCPT and Probabilistic Learning},
  author = {James F Cavanagh and Greg Light and Neal Swerdlow and Jonathan Brigman and Jared Young},
  doi = {10.18112/openneuro.ds003987.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds003987.v1.0.0},
}

About This Dataset#

Two different tasks. From: “Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms” Second phase (UH3 phase): Amphetamine trials. Data for both Bhakta et al. 5CCPT paper (humans only) and Cavanagh et al. PLT paper (humans and mice). N=23 humans. 3 drug conditions: placebo, 10mg, 20mg. N=28 mice in code folder. 4 drug condis: placebo, 0.1, 0.3, 1.0 mg/kg. EEG Triggers were odd binary recombinations that were re-translated into 0-255 in Matlab. See .m scripts and Trigger Translator.xls *********OK! LISTEN! The .bdf files were to big to import using this function. So I imported them in EEGLab, downsampled to 500 Hz, then saved them as .set files. THEN I ran the import script on these .set files. So you do not need to re-downsample in STEP1 if you run anything from the code folder. ********* Data collected circa 2016-2019 in San Diego. Data analyzed circa 2017-2021 in New Mexico. - James F Cavanagh 06/16/2021

Dataset Information#

Dataset ID	`DS003987`
Title	EEG: Amphetamine trials 5CCPT and Probabilistic Learning
Year	2022
Authors	James F Cavanagh, Greg Light, Neal Swerdlow, Jonathan Brigman, Jared Young
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003987.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: 23
Recordings: 557
Tasks: 1

Channels & sampling rate

Channels: 64 (69), 71 (69)
Sampling rate (Hz): 500.0930232558139
Duration (hours): 0.0

API Reference#

Use the DS003987 class to access this dataset programmatically.

class eegdash.dataset.DS003987(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003987. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 23; recordings: 69; 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/ds003987 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003987

Examples

>>> from eegdash.dataset import DS003987
>>> dataset = DS003987(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