EEGdash›OpenNeuro›DS006848

Iss. 6848 · 30 subjects · 52 recordings · CC0

Dataset Brief · AlphaDirection1

DS006848: eeg dataset, 30 subjects#

AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits

Citation: Alexandra I. Kosachenko, Danil I. Syttykov, Dmitry A. Tarasov, Alexander I. Kotyusov, Yuri G. Pavlov (20). AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits. 10.18112/openneuro.ds006848.v1.0.0

30-participant EEG dataset — AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits.

Data & curation Alexandra I. Kosachenko · Danil I. Syttykov · Dmitry A. Tarasov · Alexander I. Kotyusov · Yuri G. Pavlov
Year 20 · Distributed via OpenNeuro

EEG · 65 ch1000 HzBIDS 1.7.02 tasksHealthyVisualMemory

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 DS006848

dataset = DS006848(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 = DS006848(cache_dir="./data", subject="01")

Advanced query

dataset = DS006848(
    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{ds006848,
  title = {AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits},
  author = {Alexandra I. Kosachenko and Danil I. Syttykov and Dmitry A. Tarasov and Alexander I. Kotyusov and Yuri G. Pavlov},
  doi = {10.18112/openneuro.ds006848.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006848.v1.0.0},
}

§ 02Study · The README

About This Dataset#

Overview

This dataset consists of raw 64-channel EEG, electrocardiography (ECG), photoplethysmography (PPG), and behavioral data recorded from 30 healthy young adults during two experimental conditions: resting state and a verbal working memory (digit span) task with serial recall.

Resting-state recording

During the resting-state session, participants alternated between four 1-minute blocks of eyes-closed and eyes-open resting, followed by 3 minutes 52 seconds of passive cartoon watching (“The Man Who Was Afraid of Falling”, 2011). EEG, ECG, and PPG were recorded continuously throughout this session.

Verbal working memory task In the verbal working memory task, participants were presented visually with sequences of seven digits under four different presentation modes:

Simultaneous – all seven digits presented together for 2800 ms;

Fast sequential – each digit presented for 400 ms;

Fast + delay sequential – each digit presented for 400 ms with a 600 ms inter-stimulus interval (ISI);

Slow sequential – each digit presented for 1000 ms.

They were instructed to memorize each sequence and type the digits in serial order using the right hand on the numpad. Behavioral accuracy and partial-score measures were computed for each trial.

Data organization Each participant folder (sub-XXX) contains:

eeg/ — EEG, ECG, and PPG recordings in BrainVision format (.vhdr, .vmrk, .eeg) accompanied by event (_events.tsv) and metadata (.json) files.

When available, both the resting-state (task-rest) and working-memory (task-verbalwm) recordings are stored here.

beh/ — behavioral data (_beh.tsv and _beh.json) with trial-by-trial recall accuracy, sequence information, and response measures.

Participants The dataset includes 30 participants (age range 18–32 years; 23 females, 7 males). Most were right-handed, with a few left-handed or ambidextrous.

All participants contributed working memory EEG and behavioral data. Several lacked resting state data for EEG, PPG, and ECG: sub-002, sub-003, sub-004, sub-005, sub-006, sub-008, sub-009, sub-011. Potential applications This dataset can be used to: 1. Develop algorithms that classify working memory load. 2. Study neural signals, including event-related potentials and oscillations, alongside peripheral physiology from ECG and PPG during encoding, maintenance, and retrieval at a fine time scale for each sequential item. 3. Examine how neural and physiological signals relate to behavioral accuracy and retrieval time on a trial-by-trial basis.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=30, range 18–32 yr, mean 19.9 yr)

152030

Female · 25Male · 5

Sex composition

Female
25
Male
5
F : M ratio
5.00 : 1

83% female · n = 30 subjects with reported sex.

HandednessRight · 24Left · 5

Channel counts: 65 ch (n=52 recordings)

Sampling frequencies: 1000.0 Hz (n=52 recordings)

Total recording duration: 47 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 65 ch · EEG · 1000 Hz · 30 subjects, 52 recordings

Live trace viewer — sub-021 · task-rest

Showing one representative recording out of 30 subjects and 52 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 · 63 sensors — 63 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.DS006848(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Kosachenko2025

Canonical—

Importable asDS006848 · Kosachenko2025

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

class eegdash.dataset.DS006848(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits

Study:: ds006848 (OpenNeuro)
Author (year):: Kosachenko2025
Canonical:: —

Also importable as: DS006848, Kosachenko2025.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 30; recordings: 52; tasks: 2.

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/ds006848 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006848 DOI: https://doi.org/10.18112/openneuro.ds006848.v1.0.0

Examples

>>> from eegdash.dataset import DS006848
>>> dataset = DS006848(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/ds006848 · pull with datasets.load_dataset("EEGDash/ds006848").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS006848.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 ds006848 to reproduce the tutorial on this dataset.

Citation

Alexandra I. Kosachenko, Danil I. Syttykov, Dmitry A. Tarasov, Alexander I. Kotyusov, Yuri G. Pavlov (20). AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits. 10.18112/openneuro.ds006848.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.ds006848.v1.0.0.

Related & sibling datasets

DS006963EEG · 32 subj DS007081EEG · 41 subj DS005697EEG · 51 subj DS007431EEG · 47 subj DS006367EEG · 52 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds006848.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS006848`
Title	AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits
Author (year)	`Kosachenko2025`
Canonical	—
Importable as	`DS006848`, `Kosachenko2025`
Year	20
Authors	Alexandra I. Kosachenko, Danil I. Syttykov, Dmitry A. Tarasov, Alexander I. Kotyusov, Yuri G. Pavlov
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006848.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS006848: eeg dataset, 30 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#