EEGdash›NeMAR›NM000315

Iss. 315 · 10 subjects · 110 recordings · CC BY 4.0

Dataset Brief · Shin2022

NM000315: eeg dataset, 10 subjects#

Shin2022: Closed-loop 1D left/right motor imagery EEG dataset (live recordings)

Access recordings and metadata through EEGDash.

Citation: Hyonyoung Shin, Daniel Suma, Bin He (2019). Shin2022: Closed-loop 1D left/right motor imagery EEG dataset (live recordings). 10.82901/nemar.nm000315

Modality: eeg Subjects: 10 Recordings: 110 License: CC BY 4.0 Source: nemar

Metadata: Complete (100%)

10-participant EEG dataset — Shin2022: Closed-loop 1D left/right motor imagery EEG dataset (live recordings).

Data & curation Hyonyoung Shin · Daniel Suma · Bin He
Senior author Bin He
Year 2019 · Distributed via NeMAR

EEG · 16 ch250 HzBIDS 1.9.0Task · imagery

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 NM000315

dataset = NM000315(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 = NM000315(cache_dir="./data", subject="01")

Advanced query

dataset = NM000315(
    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{nm000315,
  title = {Shin2022: Closed-loop 1D left/right motor imagery EEG dataset (live recordings)},
  author = {Hyonyoung Shin and Daniel Suma and Bin He},
  doi = {10.82901/nemar.nm000315},
  url = {https://doi.org/10.82901/nemar.nm000315},
}

§ 02Study · The README

About This Dataset#

Closed-loop 1D left/right motor imagery EEG dataset (live recordings).

Code: Shin2022

Paradigm: imagery DOI: 10.3389/fnhum.2022.951591 Subjects: 10 Sessions per subject: 1 Events: right_hand=1, left_hand=2 Trial interval: [0, 3.0] s Runs per session: 11 File format: BCI2000

DOI

Shin2022

Acquisition

Sampling rate: 250.0 Hz Number of channels: 16 Channel types: eeg=16 Montage: standard_1005

View full README

DOI

Shin2022

Acquisition

Sampling rate: 250.0 Hz Number of channels: 16 Channel types: eeg=16 Montage: standard_1005 Hardware: g.tec g.Nautilus RESEARCH (16 channel) Software: BCI2000 Reference: unknown Sensor type: dry Line frequency: 60.0 Hz Online filters: broadband (online 0.5-30 Hz filter disabled at source) Cap manufacturer: g.tec medical engineering GmbH Cap model: g.SAHARA dry electrode system Electrode type: wire

Participants

Number of subjects: 10 Health status: healthy Species: human

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: right_hand, left_hand Trial duration: 6.0 s Study design: Closed-loop 1D left/right (LR) center-out SMR cursor control. Left- vs right-hand motor imagery moves a cursor toward a left or right target under continuous visual feedback. 24 trials per run (12 left / 12 right), 11 runs per subject sweeping online control parameters (BW, CV, NT); one recording day per subject. Feedback type: visual Stimulus type: visual cursor Stimulus modalities: visual Primary modality: visual Synchronicity: synchronous Mode: online Instructions: Imagine left- or right-hand movement to drive the cursor toward the cued left or right target.

HED Event Annotations

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

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

left_hand

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

Paradigm-Specific Parameters

Detected paradigm: motor_imagery Imagery tasks: right_hand, left_hand

Data Structure

Trials: 2640 Trials per class: right_hand=12, left_hand=12 Trials context: 10 subjects x 11 runs x 24 trials (12 left / 12 right)

Preprocessing

Data state: continuous

Signal Processing

Feature extraction: autoregressive_spectrum, alpha_band_power Frequency bands: alpha=[8.0, 12.0] Hz Spatial filters: s, m, a, l, l, , s, u, r, f, a, c, e, , L, a, p, l, a, c, i, a, n, , a, r, o, u, n, d, , C, 3, , a, n, d, , C, 4

