DS006848#

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

Access recordings and metadata through EEGDash.

Citation: Alexandra I. Kosachenko, Danil I. Syttykov, Dmitry A. Tarasov, Alexander I. Kotyusov, Yuri G. Pavlov (2025). AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits. 10.18112/openneuro.ds006848.v1.0.0

Modality: eeg Subjects: 30 Recordings: 520 License: CC0 Source: openneuro

Metadata: Complete (100%)

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},
}

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

View full README

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.

Dataset Information#

Dataset ID	`DS006848`
Title	AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits
Year	2025
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

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: 30
Recordings: 520
Tasks: 2

Channels & sampling rate

Channels: 65 (52), 63 (52)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS006848 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds006848. 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

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, 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