NM000148: eeg dataset, 30 subjects#

Motor imagery BCI dataset with pupillometry augmentation

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 30 Recordings: 60 License: CC0 1.0 Source: nemar

Metadata: Complete (90%)

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},
}

About This Dataset#

Motor imagery BCI dataset with pupillometry augmentation

Motor imagery BCI dataset with pupillometry augmentation.

Dataset Overview

  • Code: Rozado2015

  • Paradigm: imagery

  • DOI: 10.1371/journal.pone.0121262

View full README

Motor imagery BCI dataset with pupillometry augmentation

Motor imagery BCI dataset with pupillometry augmentation.

Dataset Overview

  • Code: Rozado2015

  • Paradigm: imagery

  • DOI: 10.1371/journal.pone.0121262

  • Subjects: 30

  • Sessions per subject: 1

  • Events: left_hand=1, rest=2

  • Trial interval: [0.0, 6.0] s

  • Runs per session: 2

  • File format: XDF

Acquisition

  • Sampling rate: 512.0 Hz

  • Number of channels: 32

  • Channel types: eeg=32

  • Montage: biosemi32

  • Hardware: BioSemi ActiveTwo

  • Reference: CMS/DRL

  • Sensor type: active

  • Line frequency: 50.0 Hz

  • Cap manufacturer: BioSemi

  • Electrode material: sintered Ag/AgCl

Participants

  • Number of subjects: 30

  • Health status: healthy

  • Age: mean=38.0, std=9.69, min=15, max=61

  • Gender distribution: male=15, female=15

  • Handedness: {‘right’: 27, ‘left’: 3}

Experimental Protocol

  • Paradigm: imagery

  • Task type: left hand grasping imagery vs rest

  • Number of classes: 2

  • Class labels: left_hand, rest

  • Trial duration: 6.0 s

  • Study design: Motor imagery with pupillometry augmentation

  • Feedback type: none

  • Stimulus type: auditory cue

  • Stimulus modalities: auditory

  • Primary modality: auditory

  • Synchronicity: synchronous

  • Mode: offline

HED Event Annotations

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

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) https://github.com/NeuroTechX/moabb

Dataset Information#

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

Unknown

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: 60

  • Tasks: 1

Channels & sampling rate

  • Channels: 32

  • Sampling rate (Hz): 512.0

  • Duration (hours): 5.702619900173612

Tags

  • Pathology: Healthy

  • Modality: Auditory

  • Type: Motor

Files & format

  • Size on disk: 975.3 MB

  • File count: 60

  • Format: BIDS

License & citation

  • License: CC0 1.0

  • DOI: —

Provenance

API Reference#

Use the NM000148 class to access this dataset programmatically.

class eegdash.dataset.NM000148(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

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, 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#