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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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) 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

Electrode Layout#

Electrode layout — EEG · 64 sensors — 64 channels

Dataset Statistics#

Age distribution (n=25, range 23–29 yr)

2025

Sex distribution

7

18

Female Male Total: 25

Channel counts (ch)

6264

Sampling frequencies: 1000.0 Hz (n=375 recordings)

Total recording duration: 35 h

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 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: 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)