EEGdash›NeMAR›NM000311

Iss. 311 · 25 subjects · 213 recordings · CC0-1.0

Dataset Brief · Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)

NM000311: eeg dataset, 25 subjects#

Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)

Citation: Ji-Hoon Jeong, Jeong-Hyun Cho, Kyung-Hwan Shim, Byoung-Hee Kwon, Byeong-Hoo Lee, Do-Yeun Lee, Dae-Hyeok Lee, Seong-Whan Lee (2020). Multimodal upper-limb MI/ME EEG (Jeong et al. 2020). 10.82901/nemar.nm000311

25-participant EEG dataset — Multimodal upper-limb MI/ME EEG (Jeong et al. 2020).

Data & curation Ji-Hoon Jeong · Jeong-Hyun Cho · Kyung-Hwan Shim · Byoung-Hee Kwon · Byeong-Hoo Lee · Do-Yeun Lee · …
Year 2020 · Distributed via NeMAR

EEG · 71 ch1000 HzBIDS 1.9.0Task · imagery3 sessionsHealthyVisualMotor

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000311

dataset = NM000311(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 = NM000311(cache_dir="./data", subject="01")

Advanced query

dataset = NM000311(
    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{nm000311,
  title = {Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)},
  author = {Ji-Hoon Jeong and Jeong-Hyun Cho and Kyung-Hwan Shim and Byoung-Hee Kwon and Byeong-Hoo Lee and Do-Yeun Lee and Dae-Hyeok Lee and Seong-Whan Lee},
  doi = {10.82901/nemar.nm000311},
  url = {https://doi.org/10.82901/nemar.nm000311},
}

§ 02Study · The README

About This Dataset#

Multimodal MI+ME dataset from Jeong et al 2020.

Code: Jeong2020

Paradigm: imagery DOI: 10.1093/gigascience/giaa098 Subjects: 25 Sessions per subject: 3 Events: reach_forward=1, reach_backward=2, reach_left=3, reach_right=4, reach_up=5, reach_down=6, grasp_cup=7, grasp_ball=8, grasp_card=9, twist_pronation=10, twist_supination=11 Trial interval: [0, 4] s Runs per session: 3 File format: BrainVision

DOI

Jeong2020

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 71 Channel types: eeg=60, eog=4, emg=7 Channel names: Fp1, AF7, AF3, AFz, F7, F5, F3, F1, Fz, FT7, FC5, FC3, FC1, T7, C5, C3, C1, Cz, TP7, CP5, CP3, CP1, CPz, P7, P5, P3, P1, Pz, PO7, PO3, POz, Fp2, AF4, AF8, F2, F4, F6, F8, FC2, FC4, FC6, FT8, C2, C4, C6, T8, CP2, CP4, CP6, TP8, P2, P4, P6, P8, PO4, PO8, O1, Oz, O2, Iz
View full README
DOI

Jeong2020

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 71 Channel types: eeg=60, eog=4, emg=7 Channel names: Fp1, AF7, AF3, AFz, F7, F5, F3, F1, Fz, FT7, FC5, FC3, FC1, T7, C5, C3, C1, Cz, TP7, CP5, CP3, CP1, CPz, P7, P5, P3, P1, Pz, PO7, PO3, POz, Fp2, AF4, AF8, F2, F4, F6, F8, FC2, FC4, FC6, FT8, C2, C4, C6, T8, CP2, CP4, CP6, TP8, P2, P4, P6, P8, PO4, PO8, O1, Oz, O2, Iz Montage: standard_1005 Hardware: BrainAmp (BrainProducts GmbH) Reference: FCz Ground: Fpz Sensor type: actiCap Line frequency: 60.0 Hz Online filters: {‘highpass’: 0.016, ‘lowpass’: 1000}

Participants

Number of subjects: 25 Health status: healthy Age: min=24.0, max=32.0 Gender distribution: female=10, male=15 Handedness: right-handed BCI experience: naive Species: human

Experimental Protocol

Paradigm: imagery Number of classes: 11 Class labels: reach_forward, reach_backward, reach_left, reach_right, reach_up, reach_down, grasp_cup, grasp_ball, grasp_card, twist_pronation, twist_supination Trial duration: 4.0 s Study design: 11 intuitive upper-limb movement tasks: 6 reaching + 3 grasping + 2 wrist twisting. MI and real movement conditions, 3 sessions. Feedback type: none Stimulus type: text cues Stimulus modalities: visual Primary modality: visual Synchronicity: synchronous Mode: offline

