DS007169: eeg dataset, 18 subjects#

Multimodal Cognitive Workload n-back Task, 4 Difficulties

Access recordings and metadata through EEGDash.

Citation: Matthew Barras, Liam Booth (2026). Multimodal Cognitive Workload n-back Task, 4 Difficulties. 10.18112/openneuro.ds007169.v1.0.5

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

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007169

dataset = DS007169(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007169(cache_dir="./data", subject="01")

Advanced query

dataset = DS007169(
    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{ds007169,
  title = {Multimodal Cognitive Workload n-back Task, 4 Difficulties},
  author = {Matthew Barras and Liam Booth},
  doi = {10.18112/openneuro.ds007169.v1.0.5},
  url = {https://doi.org/10.18112/openneuro.ds007169.v1.0.5},
}

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 (n-backMarkers) and acquisition dropout annotations (UoHDataOffsetStream); events include a marker_stream column and marker definitions are in task-nback_events.json. - Pupil Labs gaze/pupil data was exported from the XDF pupil_capture stream into sub-*/pupil as *_task-nback_pupil.tsv + *_task-nback_eyetrack.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 + *_recording-ecg_physio.json under sub-*/ecg. - Analysis note: participants excluded from the 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 (n-backMarkers) and acquisition dropout annotations (UoHDataOffsetStream); events include a marker_stream column and marker definitions are in task-nback_events.json. - Pupil Labs gaze/pupil data was exported from the XDF pupil_capture stream into sub-*/pupil as *_task-nback_pupil.tsv + *_task-nback_eyetrack.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 + *_recording-ecg_physio.json under sub-*/ecg. - Analysis note: participants excluded from the 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 data quality failures (sub-013, 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 - Tutorial phase with feedback: 20 trials at each level (1-back through 4-back) after a 60 s fixation. - Main experiment: 100 trials at each level (1-back through 4-back) with no feedback. - Each level begins with a 6.0 s instruction screen (“Remember N steps back”). - Each trial shows a letter for 1.0 s, followed by a 0.7 s blank interval. - Task events encode nback_level, key_press, matched, response_accuracy, and tutorial flags in task-nback_events.json.

Task: nback Release notes - Recorded 20 participants; released 18. - Reason: data quality failures. - 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., Hochenberger, 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

DS007169

Title

Multimodal Cognitive Workload n-back Task, 4 Difficulties

Author (year)

Barras2026_Multimodal

Canonical

Barras2021

Importable as

DS007169, Barras2026_Multimodal, Barras2021

Year

2026

Authors

Matthew Barras, Liam Booth

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007169.v1.0.5

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007169,
  title = {Multimodal Cognitive Workload n-back Task, 4 Difficulties},
  author = {Matthew Barras and Liam Booth},
  doi = {10.18112/openneuro.ds007169.v1.0.5},
  url = {https://doi.org/10.18112/openneuro.ds007169.v1.0.5},
}

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: 18

  • Tasks: 1

Channels & sampling rate

  • Channels: 24

  • Sampling rate (Hz): 250.0

  • Duration (hours): 5.090633333333333

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Memory

Files & format

  • Size on disk: 421.7 MB

  • File count: 18

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007169.v1.0.5

Provenance

API Reference#

Use the DS007169 class to access this dataset programmatically.

class eegdash.dataset.DS007169(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Multimodal Cognitive Workload n-back Task, 4 Difficulties

Study:

ds007169 (OpenNeuro)

Author (year):

Barras2026_Multimodal

Canonical:

Barras2021

Also importable as: DS007169, Barras2026_Multimodal, Barras2021.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. 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/ds007169 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007169 DOI: https://doi.org/10.18112/openneuro.ds007169.v1.0.5

Examples

>>> from eegdash.dataset import DS007169
>>> dataset = DS007169(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

See Also#