NM000141: eeg dataset, 14 subjects#

Motor execution dataset from Wairagkar et al 2018

Access recordings and metadata through EEGDash.

Citation: Maitreyee Wairagkar, Yoshikatsu Hayashi, Slawomir J. Nasuto (2018). Motor execution dataset from Wairagkar et al 2018.

Modality: eeg Subjects: 14 Recordings: 14 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000141

dataset = NM000141(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000141(cache_dir="./data", subject="01")

Advanced query

dataset = NM000141(
    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{nm000141,
  title = {Motor execution dataset from Wairagkar et al 2018},
  author = {Maitreyee Wairagkar and Yoshikatsu Hayashi and Slawomir J. Nasuto},
}

About This Dataset#

Motor execution dataset from Wairagkar et al 2018

Motor execution dataset from Wairagkar et al 2018.

Dataset Overview

  • Code: Wairagkar2018

  • Paradigm: imagery

  • DOI: 10.1371/journal.pone.0193722

View full README

Motor execution dataset from Wairagkar et al 2018

Motor execution dataset from Wairagkar et al 2018.

Dataset Overview

  • Code: Wairagkar2018

  • Paradigm: imagery

  • DOI: 10.1371/journal.pone.0193722

  • Subjects: 14

  • Sessions per subject: 1

  • Events: right_hand=1, rest=2, left_hand=3

  • Trial interval: [0, 3] s

  • File format: MAT

  • Data preprocessed: True

Acquisition

  • Sampling rate: 1024.0 Hz

  • Number of channels: 19

  • Channel types: eeg=19

  • Channel names: Fp1, Fp2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8, O1, O2

  • Montage: standard_1020

  • Hardware: Deymed TruScan 32

  • Reference: FCz

  • Ground: AFz

  • Sensor type: Ag/AgCl ring

  • Line frequency: 50.0 Hz

  • Online filters: {‘highpass’: 0.5, ‘lowpass’: 60, ‘notch_hz’: 50}

Participants

  • Number of subjects: 14

  • Health status: healthy

  • Age: mean=26.0, std=4.0

  • Gender distribution: female=8, male=6

  • Handedness: mixed (12 right, 2 left)

  • BCI experience: naive

  • Species: human

Experimental Protocol

  • Paradigm: imagery

  • Number of classes: 3

  • Class labels: right_hand, rest, left_hand

  • Trial duration: 6.0 s

  • Study design: Asynchronous voluntary finger tapping: right tap, left tap, and resting state

  • Feedback type: none

  • Stimulus type: text cues

  • Stimulus modalities: visual

  • Primary modality: visual

  • Synchronicity: asynchronous

  • Mode: offline

  • Instructions: Participants were asked to tap their index finger at a self-chosen time within a 10-second window after the cue

HED Event Annotations

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

right_hand

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

rest

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

left_hand

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

Paradigm-Specific Parameters

  • Detected paradigm: motor_imagery

  • Imagery tasks: right_hand, left_hand, rest

Data Structure

  • Trials: 1665

  • Trials context: 14 subjects x 120 trials (40 per condition), except subject 2 with 105 trials (35 per condition)

Preprocessing

  • Data state: preprocessed

  • Preprocessing applied: True

  • Steps: DC offset removal, 0.5 Hz high-pass filter, 50 Hz notch filter, 60 Hz low-pass filter, ICA artifact removal (EEGLAB infomax), trial segmentation (-3 to +3 s around movement onset)

  • Highpass filter: 0.5 Hz

  • Lowpass filter: 60.0 Hz

  • Notch filter: 50.0 Hz

Signal Processing

  • Classifiers: LDA

  • Feature extraction: autocorrelation_relaxation_time, ERD

  • Frequency bands: broadband=[0.5, 30.0] Hz; mu=[8.0, 13.0] Hz; beta=[13.0, 30.0] Hz; low=[0.5, 8.0] Hz

  • Spatial filters: bipolar_montage

Cross-Validation

  • Method: 10x10-fold

  • Folds: 10

  • Evaluation type: within_subject

BCI Application

  • Applications: motor_control

  • Environment: laboratory

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: Motor

  • Type: Research

Documentation

  • DOI: 10.1371/journal.pone.0193722

  • License: CC-BY-4.0

  • Investigators: Maitreyee Wairagkar, Yoshikatsu Hayashi, Slawomir J. Nasuto

  • Senior author: Slawomir J. Nasuto

  • Institution: University of Reading

  • Department: Brain Embodiment Lab, Biomedical Engineering

  • Country: GB

  • Repository: University of Reading Research Data Archive

  • Data URL: https://researchdata.reading.ac.uk/117/

  • Publication year: 2018

References

Wairagkar, M., Hayashi, Y., & Nasuto, S. J. (2018). Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography. PLOS ONE, 13(3), e0193722. https://doi.org/10.1371/journal.pone.0193722 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 1.4.3 (Mother of All BCI Benchmarks) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000141

Title

Motor execution dataset from Wairagkar et al 2018

Author (year)

Wairagkar2018

Canonical

—

Importable as

NM000141, Wairagkar2018

Year

2018

Authors

Maitreyee Wairagkar, Yoshikatsu Hayashi, Slawomir J. Nasuto

License

CC-BY-4.0

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: 14

  • Recordings: 14

  • Tasks: 1

Channels & sampling rate

  • Channels: 19

  • Sampling rate (Hz): 1024.0

  • Duration (hours): 2.8049180772569446

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 571.7 MB

  • File count: 14

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000141 class to access this dataset programmatically.

class eegdash.dataset.NM000141(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Motor execution dataset from Wairagkar et al 2018

Study:

nm000141 (NeMAR)

Author (year):

Wairagkar2018

Canonical:

—

Also importable as: NM000141, Wairagkar2018.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 14; recordings: 14; 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/nm000141 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000141

Examples

>>> from eegdash.dataset import NM000141
>>> dataset = NM000141(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#