EEGdash›OpenNeuro›DS006036
Iss. 6036 · 88 subjects · 88 recordings · CC0
Dataset Brief · A complementary dataset of open-eyes EEG recordings in a phot…

DS006036: eeg dataset, 88 subjects#

A complementary dataset of open-eyes EEG recordings in a photo-stimulation setting from: Alzheimer’s disease, Frontotemporal dementia and Healthy subjects

Citation: Aimilia Ntetska, Andreas Miltiadous, Alexandros T. Tzallas, Katerina D. Tzimourta, Theodora Afrantou, Panagiotis Ioannidis, Dimitrios G. Tsalikakis, Nikolaos Grigoriadis, Pantelis Angelidis, Konstantinos Sakkas, Emmanouil D. Oikonomou, Nikolaos Giannakeas, Markos G. Tsipouras (—). A complementary dataset of open-eyes EEG recordings in a photo-stimulation setting from: Alzheimer’s disease, Frontotemporal dementia and Healthy subjects. 10.18112/openneuro.ds006036.v1.0.6

88-participant EEG dataset — A complementary dataset of open-eyes EEG recordings in a photo-stimulation setting from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects.

Data & curation Aimilia Ntetska · Andreas Miltiadous · Alexandros T. Tzallas · Katerina D. Tzimourta · Theodora Afrantou · Panagiotis Ioannidis · …
EEG · 19 ch500 HzBIDS 1.8.0Task · photomarkDementiaVisualClinical/Intervention
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006036

dataset = DS006036(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006036(cache_dir="./data", subject="01")

Advanced query

dataset = DS006036(
    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{ds006036,
  title = {A complementary dataset of open-eyes EEG recordings in a photo-stimulation setting from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects},
  author = {Aimilia Ntetska and Andreas Miltiadous and Alexandros T. Tzallas and Katerina D. Tzimourta and Theodora Afrantou and Panagiotis Ioannidis and Dimitrios G. Tsalikakis and Nikolaos Grigoriadis and Pantelis Angelidis and Konstantinos Sakkas and Emmanouil D. Oikonomou and Nikolaos Giannakeas and Markos G. Tsipouras},
  doi = {10.18112/openneuro.ds006036.v1.0.6},
  url = {https://doi.org/10.18112/openneuro.ds006036.v1.0.6},
}
§ 02Study · The README

About This Dataset#

This dataset provides complementary material to the previously published dataset named “A dataset of EEG recordings from: Alzheimer’s disease, Frontotemporal dementia and Healthy subjects” with doi:10.18112/openneuro.ds004504.v1.0.8.

It is consisted of eyes-open EEG recordings in multiple photic stimulation settings, according to the clinical protocol of the 2nd department of Neurology, AHEPA University of Thessaloniki, Greece. The participant numbers match the respective participant numbers of the aforementioned dataset. In the clinical protocol, the 1st datasets recordings came first, followed by the recordings of this dataset. The dataset is designed to complement a previously published dataset in which the same cohort underwent EEG recordings with their eyes closed. During the recordings, participants were seated with their eyes open while being exposed to photic stimulation. The stimulation was administered at incremental frequencies, beginning at 5 Hz, progressing to 10 Hz, 15 Hz, and in some cases, extending up to 30 Hz, with increments of 5 Hz at each level.

This study compared cognitive function in 36 individuals with Alzheimer’s disease (AD), 23 with Frontotemporal Dementia (FTD), and 29 healthy controls (CN). Cognitive function was measured using the Mini-Mental State Examination (MMSE), where lower scores indicate greater cognitive impairment. The AD group had an average MMSE score of 17.75 (standard deviation of 4.5), the FTD group averaged 22.17 (standard deviation of 8.22), and the CN group scored 30. The average age was 66.4 (standard deviation of 7.9) for the AD group, 63.6 (standard deviation of 8.2) for the FTD group, and 67.9 (standard deviation of 5.4) for the CN group. The median disease duration was 25 months, with an interquartile range of 24 to 28.5 months. Notably, the AD group had no reported dementia-related comorbidities.

Recordings: Recordings were aquired from the 2nd Department of Neurology of AHEPA General Hospital of Thessaloniki by an experienced team of neurologists. For recording, a Nihon Kohden EEG 2100 clinical device was used, with 19 scalp electrodes (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2) according to the 10-20 international system and 2 additional ectrodes (A1 and A2) placed on the mastoids for impendance check, according to the manual of the device. Each recording was performed according to the clinical protocol with participants being in a sitting position having their eyes closed. Before the initialization of each recording, the skin impedance value was ensured to be below 5k?. The sampling rate was 500 Hz with 10uV/mm resolution. The recording montages were anterior-posterior bipolar and referential montage using Cz as the common reference. The referential montage was included in this dataset. The recordings were received under the range of the following parameters of the amplifier: Sensitivity: 10uV/mm, time constant: 0.3s, and high frequency filter at 70 Hz. Each recording lasted approximately 4.86 minutes for AD group (min=1.30 minutes , max= 8.77 minutes), 4.42 minutes for FTD group (min=1.25 minutes, max=10.05 minutes) and 6.43 minutes for CN group (min=3.17 minutes, max= 9.17 minutes). In total, 174.94 minutes of AD, 101.56 minutes of FTD and 186.50 minutes of CN recordings were collected and are included in the dataset.

