EEGdash›NeMAR›NM000327

Iss. 327 · 55 subjects · 220 recordings · CC-BY-4.0

Dataset Brief · Leeuwis2021

NM000327: eeg dataset, 55 subjects#

Leeuwis2021: Left- vs right-hand motor-imagery EEG dataset

Access recordings and metadata through EEGDash.

Citation: Nikki Leeuwis, Alissa Paas, Maryam Alimardani (2021). Leeuwis2021: Left- vs right-hand motor-imagery EEG dataset. 10.82901/nemar.nm000327

Modality: eeg Subjects: 55 Recordings: 220 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

55-participant EEG dataset — Leeuwis2021: Left- vs right-hand motor-imagery EEG dataset.

Data & curation Nikki Leeuwis · Alissa Paas · Maryam Alimardani
Senior author Maryam Alimardani
Year 2021 · Distributed via NeMAR

EEG · 16 ch250 HzBIDS 1.9.0Task · imagery

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#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000327

dataset = NM000327(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 = NM000327(cache_dir="./data", subject="01")

Advanced query

dataset = NM000327(
    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{nm000327,
  title = {Leeuwis2021: Left- vs right-hand motor-imagery EEG dataset},
  author = {Nikki Leeuwis and Alissa Paas and Maryam Alimardani},
  doi = {10.82901/nemar.nm000327},
  url = {https://doi.org/10.82901/nemar.nm000327},
}

§ 02Study · The README

About This Dataset#

Left- vs right-hand motor-imagery EEG dataset [1]_.

Code: Leeuwis2021

Paradigm: imagery DOI: 10.34894/Z7ZVOD Subjects: 55 Sessions per subject: 1 Events: left_hand=1, right_hand=2 Trial interval: [0, 4.996] s Runs per session: 4 File format: CSV

DOI

Leeuwis2021

Acquisition

Sampling rate: 250.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: F3, Fz, F4, FC5, FC1, FC2, FC6, T7, C3, C4, Cz, T8, CP5, CP1, CP2, CP6

View full README

DOI

Leeuwis2021

Acquisition

Sampling rate: 250.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: F3, Fz, F4, FC5, FC1, FC2, FC6, T7, C3, C4, Cz, T8, CP5, CP1, CP2, CP6 Montage: standard_1020 Hardware: g.Nautilus (g.tec Medical Engineering, Austria) Software: g.BSanalyze (g.tec) Reference: right earlobe Ground: AFz Line frequency: 50.0 Hz

Participants

Number of subjects: 55 Health status: healthy Age: mean=20.71, std=3.52 Handedness: right BCI experience: naive

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 5.0 s Trials per class: left_hand=80, right_hand=80 Study design: Single-session, two-class left- versus right-hand motor imagery in 55 novice BCI users. One calibration run (no feedback) plus three feedback runs of 40 trials each (20 left, 20 right). Study examined psychological and cognitive predictors of MI-BCI performance. Feedback type: visual Stimulus type: visual cue Stimulus modalities: visual Synchronicity: cue-based Mode: online Training/test split: True Instructions: Imagine moving the left or right hand following the cue.

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

right_hand

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

Tags

Pathology: healthy Modality: Motor Type: Motor Imagery

Documentation

Description: Single-session left/right-hand motor-imagery EEG from 55 novice BCI users (16 channels, 250 Hz, g.Nautilus), collected to study psychological and cognitive factors in motor-imagery BCI performance. DOI: 10.34894/Z7ZVOD License: CC-BY-4.0 Investigators: Nikki Leeuwis, Alissa Paas, Maryam Alimardani Institution: Tilburg University, Tilburg School of Humanities and Digital Sciences Country: NL Repository: DataverseNL Data URL: https://doi.org/10.34894/Z7ZVOD Publication year: 2021 Keywords: motor imagery, BCI, brain-computer interface, EEG, cognition, personality

References

Leeuwis, N., Paas, A., and Alimardani, M. (2021). Psychological and Cognitive Factors in Motor Imagery Brain Computer Interfaces. DataverseNL, V1. DOI: https://doi.org/10.34894/Z7ZVOD See also: Leeuwis, N., Paas, A., & Alimardani, M. (2021). Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces. Frontiers in Human Neuroscience, 15, 634748.

Notes .. versionadded:: 1.8.0 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 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

Ethics

Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB.

Please consult the primary publication for the exact IRB/ethics committee reference.

Ethics

Reviewed and approved by the Research Ethics Committee of Tilburg School of Humanities and Digital Sciences. All participants provided written informed consent to participate in the study (Leeuwis, Paas & Alimardani 2021, Front. Hum. Neurosci. 15:634748, DOI 10.3389/fnhum.2021.634748).

Verbatim from the source:

This study was reviewed and approved by the Research Ethics Committee of Tilburg School of Humanities and Digital Sciences. The patients/participants provided their written informed consent to participate in this study.

Source: cached paper .paper-audit/Leeuwis2021/paper-10_3389_fnhum_2021_634748.txt (Leeuwis, Paas & Alimardani 2021, Front. Hum. Neurosci. 15:634748, DOI 10.3389/fnhum.2021.634748).

