NM000148: eeg dataset, 30 subjects#

Name: Motor imagery BCI dataset with pupillometry augmentation
Published: 2019-01-01
License: CC0 1.0

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. 10.82901/nemar.nm000148

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

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

About This Dataset#

DOI

Motor imagery BCI dataset with pupillometry augmentation

Motor imagery BCI dataset with pupillometry augmentation.

Dataset Overview

Code: Rozado2015
Paradigm: imagery

View full README

DOI

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) 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	10.82901/nemar.nm000148
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

Electrode Layout#

Electrode layout — EEG · 32 sensors — 32 channels

Dataset Statistics#

Age distribution (n=30, range 38–38 yr)

35

Channel counts: 32 ch (n=60 recordings)

Sampling frequencies: 512.0 Hz (n=60 recordings)

Total recording duration: 5 h 42 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 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 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)