DS007605: eeg dataset, 97 subjects#

EPOC

Access recordings and metadata through EEGDash.

Citation: [“Christian Neumann”, “Johanna Geritz”, “Julian Keil”, “Walter Maetzler”, “Julius Welzel”] (2026). EPOC. 10.18112/openneuro.ds007605.v1.0.0

Modality: eeg Subjects: 97 Recordings: 97 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007605

dataset = DS007605(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 = DS007605(cache_dir="./data", subject="01")

Advanced query

dataset = DS007605(
    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{ds007605,
  title = {EPOC},
  author = {["Christian Neumann" and "Johanna Geritz" and "Julian Keil" and "Walter Maetzler" and "Julius Welzel"]},
  doi = {10.18112/openneuro.ds007605.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007605.v1.0.0},
}

About This Dataset#

EPOC

July 2023 - July 2025

Project discription

A project to investigate neurophysiological correlates of Post-COVID-19 by the means of EEG.

Contact Person

View full README

EPOC

July 2023 - July 2025

Project discription

A project to investigate neurophysiological correlates of Post-COVID-19 by the means of EEG.

Contact Person

M. Sc. Christian Neumann Neurology Department University Medical Center Schleswig-Holstein, Campus Kiel Arnold-Heller-Straße 3 24105 Kiel Germany E-Mail: neumann@psychologie.uni-kiel.de

Supervisors

* Prof. Dr. med. Walter Maetzler * Dr. Julius Welzel

##

Overview

Psychomotor vigilance Task (PVT)

* The PVT is a simple reaction-time task that is widely used in research on sleep and fatigue (Basner & Dinges, 2011; Jung et al., 2011; Lee et al., 2010). Each trial began with a white fixation cross displayed for 2 - 10 seconds, after which a red number appeared, counting the milliseconds elapsed since its onset (Figure 2). Participants were instructed to press the spacebar as quickly as possible once the number appeared. Upon response, the counter stopped and displayed the individual’s reaction as feedback (for 500 ms), after which the fixation cross reappeared, to initiate the next trial.

Independent variables

* Participants were divided into two groups based on whether self-reported cognitive symptoms were present or absent, with a group with self-reported cognitive symptoms and a group without self-reported cognitive symptoms.

Dependent variables

* Fatigue was assessed using the Functional Assessment of Chronic Fatigue Illness Therapy – Fatigue Subscale (FACIT-Fatigue subscale) questionnaire (Yellen et al., 1997), which was also applied in COVIDOM (Hartung et al., 2024). The items in the questionnaire refer to the past 7 days and therefore capture fatigue more as a trait than a state. A score ≤ 30 is conventionally considered indicative of clinically relevant fatigue (Piper & Cella, 2010). Anxiety and depression were assessed with the Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983), a self-report instrument developed for use in somatic settings that deliberately excluded symptoms that also occur in physical disorders (e.g., fatigue, headaches). It can be separated into an anxiety subscore (HADS-A) and a depression subscore (HADS-D). In the present study, the German translation by Herrmann & Buss (1994) was used. Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989).

control variables

* Age, Gender, years of education, presence of pre-diagnosed neurological disorders.

Methods

Subjects

* Participants (N = 98) were recruited as a subcohort from the COVIDOM/NAPKON-POP study. COVIDOM is a longitudinal, multicenter study conducted within the National Pandemic Cohort Network (NAPKON), with study sites in Berlin, Kiel, and Wuerzburg (Horn et al., 2021). All participants in this study were at least 18 years old, residents of Schleswig-Holstein, and had a confirmed SARS-CoV-2 infection in the past, diagnosed by PCR test.

Study procedures

* Participants completed a single study visit that was conducted in the laboratory of the Department of Neurology at the University Hospital Schleswig-Holstein in Kiel. First, they completed the paper-and-pencil Trail Making Test (TMT; Reitan, 1958). Subsequently, EEG preparation and recording were conducted; electrode setup required approximately one hour. Once the EEG was prepared, participants went through a battery of four computerized cognitive tasks and a resting-state measurement. After the session, participants were asked to fill out three questionnaires assessing fatigue, depressive symptoms, anxiety symptoms, and sleep quality.

EEG recording

* EEG was recorded using a 128-channel cap with equidistant electrode layout (128Ch Standard Brain Cap for actiCHamp Plus, Easycap GmbH, Wörthsee, Germany) and an actiCHamp Plus Amplifier (Brain Products GmbH, Gilching, Germany). Sampling rate was 1000 Hz with an amplitude resolution of 0.1 μV. Electrode impedances were kept below 20 kΩ. The reference electrode was placed on the nose, and two electrooculogram (EOG) electrodes were positioned diagonally beneath each eye.

Missing data

* Data were missing (with overlap) for n-back (n = 4), PVT (n = 1), MoCA (n = 1), FACIT-Fatigue subscale (n = 2), HADS-D total score (n = 3), HADS-A total score (n = 1), PSQI total score (n = 6) and EEG recordings (n = 3), which occurred when participants were unable to complete a test due to time and health constraints, when no button responses were recorded, when essential questionnaire items were not answered clearly, or when files were corrupted. All behavioral outcomes were within expected range, and no outliers were removed.

Dataset Information#

Dataset ID	`DS007605`
Title	EPOC
Author (year)	—
Canonical	—
Importable as	`DS007605`
Year	2026
Authors	[“Christian Neumann”, “Johanna Geritz”, “Julian Keil”, “Walter Maetzler”, “Julius Welzel”]
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007605.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: 97
Recordings: 97
Tasks: 1

Channels & sampling rate

Channels: 128
Sampling rate (Hz): 1000.0
Duration (hours): 12.611014166666664

API Reference#

Use the DS007605 class to access this dataset programmatically.

class eegdash.dataset.DS007605(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

EPOC

Study:: ds007605 (OpenNeuro)
Author (year):: nan
Canonical:: —

Also importable as: DS007605, nan.

Modality: eeg. Subjects: 97; recordings: 97; 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/ds007605 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007605 DOI: https://doi.org/10.18112/openneuro.ds007605.v1.0.0

Examples

>>> from eegdash.dataset import DS007605
>>> dataset = DS007605(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