Preprocessing: The EEG recordings were exported in .eeg format and are transformed to BIDS accepted .set format for the inclusion in the dataset. Automatic annotations of the Nihon Kohden EEG device marking artifacts (muscle activity, blinking, swallowing) have not been included for language compatibility purposes (If this is an issue, please use the preprocessed dataset in Folder: derivatives). The unprocessed EEG recordings are included in folders named: sub-0XX. Folders named sub-0XX in the subfolder derivatives contain the preprocessed and denoised EEG recordings. The preprocessing pipeline of the EEG signals is as follows. First, a Butterworth band-pass filter 0.5-45 Hz was applied and the signals were re-referenced to A1-A2. Then, the Artifact Subspace Reconstruction routine (ASR) which is an EEG artifact correction method included in the EEGLab Matlab software was applied to the signals, removing bad data periods which exceeded the max acceptable 0.5 second window standard deviation of 15, which is considered a conservative window. Next, the Independent Component Analysis (ICA) method (RunICA algorithm) was performed, transforming the 19 EEG signals to 19 ICA components. ICA components that were classified as “eye artifacts” or “jaw artifacts” by the automatic classification routine “ICLabel” in the EEGLAB platform were automatically rejected. It should be noted that, even though the recording was performed in a resting state, eyes-closed condition, eye artifacts of eye movement were still found at some EEG recordings.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=88, range 44–79 yr, mean 66.2 yr · sex per subject not reported)

4045505560657075

Sex composition

88
subjects
Female
44
Male
44
F : M ratio
1.00 : 1
50% female · n = 88 subjects with reported sex.

Channel counts: 19 ch (n=88 recordings)

Sampling frequencies: 500.0 Hz (n=88 recordings)

Total recording duration: 7 h 43 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 19 ch · EEG · 500 Hz · 88 subjects, 88 recordings
Live trace viewer — sub-021 · task-photomark

Showing one representative recording out of 88 subjects and 88 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.

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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 — DS006036
HED event descriptors word cloud HED event descriptors word cloud — DS006036
§ 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…
Full dataset metadata table

Dataset ID

DS006036

Title

A complementary dataset of open-eyes EEG recordings in a photo-stimulation setting from: Alzheimer’s disease, Frontotemporal dementia and Healthy subjects

Author (year)

Ntetska2025

Canonical

—

Importable as

DS006036, Ntetska2025

Year

—

Authors

Aimilia Ntetska, Andreas Miltiadous, Alexandros T. Tzallas, Katerina D. Tzimourta, Theodora Afrantou, Panagiotis Ioannidis, Dimitrios G. Tsalikakis, Nikolaos Grigoriadis, Pantelis Angelidis, Konstantinos Sakkas, Emmanouil D. Oikonomou, Nikolaos Giannakeas, Markos G. Tsipouras

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006036.v1.0.6

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006036,
  title = {A complementary dataset of open-eyes EEG recordings in a photo-stimulation setting from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects},
  author = {Aimilia Ntetska and Andreas Miltiadous and Alexandros T. Tzallas and Katerina D. Tzimourta and Theodora Afrantou and Panagiotis Ioannidis and Dimitrios G. Tsalikakis and Nikolaos Grigoriadis and Pantelis Angelidis and Konstantinos Sakkas and Emmanouil D. Oikonomou and Nikolaos Giannakeas and Markos G. Tsipouras},
  doi = {10.18112/openneuro.ds006036.v1.0.6},
  url = {https://doi.org/10.18112/openneuro.ds006036.v1.0.6},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS006036(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Ntetska2025
Canonical—
Importable asDS006036 · Ntetska2025
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS006036(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

A complementary dataset of open-eyes EEG recordings in a photo-stimulation setting from: Alzheimer’s disease, Frontotemporal dementia and Healthy subjects

Study:

ds006036 (OpenNeuro)

Author (year):

Ntetska2025

Canonical:

—

Also importable as: DS006036, Ntetska2025.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Dementia. Subjects: 88; recordings: 88; 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/ds006036 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006036 DOI: https://doi.org/10.18112/openneuro.ds006036.v1.0.6

Examples

>>> from eegdash.dataset import DS006036
>>> dataset = DS006036(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 FacePre-bundled mirror at EEGDash/ds006036 · pull with datasets.load_dataset("EEGDash/ds006036").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS006036.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 ds006036 to reproduce the tutorial on this dataset.

Citation

Aimilia Ntetska, Andreas Miltiadous, Alexandros T. Tzallas, Katerina D. Tzimourta, Theodora Afrantou, … (n.d.). A complementary dataset of open-eyes EEG recordings in a photo-stimulation setting from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects. 10.18112/openneuro.ds006036.v1.0.6

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds006036.v1.0.6.

BIDS
BIDS 1.8.0
Sidecars
events · channels · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#