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#

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)

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'])

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) https://github.com/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

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 6.7 GB

  • File count: 52

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

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, 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#