NM000167: eeg dataset, 25 subjects#

Motor imagery dataset from Ma et al. 2020

Access recordings and metadata through EEGDash.

Citation: Xuelin Ma, Shuang Qiu, Changde Du, Junfeng Xing, Huiguang He (2019). Motor imagery dataset from Ma et al. 2020.

Modality: eeg Subjects: 25 Recordings: 375 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000167

dataset = NM000167(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000167(cache_dir="./data", subject="01")

Advanced query

dataset = NM000167(
    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{nm000167,
  title = {Motor imagery dataset from Ma et al. 2020},
  author = {Xuelin Ma and Shuang Qiu and Changde Du and Junfeng Xing and Huiguang He},
}

About This Dataset#

Motor imagery dataset from Ma et al. 2020

Motor imagery dataset from Ma et al. 2020.

Dataset Overview

  • Code: Ma2020

  • Paradigm: imagery

  • DOI: 10.1038/s41597-020-0535-2

View full README

Motor imagery dataset from Ma et al. 2020

Motor imagery dataset from Ma et al. 2020.

Dataset Overview

  • Code: Ma2020

  • Paradigm: imagery

  • DOI: 10.1038/s41597-020-0535-2

  • Subjects: 25

  • Sessions per subject: 15

  • Events: right_hand=1, right_elbow=2

  • Trial interval: [0, 4] s

  • File format: CNT

Acquisition

  • Sampling rate: 1000.0 Hz

  • Number of channels: 62

  • Channel types: eeg=62

  • Channel names: Fp1, Fpz, Fp2, AF3, AF4, F7, F5, F3, F1, Fz, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCz, FC2, FC4, FC6, FT8, T7, C5, C3, C1, Cz, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO5, PO3, POz, PO4, PO6, PO8, CB1, O1, Oz, O2, CB2

  • Montage: standard_1005

  • Hardware: Neuroscan SynAmps2

  • Ground: AFz

  • Line frequency: 50.0 Hz

  • Impedance threshold: 5 kOhm

  • Auxiliary channels: EOG (2 ch, horizontal, vertical), M2

Participants

  • Number of subjects: 25

  • Health status: healthy

  • Age: mean=25.56, min=23, max=29

  • Gender distribution: male=18, female=7

  • Handedness: {‘right’: 25}

  • BCI experience: naive

Experimental Protocol

  • Paradigm: imagery

  • Task type: motor_imagery_same_limb

  • Number of classes: 2

  • Class labels: right_hand, right_elbow

  • Trial duration: 4.0 s

  • Feedback type: none

  • Stimulus type: visual cue

  • Stimulus modalities: visual

  • Primary modality: visual

  • Synchronicity: synchronous

  • Mode: offline

  • Training/test split: False

  • Instructions: Subjects were asked to concentrate on performing the indicated motor imagery task (right hand or right elbow) using kinesthetic, not visual, motor imagery while avoiding any motion during imagination.

HED Event Annotations

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

right_hand

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

right_elbow

├─ Sensory-event
└─ Label/right_elbow

Paradigm-Specific Parameters

  • Detected paradigm: motor_imagery

  • Imagery tasks: right_hand, right_elbow

  • Cue duration: 1.0 s

  • Imagery duration: 4.0 s

Data Structure

  • Trials: 600

  • Trials per class: right_hand=300, right_elbow=300

  • Blocks per session: 15

  • Trials context: 3 days x 5 MI sessions/day = 15 sessions, 40 trials/session (20 hand + 20 elbow)

Signal Processing

  • Classifiers: FBCSP+SVM

  • Feature extraction: FBCSP

  • Frequency bands: alpha=[8.0, 13.0] Hz; beta=[20.0, 25.0] Hz

  • Spatial filters: CAR, FBCSP

Cross-Validation

  • Method: 5-fold

  • Folds: 5

  • Evaluation type: within_subject

BCI Application

  • Applications: motor_rehabilitation, prosthetic_control

  • Environment: laboratory

  • Online feedback: False

Tags

  • Pathology: healthy

  • Modality: motor

  • Type: imagery

Documentation

  • DOI: 10.1038/s41597-020-0535-2

  • License: CC-BY-4.0

  • Investigators: Xuelin Ma, Shuang Qiu, Changde Du, Junfeng Xing, Huiguang He

  • Senior author: Huiguang He

  • Institution: Chinese Academy of Sciences

  • Department: Institute of Automation

  • Country: CN

  • Repository: Harvard Dataverse

  • Data URL: https://doi.org/10.7910/DVN/RBN3XG

  • Publication year: 2020

  • Funding: National Key Research and Development Plan of China (No. 2017YFB1002502); National Natural Science Foundation of China (No. 61976209); National Natural Science Foundation of China (No. 61906188)

  • Ethics approval: Ethics Committee of the Institute of Automation, Chinese Academy of Sciences

  • Keywords: motor imagery, EEG, BCI, same limb, hand, elbow

References

X. Ma, S. Qiu, C. Du, J. Xing, and H. He, “Multi-channel EEG recording during motor imagery of different joints from the same limb,” Scientific Data, vol. 7, no. 1, p. 191, 2020. DOI: 10.1038/s41597-020-0535-2 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

NM000167

Title

Motor imagery dataset from Ma et al. 2020

Author (year)

Ma2020

Canonical

—

Importable as

NM000167, Ma2020

Year

2019

Authors

Xuelin Ma, Shuang Qiu, Changde Du, Junfeng Xing, Huiguang He

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: 25

  • Recordings: 375

  • Tasks: 1

Channels & sampling rate

  • Channels: 64 (225), 62 (150)

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 35.204795833333336

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 22.4 GB

  • File count: 375

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000167 class to access this dataset programmatically.

class eegdash.dataset.NM000167(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Motor imagery dataset from Ma et al. 2020

Study:

nm000167 (NeMAR)

Author (year):

Ma2020

Canonical:

—

Also importable as: NM000167, Ma2020.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 25; recordings: 375; 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/nm000167 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000167

Examples

>>> from eegdash.dataset import NM000167
>>> dataset = NM000167(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#