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser reach_forward
     ├─ Sensory-event
     └─ Label/reach_forward

reach_backward
     ├─ Sensory-event
     └─ Label/reach_backward

reach_left
     ├─ Sensory-event
     └─ Label/reach_left

reach_right
     ├─ Sensory-event
     └─ Label/reach_right

reach_up
     ├─ Sensory-event
     └─ Label/reach_up

reach_down
     ├─ Sensory-event
     └─ Label/reach_down

grasp_cup
     ├─ Sensory-event
     └─ Label/grasp_cup

grasp_ball
     ├─ Sensory-event
     └─ Label/grasp_ball

grasp_card
     ├─ Sensory-event
     └─ Label/grasp_card

twist_pronation
     ├─ Sensory-event
     └─ Label/twist_pronation

twist_supination
├─ Sensory-event
└─ Label/twist_supination
Paradigm-Specific Parameters

Detected paradigm: motor_imagery Imagery tasks: reach_forward, reach_backward, reach_left, reach_right, reach_up, reach_down, grasp_cup, grasp_ball, grasp_card, twist_pronation, twist_supination Imagery duration: 4.0 s

Data Structure

Trials: 41250 Trials context: 25 subjects x 3 sessions x 550 trials (300 reaching + 150 grasping + 100 twisting)

Signal Processing

Classifiers: CSP+RLDA Feature extraction: CSP Frequency bands: mu_beta=[8.0, 30.0] Hz Spatial filters: CSP

Cross-Validation

Method: 10x10-fold Folds: 10 Evaluation type: within_session

BCI Application

Applications: motor_control, prosthetics Environment: laboratory Online feedback: False

Tags

Pathology: Healthy Modality: Motor Type: Research

Documentation

DOI: 10.1093/gigascience/giaa098 License: CC0-1.0 Investigators: Ji-Hoon Jeong, Jeong-Hyun Cho, Kyung-Hwan Shim, Byoung-Hee Kwon, Byeong-Hoo Lee, Do-Yeun Lee, Dae-Hyeok Lee, Seong-Whan Lee Institution: Korea University Country: KR Data URL: https://zenodo.org/records/19021436 Publication year: 2020

References

Jeong, J.-H., Cho, J.-H., Shim, K.-H., et al. (2020). Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions. GigaScience, 9(10), giaa098. https://doi.org/10.1093/gigascience/giaa098 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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 71 ch (n=213 recordings)

Sampling frequencies: 1000.0 Hz (n=213 recordings)

Total recording duration: 124 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 71 ch · EEG · 1000 Hz · 25 subjects, 213 recordings

Live trace viewer — sub-1 · ses-0 · task-imagery · run-0

Showing one representative recording out of 25 subjects and 213 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 60 sensors — 60 channels

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

§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000311(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Jeong2020

Canonical—

Importable asNM000311 · Jeong2020

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000311(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)

Study:: nm000311 (NeMAR)
Author (year):: Jeong2020
Canonical:: —

Also importable as: NM000311, Jeong2020.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 25; recordings: 213; 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/nm000311 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000311 DOI: https://doi.org/10.82901/nemar.nm000311

Examples

>>> from eegdash.dataset import NM000311
>>> dataset = NM000311(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)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000311.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000311 to reproduce the tutorial on this dataset.

Citation

Ji-Hoon Jeong, Jeong-Hyun Cho, Kyung-Hwan Shim, Byoung-Hee Kwon, Byeong-Hoo Lee, … (2020). Multimodal upper-limb MI/ME EEG (Jeong et al. 2020). 10.82901/nemar.nm000311

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000311.

Related & sibling datasets

NM000209EEG · 25 subj NM000160EEG · 18 subj NM000265EEG · 31 subj NM000167EEG · 25 subj NM000227EEG · 31 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0-1.0 · 10.82901/nemar.nm000311

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000311`
Title	Multimodal upper-limb MI/ME EEG (Jeong et al. 2020)
Author (year)	`Jeong2020`
Canonical	—
Importable as	`NM000311`, `Jeong2020`
Year	2020
Authors	Ji-Hoon Jeong, Jeong-Hyun Cho, Kyung-Hwan Shim, Byoung-Hee Kwon, Byeong-Hoo Lee, Do-Yeun Lee, Dae-Hyeok Lee, Seong-Whan Lee
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000311
Source links	OpenNeuro \| NeMAR \| Source URL

NM000311: eeg dataset, 25 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#