Cross-Validation

Evaluation type: within_subject

BCI Application

Applications: cursor_control Environment: laboratory Online feedback: True

Tags

Pathology: Healthy Modality: Motor Type: Research

Documentation

DOI: 10.3389/fnhum.2022.951591 Associated paper DOI: 10.3389/fnhum.2022.951591 License: CC BY 4.0 Investigators: Hyonyoung Shin, Daniel Suma, Bin He Senior author: Bin He Institution: Carnegie Mellon University Department: Department of Biomedical Engineering Country: US Repository: Figshare Data URL: https://doi.org/10.6084/m9.figshare.20383716 Publication year: 2022 Keywords: motor imagery, EEG, sensorimotor rhythm, brain-computer interface, closed-loop, cursor control, dry electrodes

References

H. Shin, D. Suma and B. He, “Closed-loop motor imagery EEG simulation for brain-computer interfaces,” Frontiers in Human Neuroscience, vol. 16, 951591, 2022. DOI: 10.3389/fnhum.2022.951591 H. Shin, D. Suma and B. He, “Data from: Closed-loop motor imagery EEG simulation for brain-computer interfaces,” figshare, 2022. DOI: 10.6084/m9.figshare.20383716 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 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

Ethics

Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB.

Please consult the primary publication for the exact IRB/ethics committee reference.

Ethics

Reviewed and approved by the Institutional Review Board of Carnegie Mellon University. All participants provided written informed consent to participate in the study (Shin, Suma & He 2022, Front. Hum. Neurosci. 16:951591, DOI 10.3389/fnhum.2022.951591).

Verbatim from the source:

The studies involving human participants were reviewed and approved by the Institutional Review Board of Carnegie Mellon University. The patients/participants provided their written informed consent to participate in this study.

Source: cached paper .paper-audit/Shin2022/paper-10_3389_fnhum_2022_951591.txt (Shin, Suma & He 2022, Front. Hum. Neurosci. 16:951591, DOI 10.3389/fnhum.2022.951591).

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000315-blue)](https://doi.org/10.82901/nemar.nm000315) Shin2022 ======== Closed-loop 1D left/right motor imagery EEG dataset (live recordings). Dataset Overview —————-

Code: Shin2022 Paradigm: imagery DOI: 10.3389/fnhum.2022.951591 Subjects: 10 Sessions per subject: 1 Events: right_hand=1, left_hand=2 Trial interval: [0, 3.0] s Runs per session: 11 File format: BCI2000

Acquisition#

Sampling rate: 250.0 Hz Number of channels: 16 Channel types: eeg=16 Montage: standard_1005 Hardware: g.tec g.Nautilus RESEARCH (16 channel) Software: BCI2000 Reference: unknown Sensor type: dry Line frequency: 60.0 Hz Online filters: broadband (online 0.5-30 Hz filter disabled at source) Cap manufacturer: g.tec medical engineering GmbH Cap model: g.SAHARA dry electrode system Electrode type: wire

Participants#

Number of subjects: 10 Health status: healthy Species: human

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: right_hand, left_hand Trial duration: 6.0 s Study design: Closed-loop 1D left/right (LR) center-out SMR cursor control. Left- vs right-hand motor imagery moves a cursor toward a left or right target under continuous visual feedback. 24 trials per run (12 left / 12 right), 11 runs per subject sweeping online control parameters (BW, CV, NT); one recording day per subject. Feedback type: visual Stimulus type: visual cursor Stimulus modalities: visual Primary modality: visual Synchronicity: synchronous Mode: online Instructions: Imagine left- or right-hand movement to drive the cursor toward the cued left or right target.

HED Event Annotations#

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

├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Right, Hand

left_hand
├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Left, Hand

Paradigm-Specific Parameters#

Detected paradigm: motor_imagery Imagery tasks: right_hand, left_hand

Data Structure#

Trials: 2640 Trials per class: right_hand=12, left_hand=12 Trials context: 10 subjects x 11 runs x 24 trials (12 left / 12 right)

Preprocessing#

Data state: continuous

Signal Processing#

Feature extraction: autoregressive_spectrum, alpha_band_power Frequency bands: alpha=[8.0, 12.0] Hz Spatial filters: s, m, a, l, l, , s, u, r, f, a, c, e, , L, a, p, l, a, c, i, a, n, , a, r, o, u, n, d, , C, 3, , a, n, d, , C, 4

