DS007262#

Cognitive Workload 8-level arithmetic

Access recordings and metadata through EEGDash.

Citation: Matthew Barras, Liam Booth (2026). Cognitive Workload 8-level arithmetic. 10.18112/openneuro.ds007262.v1.0.2

Modality: eeg Subjects: 18 Recordings: 134 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007262

dataset = DS007262(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 = DS007262(cache_dir="./data", subject="01")

Advanced query

dataset = DS007262(
    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{ds007262,
  title = {Cognitive Workload 8-level arithmetic},
  author = {Matthew Barras and Liam Booth},
  doi = {10.18112/openneuro.ds007262.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds007262.v1.0.2},
}

About This Dataset#

This dataset was generated from LSL/XDF recordings. Converted to bids with instructions and code presented here_

Original recordings are stored under sourcedata/xdf/ as .xdf files (non-BIDS).
EEG was converted to BrainVision format (.vhdr/.eeg/.vmrk) under each sub-*/eeg/.
*_events.tsv was generated from marker streams and then aligned so onset is relative to the EEG start time.
Marker streams include task markers (arithmetic-Markers) and acquisition dropout annotations (UoHDataOffsetStream); events include a marker_stream column and marker definitions are in task-arithmetic_events.json.
Pupil Labs gaze/pupil data was exported from the XDF pupil_capture stream into sub-*/eeg/ as eyetrack physio files (*_recording-eyetrack_physio.tsv.gz + *_recording-eyetrack_physio.json; PhysioType=eyetrack).
ECG is captured on the EEG system; the ECG channel is typed in *_channels.tsv and exported as *_recording-ecg_physio.tsv.gz + *_recording-ecg_physio.json.
ML analysis note: participants excluded from the ML analysis remain in participants.tsv with analysis_included=false; no epoch rejection was applied to this raw dataset.
Participant IDs match the original XDF filenames; missing IDs correspond to excluded participants.

View full README

This dataset was generated from LSL/XDF recordings. Converted to bids with instructions and code presented here_

Original recordings are stored under sourcedata/xdf/ as .xdf files (non-BIDS).
EEG was converted to BrainVision format (.vhdr/.eeg/.vmrk) under each sub-*/eeg/.
*_events.tsv was generated from marker streams and then aligned so onset is relative to the EEG start time.
Marker streams include task markers (arithmetic-Markers) and acquisition dropout annotations (UoHDataOffsetStream); events include a marker_stream column and marker definitions are in task-arithmetic_events.json.
Pupil Labs gaze/pupil data was exported from the XDF pupil_capture stream into sub-*/eeg/ as eyetrack physio files (*_recording-eyetrack_physio.tsv.gz + *_recording-eyetrack_physio.json; PhysioType=eyetrack).
ECG is captured on the EEG system; the ECG channel is typed in *_channels.tsv and exported as *_recording-ecg_physio.tsv.gz + *_recording-ecg_physio.json.
ML analysis note: participants excluded from the ML analysis remain in participants.tsv with analysis_included=false; no epoch rejection was applied to this raw dataset.
Participant IDs match the original XDF filenames; missing IDs correspond to excluded participants.

Participants - N_recorded: 20 - N_released: 18 - Exclusions: 2 participants excluded due to multi-modal acquisition failures (sub-002, sub-017). - Demographics in participants.tsv: age (years), sex, handedness. - Excluded IDs remain in participants.tsv with analysis_included=false.

Hardware and data collection - Combined EEG+ECG mobile EEG system (Bateson and Asghar, 2021; Clewett et al., 2016) and Pupil Labs Pupil Core, synchronized via Lab Streaming Layer (LSL). - EEG: 19-channel 10-20 montage (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2), Ag/AgCl electrodes with linked-ear reference, 250 Hz; impedances checked and Neurgel EEG gel applied. - ECG: 3-lead on the same system; positive lead right shoulder/clavicle, negative lead left shoulder/clavicle, feedback lead lower left torso. - Pupillometry: Pupil Labs Pupil Core eye tracking with infrared illuminators; LSL relay with asynchronous sampling (timestamps per sample).

Protocol summary - Arithmetic task difficulty was defined using Q-value ranges and randomized order across trials. - Task events encode difficulty in trial_type and difficulty_range (e.g.,baseline, 0.6-1.5, 1.5-2.4, …, 6.0-6.9). - Baseline for 60 seconds and then 70 questions, 10 at each difficulty level presented for 6 seconds each.

Task: arithmetic

Release notes - Recorded 20 participants; released 18. - Reason: multi-modal acquisition QC failure. - Participant IDs match original XDF filenames; missing IDs indicate excluded participants.

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8

Clewett CJ, Langley P, Bateson AD et al (2016) Non-invasive, home-based electroencephalography hypoglycaemia warning system for personal monitoring using skin surface electrodes: a single-case feasibility study. Healthc Technol Lett 3:2-5. https://doi.org/10.1049/htl.2015.0037

Bateson AD, Asghar AUR (2021) Development and evaluation of a smartphone-based electroencephalography (EEG) system. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3079992

Dataset Information#

Dataset ID	`DS007262`
Title	Cognitive Workload 8-level arithmetic
Year	2026
Authors	Matthew Barras, Liam Booth
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007262.v1.0.2
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: 18
Recordings: 134
Tasks: 1

Channels & sampling rate

Channels: 24
Sampling rate (Hz): 250.0
Duration (hours): 0.0

API Reference#

Use the DS007262 class to access this dataset programmatically.

class eegdash.dataset.DS007262(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds007262. Modality: eeg. Subjects: 18; recordings: 18; 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/ds007262 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007262

Examples

>>> from eegdash.dataset import DS007262
>>> dataset = DS007262(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