EEGdash›NeMAR›NM000145

Iss. 145 · 10 subjects · 10 recordings · CC-BY-4.0

Dataset Brief · Munich Motor Imagery dataset

NM000145: eeg dataset, 10 subjects#

Munich Motor Imagery dataset

Citation: Moritz Grosse-Wentrup, Christian Liefhold, Klaus Gramann, Martin Buss (2009). Munich Motor Imagery dataset. 10.82901/nemar.nm000145

10-participant EEG dataset — Munich Motor Imagery dataset.

Data & curation Moritz Grosse-Wentrup · Christian Liefhold · Klaus Gramann · Martin Buss
Year 2009 · Distributed via NeMAR

EEG · 128 ch500 HzBIDS 1.9.0Task · imageryHealthyVisualMotor

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 NM000145

dataset = NM000145(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 = 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'])

Cite This Dataset

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},
  doi = {10.82901/nemar.nm000145},
  url = {https://doi.org/10.82901/nemar.nm000145},
}

§ 02Study · The README

About This Dataset#

Munich Motor Imagery dataset.

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

DOI

Munich Motor Imagery dataset

right_hand

View full README

DOI

Munich Motor Imagery dataset

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) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=10, range 26–26 yr, mean 25.0 yr)

25

Other · 10

Channel counts: 128 ch (n=10 recordings)

Sampling frequencies: 500.0 Hz (n=10 recordings)

Total recording duration: 8 h 24 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 128 ch · EEG · 500 Hz · 10 subjects, 10 recordings

Live trace viewer — sub-6 · ses-0 · task-imagery · run-0

Showing one representative recording out of 10 subjects and 10 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 · 128 sensors — 128 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.NM000145(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)GrosseWentrup2009

Canonical—

Importable asNM000145 · GrosseWentrup2009

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

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

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 DOI: https://doi.org/10.82901/nemar.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: 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 — NM000145.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 nm000145 to reproduce the tutorial on this dataset.

Citation

Moritz Grosse-Wentrup, Christian Liefhold, Klaus Gramann, Martin Buss (2009). Munich Motor Imagery dataset. 10.82901/nemar.nm000145

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000145.

Related & sibling datasets

NM000172EEG · 14 subj NM000138EEG · 8 subj NM000140EEG · 12 subj NM000162EEG · 20 subj NM000161EEG · 20 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.nm000145

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

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	10.82901/nemar.nm000145
Source links	OpenNeuro \| NeMAR \| Source URL

NM000145: eeg dataset, 10 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#