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nm000311 NEMAR-native dataset

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

A multimodal EEG dataset comprising motor imagery and motor execution data from 25 healthy subjects performing 11 intuitive upper-limb movement tasks (6 reaching, 3 grasping, 2 wrist twisting) across 3 sessions. The dataset includes 71-channel EEG recordings (60 EEG + 4 EOG + 7 EMG channels) sampled at 1000 Hz, designed for brain-computer interface research and motor control applications.

EEG
Download zip Issues GitHub

Compute on this dataset

Two routes today, with a third (in-browser one-click submission) landing soon.

  1. NeuroScience Gateway (NSG) portal.

    NSG runs EEGLAB / Brainstorm / MNE pipelines on supercomputing time donated by SDSC. Create an account, point a job at this dataset's S3 prefix (s3://nemar/nm000311), and submit.
    nsgportal.org →

  2. Local processing with nemar-cli.

    Pull the dataset to your machine and run any toolbox locally. Honors the published version pinning. 

    npm install -g nemar-cli
nemar dataset clone nm000311
cd nm000311 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000311/ — the manifest carries presigned S3 URLs.

Direct compute access is coming soon. One-click NSG submission from this page is scoped for a follow-up phase. Tracked on nemarOrg/website#6.
DOI 10.82901/nemar.nm000311

![DOI](https://doi.org/10.82901/nemar.nm000311)

Jeong2020 =========

Multimodal MI+ME dataset from Jeong et al 2020.

Dataset Overview

Code: Jeong2020 Paradigm: imagery DOI: 10.1093/gigascience/giaa098 Subjects: 25 Sessions per subject: 3 Events: reachforward=1, reachbackward=2, reachleft=3, reachright=4, reachup=5, reachdown=6, graspcup=7, graspball=8, graspcard=9, twistpronation=10, twist_supination=11 Trial interval: [0, 4] s Runs per session: 3 File format: BrainVision

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: reachforward, reachbackward, reachleft, reachright, reachup, reachdown, graspcup, graspball, graspcard, twistpronation, 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

reachforward ├─ Sensory-event └─ Label/reachforward

reachbackward ├─ Sensory-event └─ Label/reachbackward

reachleft ├─ Sensory-event └─ Label/reachleft

reachright ├─ Sensory-event └─ Label/reachright

reachup ├─ Sensory-event └─ Label/reachup

reachdown ├─ Sensory-event └─ Label/reachdown

graspcup ├─ Sensory-event └─ Label/graspcup

graspball ├─ Sensory-event └─ Label/graspball

graspcard ├─ Sensory-event └─ Label/graspcard

twistpronation ├─ Sensory-event └─ Label/twistpronation

twistsupination ├─ Sensory-event └─ Label/twistsupination

Paradigm-Specific Parameters

Detected paradigm: motorimagery Imagery tasks: reachforward, reachbackward, reachleft, reachright, reachup, reachdown, graspcup, graspball, graspcard, twistpronation, twistsupination 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) https://github.com/NeuroTechX/moabb

Files

31 top-level entries · 88.6 GB total