EEGdash›NeMAR›NM000173
Iss. 173 · 15 subjects · 300 recordings · CC-BY-4.0
Dataset Brief · Motor Imagery ataset from Ofner et al 2017

NM000173: eeg dataset, 15 subjects#

Motor Imagery ataset from Ofner et al 2017

Citation: Patrick Ofner, Andreas Schwarz, Joana Pereira, Gernot R. Müller-Putz (2019). Motor Imagery ataset from Ofner et al 2017. 10.82901/nemar.nm000173

15-participant EEG dataset — Motor Imagery ataset from Ofner et al 2017.

Data & curation Patrick Ofner · Andreas Schwarz · Joana Pereira · Gernot R. Müller-Putz
Year 2019 · Distributed via NeMAR
Funding H2020-643955 MoreGrasp · ERC Consolidator Grant ERC-681231 Feel Your Reach
EEG · 61 ch512 HzBIDS 1.9.0Task · imagery2 sessionsHealthyVisualMotor
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000173

dataset = NM000173(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000173(cache_dir="./data", subject="01")

Advanced query

dataset = NM000173(
    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{nm000173,
  title = {Motor Imagery ataset from Ofner et al 2017},
  author = {Patrick Ofner and Andreas Schwarz and Joana Pereira and Gernot R. Müller-Putz},
  doi = {10.82901/nemar.nm000173},
  url = {https://doi.org/10.82901/nemar.nm000173},
}
§ 02Study · The README

About This Dataset#

Motor Imagery ataset from Ofner et al 2017.

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

DOI

Motor Imagery ataset from Ofner et al 2017

right_elbow_flexion
View full README

DOI

Motor Imagery ataset from Ofner et al 2017

right_elbow_flexion

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Flex
           └─ Right, Elbow

right_elbow_extension

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Stretch
           └─ Right, Elbow

right_supination

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Turn
           ├─ Right, Forearm
           └─ Label/supination

right_pronation

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Turn
           ├─ Right, Forearm
           └─ Label/pronation

right_hand_close

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

right_hand_open

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

rest

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

Paradigm-Specific Parameters

  • Detected paradigm: motor_imagery

  • Imagery tasks: elbow_flexion, elbow_extension, forearm_supination, forearm_pronation, hand_open, hand_close

Data Structure

  • Trials: 420

  • Trials per class: elbow_flexion=60, elbow_extension=60, forearm_supination=60, forearm_pronation=60, hand_open=60, hand_close=60, rest=60

  • Trials context: per_session

Preprocessing

  • Preprocessing applied: False

Signal Processing

  • Classifiers: sLDA

  • Feature extraction: time-domain signals, discriminative spatial patterns (DSP)

  • Frequency bands: analyzed=[0.3, 3.0] Hz

  • Spatial filters: sLORETA source localization

Cross-Validation

  • Method: 10x10-fold cross-validation

  • Folds: 10

  • Evaluation type: within-session

Performance (Original Study)

  • Mov Vs Mov Me: 55.0

  • Mov Vs Rest Me: 87.0

  • Mov Vs Mov Mi: 27.0

  • Mov Vs Rest Mi: 73.0

BCI Application

  • Applications: neuroprosthesis, robotic_arm

  • Environment: laboratory

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: Motor

  • Type: Motor Imagery, Motor Execution

Documentation

  • DOI: 10.1371/journal.pone.0182578

  • Associated paper DOI: 10.1371/journal.pone.0182578

  • License: CC-BY-4.0

  • Investigators: Patrick Ofner, Andreas Schwarz, Joana Pereira, Gernot R. Müller-Putz

  • Senior author: Gernot R. Müller-Putz

  • Contact: gernot.mueller@tugraz.at

  • Institution: Graz University of Technology

  • Department: Institute of Neural Engineering, BCI-Lab

  • Country: AT

  • Repository: BNCI Horizon 2020

  • Data URL: https://bnci-horizon-2020.eu/database/data-sets

  • Publication year: 2017

  • Funding: H2020-643955 MoreGrasp; ERC Consolidator Grant ERC-681231 Feel Your Reach

  • Ethics approval: Medical University of Graz, approval number 28-108 ex 15/16

  • Acknowledgements: Data are available from the BNCI Horizon 2020 database at http://bnci-horizon-2020.eu/database/data-sets (accession number 001-2017) and from Zenodo at DOI 10.5281/zenodo.834976

  • Keywords: upper limb movements, EEG, motor imagery, movement execution, low-frequency, time-domain, BCI, neuroprosthesis

Abstract

How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.

Methodology

Subjects performed 6 sustained upper limb movements (elbow flexion/extension, forearm supination/pronation, hand open/close) plus rest in two separate sessions (movement execution and motor imagery). EEG was recorded from 61 channels, filtered to 0.3-3 Hz, and classified using shrinkage LDA with discriminative spatial patterns. Source localization was performed using sLORETA. Classification employed both single time-point and time-window approaches with 10x10-fold cross-validation.

References

Ofner, P., Schwarz, A., Pereira, J. and Müller-Putz, G.R., 2017. Upper limb movements can be decoded from the time-domain of low-frequency EEG. PloS one, 12(8), p.e0182578. https://doi.org/10.1371/journal.pone.0182578 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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=15, range 2014–2014 yr)

2010
Other · 15

Channel counts: 61 ch (n=300 recordings)

Sampling frequencies: 512.0 Hz (n=300 recordings)

Total recording duration: 27 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 61 ch · EEG · 512 Hz · 15 subjects, 300 recordings
Live trace viewer — sub-13 · ses-0execution · task-imagery · run-2

Showing one representative recording out of 15 subjects and 300 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 · 61 sensors — 61 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.

NEMAR pipeline statistics — NM000173
HED event descriptors word cloud HED event descriptors word cloud — NM000173
§ 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…
Full dataset metadata table

Dataset ID

NM000173

Title

Motor Imagery ataset from Ofner et al 2017

Author (year)

Ofner2017

Canonical

—

Importable as

NM000173, Ofner2017

Year

2019

Authors

Patrick Ofner, Andreas Schwarz, Joana Pereira, Gernot R. Müller-Putz

License

CC-BY-4.0

Citation / DOI

10.82901/nemar.nm000173

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000173,
  title = {Motor Imagery ataset from Ofner et al 2017},
  author = {Patrick Ofner and Andreas Schwarz and Joana Pereira and Gernot R. Müller-Putz},
  doi = {10.82901/nemar.nm000173},
  url = {https://doi.org/10.82901/nemar.nm000173},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000173(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Ofner2017
Canonical—
Importable asNM000173 · Ofner2017
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000173(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Motor Imagery ataset from Ofner et al 2017

Study:

nm000173 (NeMAR)

Author (year):

Ofner2017

Canonical:

—

Also importable as: NM000173, Ofner2017.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 15; recordings: 300; 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/nm000173 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000173 DOI: https://doi.org/10.82901/nemar.nm000173

Examples

>>> from eegdash.dataset import NM000173
>>> dataset = NM000173(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 — NM000173.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 nm000173 to reproduce the tutorial on this dataset.

Citation

Patrick Ofner, Andreas Schwarz, Joana Pereira, Gernot R. Müller-Putz (2019). Motor Imagery ataset from Ofner et al 2017. 10.82901/nemar.nm000173

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000173.

BIDS
BIDS 1.9.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC-BY-4.0 · 10.82901/nemar.nm000173
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#