NM000145: eeg dataset, 10 subjects#

Munich Motor Imagery dataset

Access recordings and metadata through EEGDash.

Citation: Moritz Grosse-Wentrup, Christian Liefhold, Klaus Gramann, Martin Buss (2009). Munich Motor Imagery dataset.

Modality: eeg Subjects: 10 Recordings: 10 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000145

dataset = NM000145(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000145(cache_dir="./data", subject="01")

Advanced query

dataset = NM000145(
    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{nm000145,
  title = {Munich Motor Imagery dataset},
  author = {Moritz Grosse-Wentrup and Christian Liefhold and Klaus Gramann and Martin Buss},
}

About This Dataset#

Munich Motor Imagery dataset

Munich Motor Imagery dataset.

Dataset Overview

  • Code: GrosseWentrup2009

  • Paradigm: imagery

  • DOI: 10.1109/TBME.2008.2009768

View full README

Munich Motor Imagery dataset

Munich Motor Imagery dataset.

Dataset Overview

  • Code: GrosseWentrup2009

  • Paradigm: imagery

  • DOI: 10.1109/TBME.2008.2009768

  • Subjects: 10

  • Sessions per subject: 1

  • Events: right_hand=2, left_hand=1

  • Trial interval: [0, 7] s

  • File format: set

  • Data preprocessed: True

Acquisition

  • Sampling rate: 500.0 Hz

  • Number of channels: 128

  • Channel types: eeg=128

  • Channel names: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128

  • Montage: standard_1020

  • Hardware: BrainAmp

  • Reference: Cz

  • Line frequency: 50.0 Hz

  • Online filters: {‘highpass_time_constant_s’: 10}

  • Impedance threshold: 10 kOhm

Participants

  • Number of subjects: 10

  • Health status: healthy

  • Age: mean=25.6, std=2.5

  • Gender distribution: male=8, female=2

  • Handedness: {‘right’: 8}

  • BCI experience: mixed

  • Species: human

Experimental Protocol

  • Paradigm: imagery

  • Task type: motor_imagery

  • Number of classes: 2

  • Class labels: right_hand, left_hand

  • Trial duration: 10 s

  • Tasks: motor_imagery

  • Study design: two-class motor imagery with arrow cues

  • Feedback type: none

  • Stimulus type: arrow_cue

  • Stimulus modalities: visual

  • Primary modality: visual

  • Synchronicity: synchronous

  • Mode: offline

  • Instructions: Subjects were instructed to perform haptic motor imagery of the left or the right hand during display of the arrow, as indicated by the direction of the arrow

HED Event Annotations

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

right_hand

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

left_hand

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

Paradigm-Specific Parameters

  • Detected paradigm: motor_imagery

  • Imagery tasks: left_hand, right_hand

  • Cue duration: 7.0 s

  • Imagery duration: 7.0 s

Data Structure

  • Trials: 150

  • Trials context: per_class

Preprocessing

  • Data state: preprocessed

  • Preprocessing applied: True

  • Artifact methods: none

  • Re-reference: car

  • Notes: No trials were rejected and no artifact correction was performed. Data were re-referenced to common average reference offline.

Signal Processing

  • Classifiers: Logistic Regression

  • Feature extraction: CSP, Beamforming, Laplacian, Bandpower

  • Frequency bands: analyzed=[7.0, 30.0] Hz

  • Spatial filters: CSP, Beamforming, Laplacian

Cross-Validation

  • Method: bootstrapping

  • Evaluation type: within_subject

BCI Application

  • Applications: motor_control

  • Environment: shielded_room

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: Motor

  • Type: Motor

Documentation

  • DOI: 10.1109/TBME.2008.2009768

  • License: CC-BY-4.0

  • Investigators: Moritz Grosse-Wentrup, Christian Liefhold, Klaus Gramann, Martin Buss

  • Senior author: Martin Buss

  • Contact: moritzgw@ieee.org

  • Institution: Technische Universität München

  • Department: Institute of Automatic Control Engineering (LSR)

  • Country: DE

  • Repository: Zenodo

  • Publication year: 2009

  • Keywords: Beamforming, brain-computer interfaces, common spatial patterns, electroencephalography, motor imagery, spatial filtering

References

Grosse-Wentrup, Moritz, et al. “Beamforming in noninvasive brain–computer interfaces.” IEEE Transactions on Biomedical Engineering 56.4 (2009): 1209-1219. 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

NM000145

Title

Munich Motor Imagery dataset

Author (year)

GrosseWentrup2009

Canonical

—

Importable as

NM000145, GrosseWentrup2009

Year

2009

Authors

Moritz Grosse-Wentrup, Christian Liefhold, Klaus Gramann, Martin Buss

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: 10

  • Recordings: 10

  • Tasks: 1

Channels & sampling rate

  • Channels: 128

  • Sampling rate (Hz): 500.0

  • Duration (hours): 8.404805555555555

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 5.4 GB

  • File count: 10

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000145 class to access this dataset programmatically.

class eegdash.dataset.NM000145(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Munich Motor Imagery dataset

Study:

nm000145 (NeMAR)

Author (year):

GrosseWentrup2009

Canonical:

—

Also importable as: NM000145, GrosseWentrup2009.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 10; recordings: 10; 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/nm000145 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000145

Examples

>>> from eegdash.dataset import NM000145
>>> dataset = NM000145(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#