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000327-blue)](https://doi.org/10.82901/nemar.nm000327) Leeuwis2021 =========== Left- vs right-hand motor-imagery EEG dataset [1]_. Dataset Overview —————-

Code: Leeuwis2021 Paradigm: imagery DOI: 10.34894/Z7ZVOD Subjects: 55 Sessions per subject: 1 Events: left_hand=1, right_hand=2 Trial interval: [0, 4.996] s Runs per session: 4 File format: CSV

Acquisition#

Sampling rate: 250.0 Hz Number of channels: 16 Channel types: eeg=16 Channel names: F3, Fz, F4, FC5, FC1, FC2, FC6, T7, C3, C4, Cz, T8, CP5, CP1, CP2, CP6 Montage: standard_1020 Hardware: g.Nautilus (g.tec Medical Engineering, Austria) Software: g.BSanalyze (g.tec) Reference: right earlobe Ground: AFz Line frequency: 50.0 Hz

Participants#

Number of subjects: 55 Health status: healthy Age: mean=20.71, std=3.52 Handedness: right BCI experience: naive

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 5.0 s Trials per class: left_hand=80, right_hand=80 Study design: Single-session, two-class left- versus right-hand motor imagery in 55 novice BCI users. One calibration run (no feedback) plus three feedback runs of 40 trials each (20 left, 20 right). Study examined psychological and cognitive predictors of MI-BCI performance. Feedback type: visual Stimulus type: visual cue Stimulus modalities: visual Synchronicity: cue-based Mode: online Training/test split: True Instructions: Imagine moving the left or right hand following the cue.

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

right_hand
├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Right, Hand

Tags#

Pathology: healthy Modality: Motor Type: Motor Imagery

Documentation#

Description: Single-session left/right-hand motor-imagery EEG from 55 novice BCI users (16 channels, 250 Hz, g.Nautilus), collected to study psychological and cognitive factors in motor-imagery BCI performance. DOI: 10.34894/Z7ZVOD License: CC-BY-4.0 Investigators: Nikki Leeuwis, Alissa Paas, Maryam Alimardani Institution: Tilburg University, Tilburg School of Humanities and Digital Sciences Country: NL Repository: DataverseNL Data URL: https://doi.org/10.34894/Z7ZVOD Publication year: 2021 Keywords: motor imagery, BCI, brain-computer interface, EEG, cognition, personality

References#

Leeuwis, N., Paas, A., and Alimardani, M. (2021). Psychological and Cognitive Factors in Motor Imagery Brain Computer Interfaces. DataverseNL, V1. DOI: https://doi.org/10.34894/Z7ZVOD See also: Leeuwis, N., Paas, A., & Alimardani, M. (2021). Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces. Frontiers in Human Neuroscience, 15, 634748. Notes .. versionadded:: 1.8.0 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 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb Ethics —— Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB. Please consult the primary publication for the exact IRB/ethics committee reference. ## Ethics Reviewed and approved by the Research Ethics Committee of Tilburg School of Humanities and Digital Sciences. All participants provided written informed consent to participate in the study (Leeuwis, Paas & Alimardani 2021, Front. Hum. Neurosci. 15:634748, DOI 10.3389/fnhum.2021.634748). Verbatim from the source: > This study was reviewed and approved by the Research Ethics Committee of Tilburg School of Humanities and Digital Sciences. The patients/participants provided their written informed consent to participate in this study. Source: cached paper .paper-audit/Leeuwis2021/paper-10_3389_fnhum_2021_634748.txt (Leeuwis, Paas & Alimardani 2021, Front. Hum. Neurosci. 15:634748, DOI 10.3389/fnhum.2021.634748).

License: CC-BY-4.0

Authors:

Nikki Leeuwis
Alissa Paas
Maryam Alimardani

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000327

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=55, range 21–21 yr, mean 20.0 yr)

20

Other · 55

Channel counts: 16 ch (n=220 recordings)

Sampling frequencies: 250.0 Hz (n=220 recordings)

Total recording duration: 19 h 33 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 250 Hz · 55 subjects, 220 recordings

Live trace viewer — sub-47 · ses-0 · task-imagery · run-3

Showing one representative recording out of 55 subjects and 220 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 · 16 sensors — 16 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.

HED event descriptors word cloud

§ 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…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000327(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000327

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000327(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Leeuwis2021: Left- vs right-hand motor-imagery EEG dataset

Study:: nm000327 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000327.

Modality: eeg; Subject type: Unknown. Subjects: 55; recordings: 220; 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/nm000327 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000327 DOI: https://doi.org/10.82901/nemar.nm000327

Examples

>>> from eegdash.dataset import NM000327
>>> dataset = NM000327(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 — NM000327.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 nm000327 to reproduce the tutorial on this dataset.

Citation

Nikki Leeuwis, Alissa Paas, Maryam Alimardani (2021). Leeuwis2021: Left- vs right-hand motor-imagery EEG dataset. 10.82901/nemar.nm000327

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000327.

Related & sibling datasets

NM000280EEG · 47 subj NM000328EEG · 60 subj NM000334EEG · 41 subj NM000324EEG · 32 subj NM000296EEG · 30 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000327

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000327`
Title	Leeuwis2021: Left- vs right-hand motor-imagery EEG dataset
Author (year)	—
Canonical	—
Importable as	`NM000327`
Year	2021
Authors	Nikki Leeuwis, Alissa Paas, Maryam Alimardani
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000327
Source links	OpenNeuro \| NeMAR \| Source URL

NM000327: eeg dataset, 55 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Tags#

Documentation#

References#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#