DS007609: eeg dataset, 51 subjects#

Resting-State EEG and Trait Anxiety

Access recordings and metadata through EEGDash.

Citation: Tamari Shalamberidze, Kyle Nash, Jeremy B. Caplan (2026). Resting-State EEG and Trait Anxiety. 10.18112/openneuro.ds007609.v1.0.0

Modality: eeg Subjects: 51 Recordings: 51 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

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)

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'])

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},
}

About This Dataset#

Resting-State EEG and Trait Anxiety

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.

Participants

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

View full README

Resting-State EEG and Trait Anxiety

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.

Participants

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.

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).

Dataset Information#

Dataset ID

DS007609

Title

Resting-State EEG and Trait Anxiety

Author (year)

Shalamberidze2026

Canonical

—

Importable as

DS007609, Shalamberidze2026

Year

2026

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
Copy-paste 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},
}

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: 51

  • Recordings: 51

  • Tasks: 1

Channels & sampling rate

  • Channels: 256

  • Sampling rate (Hz): 500.0

  • Duration (hours): 4.057284444444445

Tags

  • Pathology: Healthy

  • Modality: Resting State

  • Type: Affect

Files & format

  • Size on disk: 7.0 GB

  • File count: 51

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007609.v1.0.0

Provenance

Electrode Layout#

Electrode layout — EEG · 256 sensors — 256 channels

Dataset Statistics#

Age distribution (n=51, range 17–51 yr)

1520253050

Sex distribution

25
26
Female  Male  Total: 51

Channel counts: 256 ch (n=51 recordings)

Sampling frequencies: 500.0 Hz (n=51 recordings)

Total recording duration: 4 h 3 min

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.

NEMAR pipeline statistics — DS007609
HED event descriptors word cloud HED event descriptors word cloud — DS007609

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.

Files: —
Size: —
Subjects: —
Click to load file structure…

API Reference#

Use the DS007609 class to access this dataset programmatically.

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

Bases: EEGDashDataset

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.

See Also#