NM000173: eeg dataset, 15 subjects#

Motor Imagery ataset from Ofner et al 2017

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 15 Recordings: 300 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

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},
}

About This Dataset#

Motor Imagery ataset from Ofner et al 2017

Motor Imagery ataset from Ofner et al 2017.

Dataset Overview

  • Code: Ofner2017

  • Paradigm: imagery

  • DOI: 10.1371/journal.pone.0182578

View full README

Motor Imagery ataset from Ofner et al 2017

Motor Imagery ataset from Ofner et al 2017.

Dataset Overview

  • Code: Ofner2017

  • Paradigm: imagery

  • DOI: 10.1371/journal.pone.0182578

  • Subjects: 15

  • Sessions per subject: 2

  • Events: right_elbow_flexion=1536, right_elbow_extension=1537, right_supination=1538, right_pronation=1539, right_hand_close=1540, right_hand_open=1541, rest=1542

  • Trial interval: [0, 3] s

  • Runs per session: 10

  • Session IDs: movement_execution, motor_imagery

  • File format: gdf

Acquisition

  • Sampling rate: 512.0 Hz

  • Number of channels: 61

  • Channel types: eeg=61, eog=3, misc=32

  • Channel names: C1, C2, C3, C4, C5, C6, CCP1h, CCP2h, CCP3h, CCP4h, CCP5h, CCP6h, CP1, CP2, CP3, CP4, CP5, CP6, CPP1h, CPP2h, CPP3h, CPP4h, CPP5h, CPP6h, CPz, Cz, F1, F2, F3, F4, FC1, FC2, FC3, FC4, FC5, FC6, FCC1h, FCC2h, FCC3h, FCC4h, FCC5h, FCC6h, FCz, FFC1h, FFC2h, FFC3h, FFC4h, FFC5h, FFC6h, FTT7h, FTT8h, Fz, P1, P2, P3, P4, PPO1h, PPO2h, Pz, TTP7h, TTP8h, armeodummy-0, armeodummy-1, armeodummy-10, armeodummy-11, armeodummy-12, armeodummy-2, armeodummy-3, armeodummy-4, armeodummy-5, armeodummy-6, armeodummy-7, armeodummy-8, armeodummy-9, eog-l, eog-m, eog-r, gesture, index_far, index_middle, index_near, litte_far, litte_near, middle_far, middle_near, middle_ring, pitch, ring_far, ring_little, ring_near, roll, thumb_far, thumb_index, thumb_near, thumb_palm, wrist_bend

  • Montage: standard_1005

  • Hardware: g.tec medical engineering GmbH

  • Reference: right mastoid

  • Ground: AFz

  • Sensor type: active

  • Line frequency: 50.0 Hz

  • Online filters: 0.01-200 Hz bandpass (8th order Chebyshev), 50 Hz notch

Participants

  • Number of subjects: 15

  • Health status: healthy

  • Age: mean=27.0, std=5.0, min=22.0, max=40.0

  • Gender distribution: female=9, male=6

  • Handedness: {‘right’: 14, ‘left’: 1}

  • Species: human

Experimental Protocol

  • Paradigm: imagery

  • Number of classes: 7

  • Class labels: right_elbow_flexion, right_elbow_extension, right_supination, right_pronation, right_hand_close, right_hand_open, rest

  • Study design: Trial-based paradigm with sustained movements/motor imagery. Each trial: fixation cross at 0s, cue presentation at 2s, sustained movement/MI execution. Subjects performed both movement execution (ME) and motor imagery (MI) in separate sessions.

  • Feedback type: none

  • Stimulus type: visual cue

  • Synchronicity: synchronous

  • Mode: offline

  • Training/test split: False

  • Instructions: Subjects were instructed to execute sustained movements in ME session and perform kinesthetic motor imagery in MI session. For rest class, subjects were instructed to avoid any movement and to stay in the starting position.

HED Event Annotations

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

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) https://github.com/NeuroTechX/moabb

Dataset Information#

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

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: 15

  • Recordings: 300

  • Tasks: 1

Channels & sampling rate

  • Channels: 61

  • Sampling rate (Hz): 512.0

  • Duration (hours): 27.10289279513889

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 8.5 GB

  • File count: 300

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000173 class to access this dataset programmatically.

class eegdash.dataset.NM000173(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

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, 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#