EEGdash›NeMAR›NM000148
Iss. 148 · 30 subjects · 60 recordings · CC0 1.0
Dataset Brief · Motor imagery BCI dataset with pupillometry augmentation

NM000148: eeg dataset, 30 subjects#

Motor imagery BCI dataset with pupillometry augmentation

Citation: David Rozado, Andreas Duenser, Ben Howell (2019). Motor imagery BCI dataset with pupillometry augmentation. 10.82901/nemar.nm000148

30-participant EEG dataset — Motor imagery BCI dataset with pupillometry augmentation.

Data & curation David Rozado · Andreas Duenser · Ben Howell
Year 2019 · Distributed via NeMAR
EEG · 32 ch512 HzBIDS 1.9.0Task · imageryHealthyAuditoryMotor
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000148

dataset = NM000148(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000148(cache_dir="./data", subject="01")

Advanced query

dataset = NM000148(
    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{nm000148,
  title = {Motor imagery BCI dataset with pupillometry augmentation},
  author = {David Rozado and Andreas Duenser and Ben Howell},
  doi = {10.82901/nemar.nm000148},
  url = {https://doi.org/10.82901/nemar.nm000148},
}
§ 02Study · The README

About This Dataset#

Motor imagery BCI dataset with pupillometry augmentation.

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

DOI

Motor imagery BCI dataset with pupillometry augmentation

left_hand
View full README

DOI

Motor imagery BCI dataset with pupillometry augmentation

left_hand

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Move
           └─ Left, Hand

rest

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest

Paradigm-Specific Parameters

  • Detected paradigm: motor_imagery

  • Imagery tasks: left hand grasping, rest

  • Imagery duration: 6.0 s

Data Structure

  • Blocks per session: 2

  • Block duration: 300.0 s

  • Trials context: 2 experiments of 25 trials each (50 trials total per subject). Each experiment is stored as one XDF file.

Signal Processing

  • Classifiers: LDA

  • Feature extraction: CSP, pupil_diameter

  • Frequency bands: bandpass=[8.0, 30.0] Hz

  • Spatial filters: CSP

Cross-Validation

  • Method: 10-fold

  • Folds: 10

  • Evaluation type: within_subject

BCI Application

  • Environment: lab

  • Online feedback: False

Tags

  • Pathology: healthy

  • Modality: auditory

  • Type: motor_imagery

Documentation

  • DOI: 10.1371/journal.pone.0121262

  • License: CC0 1.0

  • Investigators: David Rozado, Andreas Duenser, Ben Howell

  • Senior author: David Rozado

  • Institution: CSIRO

  • Department: Digital Productivity Flagship

  • Country: AU

  • Repository: Harvard Dataverse

  • Data URL: https://doi.org/10.7910/DVN/28932

  • Publication year: 2015

  • Keywords: motor imagery, BCI, pupillometry, EEG, brain-computer interface

References

D. Rozado, T. Duenser, and B. Gruen, “Improving the performance of an EEG-based motor imagery brain computer interface using task evoked changes in pupil diameter,” PLoS ONE, vol. 10, no. 3, e0121262, 2015. DOI: 10.1371/journal.pone.0121262 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 Generated by MOABB 1.5.0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=30, range 38–38 yr, mean 38.0 yr)

35
Other · 30

Channel counts: 32 ch (n=60 recordings)

Sampling frequencies: 512.0 Hz (n=60 recordings)

Total recording duration: 5 h 42 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 512 Hz · 30 subjects, 60 recordings
Live trace viewer — sub-11 · ses-0 · task-imagery · run-1

Showing one representative recording out of 30 subjects and 60 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 · 32 sensors — 32 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.

NEMAR pipeline statistics — NM000148
HED event descriptors word cloud HED event descriptors word cloud — NM000148
§ 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…
Full dataset metadata table

Dataset ID

NM000148

Title

Motor imagery BCI dataset with pupillometry augmentation

Author (year)

Rozado2015

Canonical

—

Importable as

NM000148, Rozado2015

Year

2019

Authors

David Rozado, Andreas Duenser, Ben Howell

License

CC0 1.0

Citation / DOI

10.82901/nemar.nm000148

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000148,
  title = {Motor imagery BCI dataset with pupillometry augmentation},
  author = {David Rozado and Andreas Duenser and Ben Howell},
  doi = {10.82901/nemar.nm000148},
  url = {https://doi.org/10.82901/nemar.nm000148},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000148(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Rozado2015
Canonical—
Importable asNM000148 · Rozado2015
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000148(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Motor imagery BCI dataset with pupillometry augmentation

Study:

nm000148 (NeMAR)

Author (year):

Rozado2015

Canonical:

—

Also importable as: NM000148, Rozado2015.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 30; recordings: 60; 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/nm000148 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000148 DOI: https://doi.org/10.82901/nemar.nm000148

Examples

>>> from eegdash.dataset import NM000148
>>> dataset = NM000148(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 FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — NM000148.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 nm000148 to reproduce the tutorial on this dataset.

Citation

David Rozado, Andreas Duenser, Ben Howell (2019). Motor imagery BCI dataset with pupillometry augmentation. 10.82901/nemar.nm000148

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000148.

BIDS
BIDS 1.9.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC0 1.0 · 10.82901/nemar.nm000148
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#