NM000245: eeg dataset, 52 subjects#

Motor Imagery dataset from Cho et al 2017

Access recordings and metadata through EEGDash.

Citation: Hohyun Cho, Minkyu Ahn, Sangtae Ahn, Moonyoung Kwon, Sung Chan Jun (2019). Motor Imagery dataset from Cho et al 2017.

Modality: eeg Subjects: 52 Recordings: 52 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000245

dataset = NM000245(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 = NM000245(cache_dir="./data", subject="01")

Advanced query

dataset = NM000245(
    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{nm000245,
  title = {Motor Imagery dataset from Cho et al 2017},
  author = {Hohyun Cho and Minkyu Ahn and Sangtae Ahn and Moonyoung Kwon and Sung Chan Jun},
}

About This Dataset#

Motor Imagery dataset from Cho et al 2017

Motor Imagery dataset from Cho et al 2017.

Dataset Overview

Code: Cho2017
Paradigm: imagery
DOI: 10.5524/100295

View full README

Motor Imagery dataset from Cho et al 2017

Motor Imagery dataset from Cho et al 2017.

Dataset Overview

Code: Cho2017
Paradigm: imagery
DOI: 10.5524/100295
Subjects: 52
Sessions per subject: 1
Events: left_hand=1, right_hand=2
Trial interval: [0, 3] s
File format: .mat (MATLAB)

Acquisition

Sampling rate: 512.0 Hz
Number of channels: 68
Channel types: eeg=64, emg=4
Channel names: AF3, AF4, AF7, AF8, AFz, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, EMG1, EMG2, EMG3, EMG4, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fp1, Fp2, Fpz, Fz, Iz, O1, O2, Oz, P1, P10, P2, P3, P4, P5, P6, P7, P8, P9, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP7, TP8
Montage: standard_1005
Hardware: Biosemi ActiveTwo
Software: BCI2000 3.0.2
Reference: CMS/DRL
Sensor type: active electrodes
Line frequency: 60.0 Hz
Electrode type: active
Auxiliary channels: EMG (4 ch)

Participants

Number of subjects: 52
Health status: healthy
Age: mean=24.8, std=3.86
Gender distribution: female=19, male=33
Handedness: {‘right’: 50, ‘both’: 2}
BCI experience: collected via questionnaire (0 = no, number = how many times)
Species: human

Experimental Protocol

Paradigm: imagery
Number of classes: 2
Class labels: left_hand, right_hand
Trial duration: 3.0 s
Study design: motor imagery
Feedback type: none
Stimulus type: visual instruction
Stimulus modalities: visual
Primary modality: visual
Mode: offline
Instructions: Subjects were asked to imagine kinesthetic finger movements (touching index, middle, ring, and little finger to thumb within 3 seconds)

HED Event Annotations

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

left_hand

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

right_hand

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

Paradigm-Specific Parameters

Detected paradigm: motor_imagery
Imagery tasks: left_hand, right_hand
Cue duration: 3.0 s
Imagery duration: 3.0 s

Data Structure

Trials: 100 or 120 per class (200-240 total)
Blocks per session: 5 or 6
Trials context: per_class

Preprocessing

Data state: raw
Preprocessing applied: False
Notes: Bad trial indices provided separately in .mat files (bad_trial_indices); raw EEG data is unfiltered

Signal Processing

Classifiers: FLDA
Feature extraction: CSP, ERD, ERS
Frequency bands: alpha=[8.0, 14.0] Hz; mu=[8, 12] Hz; analyzed=[8.0, 30.0] Hz

Cross-Validation

Method: random subset selection
Folds: 10
Evaluation type: within_session

Performance (Original Study)

Accuracy: 67.46%
Accuracy Std: 13.17
Discriminative Subjects: 38
Total Subjects: 50

BCI Application

Applications: motor_control
Online feedback: False

Tags

Pathology: Healthy
Modality: Motor
Type: Research

Documentation

Description: EEG datasets for motor imagery brain-computer interface from 52 subjects with psychological and physiological questionnaire, EMG datasets, 3D EEG electrode locations, and non-task-related states
DOI: 10.5524/100295
Associated paper DOI: 10.1093/gigascience/gix034
License: CC-BY-4.0
Investigators: Hohyun Cho, Minkyu Ahn, Sangtae Ahn, Moonyoung Kwon, Sung Chan Jun
Senior author: Sung Chan Jun
Contact: scjun@gist.ac.kr; TEL: +82-62-715-2216; FAX: +82-62-715-2204
Institution: Gwangju Institute of Science and Technology
Department: School of Electrical Engineering and Computer Science
Address: 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea
Country: KR
Repository: GigaDB
Data URL: http://dx.doi.org/10.5524/100295
Publication year: 2017
Funding: GIST Research Institute (GRI) grant funded by the GIST in 2017; Institute for Information & Communication Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451)
Ethics approval: Institutional Review Board of Gwangju Institute of Science and Technology
Keywords: motor imagery, EEG, brain-computer interface, performance variation, subject-to-subject transfer

Abstract

Motor imagery (MI)-based brain-computer interface (BCI) dataset from 52 subjects with EEG, EMG, psychological and physiological questionnaire, 3D EEG electrode locations, and non-task-related states. The dataset includes 100 or 120 trials per class (left/right hand) with validation showing 73.08% (38 subjects) had discriminative information. Mean accuracy of 67.46% (±13.17%) over 50 subjects (excluding 2 bad subjects). Dataset stored in GigaDB and validated using bad trial percentage, ERD/ERS analysis, and classification analysis.

Methodology

Subjects performed motor imagery of left and right hand finger movements (kinesthetic imagery). Each trial consisted of: 2 seconds fixation cross, 3 seconds instruction (left/right hand), followed by random 4.1-4.8 second break. Five or six runs performed with feedback after each run. Additional data collected: 6 types of non-task-related data (eye blinking, eyeball movements, head movement, jaw clenching, resting state) and 20 trials of real hand movement per class. 3D electrode coordinates measured with Polhemus Fastrak digitizer. Experiments conducted August-September 2011 in four time slots (9:30-12:00, 12:30-15:00, 15:30-18:00, 19:00-21:30) with background noise 37-39 dB.

References

Cho, H., Ahn, M., Ahn, S., Kwon, M. and Jun, S.C., 2017. EEG datasets for motor imagery brain computer interface. GigaScience. https://doi.org/10.1093/gigascience/gix034 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	`NM000245`
Title	Motor Imagery dataset from Cho et al 2017
Author (year)	`Cho2017`
Canonical	—
Importable as	`NM000245`, `Cho2017`
Year	2019
Authors	Hohyun Cho, Minkyu Ahn, Sangtae Ahn, Moonyoung Kwon, Sung Chan Jun
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: 52
Recordings: 52
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 512.0
Duration (hours): 20.45552734375

Electrode Layout#

Electrode layout — EEG · 64 sensors — 64 channels

Dataset Statistics#

Age distribution (n=52, range 24–24 yr)

20

Channel counts: 64 ch (n=52 recordings)

Sampling frequencies: 512.0 Hz (n=52 recordings)

Total recording duration: 20 h 27 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 NM000245 class to access this dataset programmatically.

class eegdash.dataset.NM000245(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Motor Imagery dataset from Cho et al 2017

Study:: nm000245 (NeMAR)
Author (year):: Cho2017
Canonical:: —

Also importable as: NM000245, Cho2017.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 52; recordings: 52; 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/nm000245 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000245

Examples

>>> from eegdash.dataset import NM000245
>>> dataset = NM000245(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)