Cross-Validation#

Evaluation type: within_subject

BCI Application#

Applications: cursor_control Environment: laboratory Online feedback: True

Tags#

Pathology: Healthy Modality: Motor Type: Research

Documentation#

DOI: 10.3389/fnhum.2022.951591 Associated paper DOI: 10.3389/fnhum.2022.951591 License: CC BY 4.0 Investigators: Hyonyoung Shin, Daniel Suma, Bin He Senior author: Bin He Institution: Carnegie Mellon University Department: Department of Biomedical Engineering Country: US Repository: Figshare Data URL: https://doi.org/10.6084/m9.figshare.20383716 Publication year: 2022 Keywords: motor imagery, EEG, sensorimotor rhythm, brain-computer interface, closed-loop, cursor control, dry electrodes

References#

H. Shin, D. Suma and B. He, “Closed-loop motor imagery EEG simulation for brain-computer interfaces,” Frontiers in Human Neuroscience, vol. 16, 951591, 2022. DOI: 10.3389/fnhum.2022.951591 H. Shin, D. Suma and B. He, “Data from: Closed-loop motor imagery EEG simulation for brain-computer interfaces,” figshare, 2022. DOI: 10.6084/m9.figshare.20383716 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 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb Ethics —— Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB. Please consult the primary publication for the exact IRB/ethics committee reference. ## Ethics Reviewed and approved by the Institutional Review Board of Carnegie Mellon University. All participants provided written informed consent to participate in the study (Shin, Suma & He 2022, Front. Hum. Neurosci. 16:951591, DOI 10.3389/fnhum.2022.951591). Verbatim from the source: > The studies involving human participants were reviewed and approved by the Institutional Review Board of Carnegie Mellon University. The patients/participants provided their written informed consent to participate in this study. Source: cached paper .paper-audit/Shin2022/paper-10_3389_fnhum_2022_951591.txt (Shin, Suma & He 2022, Front. Hum. Neurosci. 16:951591, DOI 10.3389/fnhum.2022.951591).

License: CC BY 4.0

Authors:

Hyonyoung Shin
Daniel Suma
Bin He

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000315

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 16 ch (n=110 recordings)

Sampling frequencies: 250.0 Hz (n=110 recordings)

Total recording duration: 7 h 28 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 250 Hz · 10 subjects, 110 recordings

Live trace viewer — sub-7 · ses-0 · task-imagery · run-2

Showing one representative recording out of 10 subjects and 110 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.

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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.NM000315(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000315

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

class eegdash.dataset.NM000315(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Shin2022: Closed-loop 1D left/right motor imagery EEG dataset (live recordings)

Study:: nm000315 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000315.

Modality: eeg; Subject type: Unknown. Subjects: 10; recordings: 110; 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/nm000315 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000315 DOI: https://doi.org/10.82901/nemar.nm000315

Examples

>>> from eegdash.dataset import NM000315
>>> dataset = NM000315(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 — NM000315.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 nm000315 to reproduce the tutorial on this dataset.

Citation

Hyonyoung Shin, Daniel Suma, Bin He (2019). Shin2022: Closed-loop 1D left/right motor imagery EEG dataset (live recordings). 10.82901/nemar.nm000315

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000315.

Related & sibling datasets

NM000300EEG · 10 subj NM000294EEG · 12 subj NM000302EEG · 6 subj NM000320EEG · 10 subj NM000291EEG · 14 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC BY 4.0 · 10.82901/nemar.nm000315

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000315`
Title	Shin2022: Closed-loop 1D left/right motor imagery EEG dataset (live recordings)
Author (year)	—
Canonical	—
Importable as	`NM000315`
Year	2019
Authors	Hyonyoung Shin, Daniel Suma, Bin He
License	CC BY 4.0
Citation / DOI	10.82901/nemar.nm000315
Source links	OpenNeuro \| NeMAR \| Source URL

NM000315: eeg dataset, 10 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Paradigm-Specific Parameters#

Data Structure#

Preprocessing#

Signal Processing#

Cross-Validation#

BCI Application#

Tags#

Documentation#

References#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#