DS003775: eeg dataset, 111 subjects#
SRM Resting-state EEG
Citation: Christoffer Hatlestad-Hall, Trine Waage Rygvold, Stein Andersson (2022). SRM Resting-state EEG. 10.18112/openneuro.ds003775.v1.2.1
111-participant EEG dataset — SRM Resting-state EEG.
Quickstart#
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)
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'])
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#
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).
SRM Resting-state EEG
Introduction
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.
View full README
SRM Resting-state EEG
Introduction
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).
Cohort#
Dataset Statistics#
Age distribution by gender (n=111, range 17–71 yr, mean 37.6 yr)
Sex composition
Channel counts: 64 ch (n=153 recordings)
Sampling frequencies: 1024.0 Hz (n=153 recordings)
Total recording duration: 10 h 12 min
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · ses-t2 · task-resteyesc
Showing one representative recording out of
111 subjects and 153 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 64 sensors — 64 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
SRM Resting-state EEG |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2022 |
Authors |
Christoffer Hatlestad-Hall, Trine Waage Rygvold, Stein Andersson |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003775 · HatlestadHall2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003775(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
SRM Resting-state EEG
- Study:
ds003775(OpenNeuro)- Author (year):
HatlestadHall2021- Canonical:
—
Also importable as:
DS003775,HatlestadHall2021.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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds003775.v1.2.1 NEMAR citation count: 8
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: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds003775").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds003775 to reproduce the tutorial on this dataset.
Citation
Christoffer Hatlestad-Hall, Trine Waage Rygvold, Stein Andersson (2022). SRM Resting-state EEG. 10.18112/openneuro.ds003775.v1.2.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003775.v1.2.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset