DS003775#

SRM Resting-state EEG

Access recordings and metadata through EEGDash.

Citation: Christoffer Hatlestad-Hall, Trine Waage Rygvold, Stein Andersson (2021). SRM Resting-state EEG. 10.18112/openneuro.ds003775.v1.2.1

Modality: eeg Subjects: 111 Recordings: 153 License: CC0 Source: openneuro Citations: 8.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003775

dataset = DS003775(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 = DS003775(cache_dir="./data", subject="01")

Advanced query

dataset = DS003775(
    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{ds003775,
  title = {SRM Resting-state EEG},
  author = {Christoffer Hatlestad-Hall and Trine Waage Rygvold and Stein Andersson},
  doi = {10.18112/openneuro.ds003775.v1.2.1},
  url = {https://doi.org/10.18112/openneuro.ds003775.v1.2.1},
}

About This Dataset#

SRM Resting-state EEG

Introduction

This EEG dataset contains resting-state EEG extracted from the experimental paradigm used in the Stimulus-Selective Response Modulation (SRM) project at the Dept. of Psychology, University of Oslo, Norway. The data is recorded with a BioSemi ActiveTwo system, using 64 electrodes

View full README

SRM Resting-state EEG

Introduction

This EEG dataset contains resting-state EEG extracted from the experimental paradigm used in the Stimulus-Selective Response Modulation (SRM) project at the Dept. of Psychology, University of Oslo, Norway. The data is recorded with a BioSemi ActiveTwo system, using 64 electrodes following the positional scheme of the extended 10-20 system (10-10). Each datafile comprises four minutes of uninterrupted EEG acquired while the subjects were resting with their eyes closed. The dataset includes EEG from 111 healthy control subjects (the “t1” session), of which a number underwent an additional EEG recording at a later date (the “t2” session). Thus, some subjects have one associated EEG file, whereas others have two.

Disclaimer

The dataset is provided “as is”. Hereunder, the authors take no responsibility with regard to data quality. The user is solely responsible for ascertaining that the data used for publications or in other contexts fulfil the required quality criteria.

The data

Raw data files

The raw EEG data signals are rereferenced to the average reference. Other than that, no operations have been performed on the data. The files contain no events; the whole continuous segment is resting-state data. The data signals are unfiltered (recorded in Europe, the line noise frequency is 50 Hz). The time points for the subject’s EEG recording(s), are listed in the *_scans.tsv file (particularly interesting for the subjects with two recordings). Please note that the quality of the raw data has not been carefully assessed. While most data files are of high quality, a few might be of poorer quality. The data files are provided “as is”, and it is the user’s esponsibility to ascertain the quality of the individual data file.

/derivatives/cleaned_data

For convenience, a cleaned dataset is provided. The files in this derived dataset have been preprocessed with a basic, fully automated pipeline (see /code/s2_preprocess.m for details) directory for details. The derived files are stored as EEGLAB .set files in a directory structure identical to that of the raw files. Please note that the *_channels.tsv files associated with the derived files have been updated with status information about each channel (“good” or “bad”). The “bad” channels are – for the sake of consistency – interpolated, and thus still present in the data. It might be advisable to remove these channels in some analyses, as they (per definition) do not provide anything to the EEG data. The cleaned data signals are referenced to the average reference (including the interpolated channels). Please mind the automatic nature of the employed pipeline. It might not perform optimally on all data files (e.g. over-/underestimating proportion of bad channels). For publications, we recommend implementing a more sensitive cleaning pipeline.

Demographic and cognitive test data

The participants.tsv file in the root folder contains the variables age, sex, and a range of cognitive test scores. See the sidecar participants.json for more information on the behavioural measures. Please note that these measures were collected in connection with the “t1” session recording.

How to cite

All use of this dataset in a publication context requires the following paper to be cited: Hatlestad-Hall, C., Rygvold, T. W., & Andersson, S. (2022). BIDS-structured resting-state electroencephalography (EEG) data extracted from an experimental paradigm. Data in Brief, 45, 108647. https://doi.org/10.1016/j.dib.2022.108647

Contact

Questions regarding the EEG data may be addressed to Christoffer Hatlestad-Hall (chr.hh@pm.me). Question regarding the project in general may be addressed to Stein Andersson (stein.andersson@psykologi.uio.no) or Trine W. Rygvold (t.w.rygvold@psykologi.uio.no).

Dataset Information#

Dataset ID	`DS003775`
Title	SRM Resting-state EEG
Year	2021
Authors	Christoffer Hatlestad-Hall, Trine Waage Rygvold, Stein Andersson
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003775.v1.2.1
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: 111
Recordings: 153
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 1024.0
Duration (hours): 0.0

API Reference#

Use the DS003775 class to access this dataset programmatically.

class eegdash.dataset.DS003775(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003775. Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 111; recordings: 153; 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/ds003775 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003775

Examples

>>> from eegdash.dataset import DS003775
>>> dataset = DS003775(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