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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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	—
Importable as	`DS007169`, `Barras2026_Multimodal`
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

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

Electrode Layout#

Electrode layout — EEG · 19 sensors — 19 channels

Dataset Statistics#

Age distribution (n=20, range 18–48 yr)

1520253045

Sex distribution

6

14

Female Male Total: 20

Channel counts: 24 ch (n=18 recordings)

Sampling frequencies: 250.0 Hz (n=18 recordings)

Total recording duration: 5 h 5 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

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:: —

Also importable as: DS007169, Barras2026_Multimodal.

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: 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)