NM000338: eeg dataset, 54 subjects#

Name: Lee2019-MI
Published: 2019
License: GPL-3.0

Lee2019-MI

Access recordings and metadata through EEGDash.

Citation: Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee (2019). Lee2019-MI. 10.1093/gigascience/giz002

Modality: eeg Subjects: 54 Recordings: 216 License: GPL-3.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000338

dataset = NM000338(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 = NM000338(cache_dir="./data", subject="01")

Advanced query

dataset = NM000338(
    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{nm000338,
  title = {Lee2019-MI},
  author = {Min-Ho Lee and O-Yeon Kwon and Yong-Jeong Kim and Hong-Kyung Kim and Young-Eun Lee and John Williamson and Siamac Fazli and Seong-Whan Lee},
  doi = {10.1093/gigascience/giz002},
  url = {https://doi.org/10.1093/gigascience/giz002},
}

About This Dataset#

Lee2019-MI

BMI/OpenBMI dataset for MI.

Dataset Overview

Code: Lee2019-MI Paradigm: imagery DOI: 10.5524/100542

View full README

Lee2019-MI

BMI/OpenBMI dataset for MI.

Dataset Overview

Code: Lee2019-MI Paradigm: imagery DOI: 10.5524/100542 Subjects: 54 Sessions per subject: 2 Events: left_hand=2, right_hand=1 Trial interval: [0.0, 4.0] s File format: MAT

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 62 Channel types: eeg=62, emg=4 Channel names: AF3, AF4, AF7, AF8, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, EMG1, EMG2, EMG3, EMG4, F10, F3, F4, F7, F8, F9, FC1, FC2, FC3, FC4, FC5, FC6, FT10, FT9, FTT10h, FTT9h, Fp1, Fp2, Fz, O1, O2, Oz, P1, P2, P3, P4, P7, P8, PO10, PO3, PO4, PO9, POz, Pz, T7, T8, TP10, TP7, TP8, TP9, TPP10h, TPP8h, TPP9h, TTP7h Montage: standard_1005 Hardware: BrainAmp Reference: nasion Ground: AFz Sensor type: Ag/AgCl Line frequency: 60.0 Hz Impedance threshold: 10.0 kOhm Auxiliary channels: EMG (4 ch)

Participants

Number of subjects: 54 Health status: healthy Age: min=24, max=35 Gender distribution: female=25, male=29 Handedness: {‘right’: 50, ‘left’: 2, ‘ambidexter’: 2} BCI experience: mixed

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 4.0 s Tasks: MI Study design: Binary-class motor imagery (left/right hand grasping). Two sessions on different days, each with offline training and online test phases of 100 trials each. Feedback type: visual Stimulus type: arrow Stimulus modalities: visual Primary modality: visual Synchronicity: synchronous Mode: both Training/test split: True Instructions: Subjects performed the imagery task of grasping with the appropriate hand for 4 s when the right or left arrow appeared as a visual cue. First 3 s of each trial began with a black fixation cross to prepare subjects for the MI task. After each task, the screen remained blank for 6 s (± 1.5 s).

HED Event Annotations

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

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

right_hand

├─ Sensory-event
│  ├─ Experimental-stimulus
│  ├─ Visual-presentation
│  └─ Rightward, Arrow
└─ 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: 4.0 s

Data Structure

Trials: 200 Trials per class: left_hand=100, right_hand=100 Trials context: 100 trials per session per phase (50 per class per phase). Training: 50 left + 50 right. Test: 50 left + 50 right. Total per session: 200.

