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. 10.82901/nemar.nm000145

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

Metadata: Complete (100%)

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},
}

About This Dataset#

DOI

Munich Motor Imagery dataset

Munich Motor Imagery dataset.

Dataset Overview

Code: GrosseWentrup2009
Paradigm: imagery

View full README

DOI

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) 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	10.82901/nemar.nm000145
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

Electrode Layout#

Electrode layout — EEG · 128 sensors — 128 channels

Dataset Statistics#

Age distribution (n=10, range 25–25 yr)

25

Channel counts: 128 ch (n=10 recordings)

Sampling frequencies: 500.0 Hz (n=10 recordings)

Total recording duration: 8 h 24 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

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 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)