EEGdash›OpenNeuro›DS007609

Iss. 7609 · 51 subjects · 51 recordings · CC0

Dataset Brief · Resting-State EEG and Trait Anxiety

DS007609: eeg dataset, 51 subjects#

Resting-State EEG and Trait Anxiety

Citation: Tamari Shalamberidze, Kyle Nash, Jeremy B. Caplan (2025). Resting-State EEG and Trait Anxiety. 10.18112/openneuro.ds007609.v1.0.0

51-participant EEG dataset — Resting-State EEG and Trait Anxiety.

Data & curation Tamari Shalamberidze · Kyle Nash · Jeremy B. Caplan
Year 2025 · Distributed via OpenNeuro
Funding Social Sciences and Humanities Research Council of Canada (SSHRC) · Natural Sciences and Engineering Research Council of Canada (NSERC)

EEG · 256 ch500 HzBIDS 1.9.0Task · restHealthyResting StateAffect

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007609

dataset = DS007609(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 = DS007609(cache_dir="./data", subject="01")

Advanced query

dataset = DS007609(
    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{ds007609,
  title = {Resting-State EEG and Trait Anxiety},
  author = {Tamari Shalamberidze and Kyle Nash and Jeremy B. Caplan},
  doi = {10.18112/openneuro.ds007609.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007609.v1.0.0},
}

§ 02Study · The README

About This Dataset#

This dataset contains resting-state EEG recordings from 51 participants,

collected as part of a study examining the relationship between resting-state EEG alpha/theta power, oscillatory dynamics, and trait anxiety.

51 right-handed undergraduate students (25 female) from the University of

Alberta, aged 17-51 years (mean = 20.4, SD = 4.9), participated for course credit.

Resting-State EEG and Trait Anxiety

Authors

Tamari Shalamberidze (a), Kyle Nash (a,b), Jeremy B. Caplan (a,b) (a) Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada (b) Department of Psychology, University of Alberta, Edmonton, AB, Canada Corresponding Author: Tamari Shalamberidze (shalambe@ualberta.ca)

Related Publication

View full README

Resting-State EEG and Trait Anxiety

Authors

Tamari Shalamberidze (a), Kyle Nash (a,b), Jeremy B. Caplan (a,b) (a) Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada (b) Department of Psychology, University of Alberta, Edmonton, AB, Canada Corresponding Author: Tamari Shalamberidze (shalambe@ualberta.ca)

Related Publication

Shalamberidze, T., Nash, K., & Caplan, J.B. (2025). Resting-state EEG and trait anxiety. Imaging Neuroscience. https://doi.org/10.1162/IMAG.a.44

Recording

EEG was recorded using a 256-channel EGI HydroCel Geodesic Sensor Net with Net Amps amplifier. The original sampling rate was 500 Hz.

Online reference was Cz.

Paradigm

Participants completed a resting-state protocol consisting of alternating 1-minute eyes-open (EO) and 1-minute eyes-closed (EC) blocks, repeated twice (EO-EC-EO-EC), for a total of 4 minutes. Transitions between blocks were signaled by an auditory beep.

Preprocessing

Data were preprocessed in EEGLAB (MATLAB) with the following steps: - Bandpass filter: 0.1-50 Hz - Line noise removal: CleanLine at 60 Hz and 120 Hz - Channel rejection: kurtosis-based (2x threshold), applied twice - Re-referencing to the average - ICA decomposition (runica, extended) - Artifact component removal via ICLabel (>0.8 probability threshold) + visual inspection - Spherical interpolation of removed channels

Phenotype Data

The phenotype/ directory contains anxiety and personality questionnaire scores: - STAI: State-Trait Anxiety Inventory (Spielberger et al., 1983) - TIPI: Ten-Item Personality Inventory, emotional stability subscale (Gosling et al., 2003) - BIS/FFFS: Behavioural Inhibition Scale and Fight-Flight-Freeze System

from the RST-PQ (Corr & Cooper, 2016), with Heym and Jackson factor structures. BIS data are unavailable for the first 5 participants.

Ethics

This study received ethics approval from the University of Alberta Research Ethics Board. Project Name: “Physiological Bases of Human Memory”, No. Pro00113334.

Funding

Partly supported by the Social Sciences and Humanities Research Council in Canada (SSHRC), and the Natural Sciences and Engineering Research Council of Canada (NSERC).

License

This dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=51, range 17–51 yr, mean 20.4 yr)

1520253050

Female · 25Male · 26

Sex composition

Female
25
Male
26
F : M ratio
0.96 : 1

49% female · n = 51 subjects with reported sex.

Channel counts: 256 ch (n=51 recordings)

Sampling frequencies: 500.0 Hz (n=51 recordings)

Total recording duration: 4 h 3 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 256 ch · EEG · 500 Hz · 51 subjects, 51 recordings

Live trace viewer — sub-1022 · task-rest

Showing one representative recording out of 51 subjects and 51 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 · 256 sensors — 256 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS007609(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Shalamberidze2026

Canonical—

Importable asDS007609 · Shalamberidze2026

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.DS007609(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Resting-State EEG and Trait Anxiety

Study:: ds007609 (OpenNeuro)
Author (year):: Shalamberidze2026
Canonical:: —

Also importable as: DS007609, Shalamberidze2026.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 51; recordings: 51; 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/ds007609 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007609 DOI: https://doi.org/10.18112/openneuro.ds007609.v1.0.0

Examples

>>> from eegdash.dataset import DS007609
>>> dataset = DS007609(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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS007609.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 ds007609 to reproduce the tutorial on this dataset.

Citation

Tamari Shalamberidze, Kyle Nash, Jeremy B. Caplan (2025). Resting-State EEG and Trait Anxiety. 10.18112/openneuro.ds007609.v1.0.0

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds007609.v1.0.0.

Related & sibling datasets

DS005420EEG · 37 subj DS006801EEG · 21 subj DS007176EEG · 45 subj DS003775EEG · 111 subj DS006648EEG · 47 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds007609.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007609`
Title	Resting-State EEG and Trait Anxiety
Author (year)	`Shalamberidze2026`
Canonical	—
Importable as	`DS007609`, `Shalamberidze2026`
Year	2025
Authors	Tamari Shalamberidze, Kyle Nash, Jeremy B. Caplan
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007609.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS007609: eeg dataset, 51 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#