Preprocessing

Data state: raw Preprocessing applied: False

Signal Processing

Classifiers: CSP+LDA, CSSP, FBCSP, BSSFO Feature extraction: CSP, CSSP, FBCSP, BSSFO, log-variance Frequency bands: mu=[8.0, 12.0] Hz; analyzed=[8.0, 30.0] Hz Spatial filters: CSP, CSSP, FBCSP, BSSFO

Cross-Validation

Method: train-test split Evaluation type: within_session, cross_session

Performance (Original Study)

Accuracy: 71.1% Accuracy Std: 0.15 Illiteracy Rate: 53.7 Session1 Accuracy: 70.0 Session2 Accuracy: 72.2

BCI Application

Applications: motor_control Environment: laboratory Online feedback: True

Tags

Pathology: Healthy Modality: Motor Type: Research

Documentation

Description: EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. Includes MI, ERP, and SSVEP paradigms with a large number of subjects over multiple sessions. DOI: 10.1093/gigascience/giz002 License: GPL-3.0 Investigators: Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee Senior author: Seong-Whan Lee Contact: sw.lee@korea.ac.kr Institution: Korea University Department: Department of Brain and Cognitive Engineering Address: 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea Country: KR Repository: GigaDB Publication year: 2019 How to acknowledge: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Keywords: EEG datasets, brain-computer interface, event-related potential, steady-state visually evoked potential, motor-imagery, OpenBMI toolbox, BCI illiteracy

Abstract

Electroencephalography (EEG)-based brain-computer interface (BCI) systems are mainly divided into three major paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). Here, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. In addition, information about the psychological and physiological conditions of BCI users was obtained using a questionnaire, and task-unrelated parameters such as resting state, artifacts, and electromyography of both arms were also recorded. We evaluated the decoding accuracies for the individual paradigms and determined performance variations across both subjects and sessions. Furthermore, we looked for more general, severe cases of BCI illiteracy than have been previously reported in the literature. Average decoding accuracies across all subjects and sessions were 71.1% (± 0.15), 96.7% (± 0.05), and 95.1% (± 0.09), and rates of BCI illiteracy were 53.7%, 11.1%, and 10.2% for MI, ERP, and SSVEP, respectively. Compared to the ERP and SSVEP paradigms, the MI paradigm exhibited large performance variations between both subjects and sessions. Furthermore, we found that 27.8% (15 out of 54) of users were universally BCI literate, i.e., they were able to proficiently perform all three paradigms. Interestingly, we found no universally illiterate BCI user, i.e., all participants were able to control at least one type of BCI system.

Methodology

Experimental procedure: 54 healthy subjects participated in two sessions on different days. Each session consisted of three BCI paradigms performed sequentially: ERP speller (36 symbols, row-column presentation with face stimuli), MI task (binary left/right hand imagery), and SSVEP (four target frequencies: 5.45, 6.67, 8.57, 12 Hz). Each paradigm had offline training and online test phases. EEG recorded at 1000 Hz with 62 Ag/AgCl electrodes using BrainAmp amplifier, nose-referenced, grounded to AFz. Impedance maintained below 10 kOhm. Subjects seated 60 cm from 21-inch LCD monitor. Questionnaires collected demographic, physiological, and psychological data. Artifact data (eye blinking, eye movements, teeth clenching, arm flexing) and resting state EEG also recorded. Total experiment duration: ~205 minutes per session.

References

Lee, M. H., Kwon, O. Y., Kim, Y. J., Kim, H. K., Lee, Y. E., Williamson, J., … Lee, S. W. (2019). EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy. GigaScience, 8(5), 1–16. https://doi.org/10.1093/gigascience/giz002 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	`NM000338`
Title	Lee2019-MI
Author (year)	`Lee2019_MI`
Canonical	`OpenBMI_MI`
Importable as	`NM000338`, `Lee2019_MI`, `OpenBMI_MI`
Year	2019
Authors	Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee
License	GPL-3.0
Citation / DOI	doi:10.1093/gigascience/giz002
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: 54
Recordings: 216
Tasks: 1

Channels & sampling rate

Channels: 66
Sampling rate (Hz): 1000.0
Duration (hours): 91.54160666666668

API Reference#

Use the NM000338 class to access this dataset programmatically.

class eegdash.dataset.NM000338(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Lee2019-MI

Study:: nm000338 (NeMAR)
Author (year):: Lee2019_MI
Canonical:: OpenBMI_MI

Also importable as: NM000338, Lee2019_MI, OpenBMI_MI.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 54; recordings: 216; 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/nm000338 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000338 DOI: https://doi.org/10.1093/gigascience/giz002

Examples

>>> from eegdash.dataset import NM000338
>>> dataset = NM000338(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