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. 10.82901/nemar.nm000141

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

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
  doi = {10.82901/nemar.nm000141},
  url = {https://doi.org/10.82901/nemar.nm000141},
}

About This Dataset#

DOI

Motor execution dataset from Wairagkar et al 2018

Motor execution dataset from Wairagkar et al 2018.

Dataset Overview

Code: Wairagkar2018
Paradigm: imagery

View full README

DOI

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) 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	10.82901/nemar.nm000141
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

Electrode Layout#

Electrode layout — EEG · 19 sensors — 19 channels

Dataset Statistics#

Age distribution (n=14, range 26–26 yr)

25

Channel counts: 19 ch (n=14 recordings)

Sampling frequencies: 1024.0 Hz (n=14 recordings)

Total recording duration: 2 h 48 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 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 DOI: https://doi.org/10.82901/nemar.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: 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)