EEGdash›NeMAR›NM000339

Iss. 339 · 62 subjects · 598 recordings · CC-BY-NC-4.0

Dataset Brief · Stieger et al. 2021 — Continuous sensorimotor rhythm based br…

NM000339: eeg dataset, 62 subjects#

Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population

Citation: James R. Stieger, Stephen A. Engel, Bin He (2021). Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population. 10.1038/s41597-021-00883-1

62-participant EEG dataset — Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population.

Data & curation James R. Stieger · Stephen A. Engel · Bin He
Year 2021 · Distributed via NeMAR
Funding NIH AT009263 · NIH EB021027 · + 3 more

EEG · 60 ch1000 HzBIDS 1.9.0Task · imagery11 sessionsHealthyVisualLearning

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 NM000339

dataset = NM000339(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 = NM000339(cache_dir="./data", subject="01")

Advanced query

dataset = NM000339(
    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{nm000339,
  title = {Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population},
  author = {James R. Stieger and Stephen A. Engel and Bin He},
  doi = {10.1038/s41597-021-00883-1},
  url = {https://doi.org/10.1038/s41597-021-00883-1},
}

§ 02Study · The README

About This Dataset#

Motor Imagery dataset from Stieger et al. 2021 [1]_.

Code: Stieger2021

Paradigm: imagery DOI: 10.1038/s41597-021-00883-1 Subjects: 62 Sessions per subject: 11 Events: right_hand=1, left_hand=2, both_hand=3, rest=4 Trial interval: [0, 3] s File format: MAT

Stieger2021

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 62 Channel types: eeg=62 Channel names: AF3, AF4, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fp1, Fp2, Fpz, Fz, O1, O2, Oz, P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO5, PO6, PO7, PO8, POz, Pz, T7, T8, TP7, TP8 Montage: 10-10 Hardware: Neuroscan SynAmps RT amplifiers
View full README
Stieger2021

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 62 Channel types: eeg=62 Channel names: AF3, AF4, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fp1, Fp2, Fpz, Fz, O1, O2, Oz, P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO5, PO6, PO7, PO8, POz, Pz, T7, T8, TP7, TP8 Montage: 10-10 Hardware: Neuroscan SynAmps RT amplifiers Software: Neuroscan Sensor type: EEG Line frequency: 60.0 Hz Online filters: 0.1 to 200 Hz with 60 Hz notch filter Impedance threshold: 5.0 kOhm Cap manufacturer: Neuroscan Cap model: Quik-Cap

Participants

Number of subjects: 62 Health status: healthy Age: min=18, max=63 Gender distribution: male=13, female=49 Handedness: mostly right-handed Species: human

Experimental Protocol

Paradigm: imagery Number of classes: 4 Class labels: right_hand, left_hand, both_hand, rest Tasks: LR, UD, 2D Study design: longitudinal training study with intervention Feedback type: visual Stimulus type: target_bar Stimulus modalities: visual Primary modality: visual Mode: online Instructions: Imagine your left (right) hand opening and closing to move the cursor left (right). Imagine both hands opening and closing to move the cursor up. Finally, to move the cursor down, voluntarily rest; in other words, clear your mind.

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

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

rest
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest
Paradigm-Specific Parameters

Detected paradigm: motor_imagery Imagery tasks: left_hand, right_hand, both_hands, rest Cue duration: 2.0 s Imagery duration: 6.0 s

Data Structure

Trials: 450 Blocks per session: 18 Trials context: per_session

Preprocessing

Data state: raw Preprocessing applied: False

Signal Processing

Feature extraction: ERD, ERS, autoregressive model, power spectrum Frequency bands: alpha=[10.5, 13.5] Hz; mu=[8, 14] Hz Spatial filters: Laplacian (C3/C4 with 4 surrounding electrodes)

Cross-Validation

Evaluation type: cross_session

Performance (Original Study)

Accuracy: 70.0% Pvc 1D Threshold: 70.0 Pvc 2D Threshold: 40.0

BCI Application

Applications: cursor_control Environment: laboratory Online feedback: True

Tags

Pathology: Healthy Modality: Motor Type: Active

Documentation

Description: Continuous sensorimotor rhythm based brain computer interface learning in a large population DOI: 10.1038/s41597-021-00883-1 License: CC-BY-NC-4.0 Investigators: James R. Stieger, Stephen A. Engel, Bin He Senior author: Bin He Contact: bhe1@andrew.cmu.edu Institution: Carnegie Mellon University, University of Minnesota Department: Carnegie Mellon University, Pittsburgh, PA, USA; University of Minnesota, Minneapolis, MN, USA Address: Pittsburgh, PA, USA; Minneapolis, MN, USA Country: US Repository: GitHub Data URL: https://doi.org/10.6084/m9.figshare.13123148.v1 Publication year: 2021 Funding: NIH AT009263; NIH EB021027; NIH NS096761; NIH MH114233; NIH EB029354 Ethics approval: University of Minnesota IRB; Carnegie Mellon University IRB Keywords: BCI, sensorimotor rhythm, motor imagery, EEG, longitudinal, learning

Abstract

Brain computer interfaces (BCIs) are valuable tools that expand the nature of communication through bypassing traditional neuromuscular pathways. The non-invasive, intuitive, and continuous nature of sensorimotor rhythm (SMR) based BCIs enables individuals to control computers, robotic arms, wheelchairs, and even drones by decoding motor imagination from electroencephalography (EEG). Large and uniform datasets are needed to design, evaluate, and improve the BCI algorithms. In this work, we release a large and longitudinal dataset collected during a study that examined how individuals learn to control SMR-BCIs. The dataset contains over 600 hours of EEG recordings collected during online and continuous BCI control from 62 healthy adults, (mostly) right hand dominant participants, across (up to) 11 training sessions per participant. The data record consists of 598 recording sessions, and over 250,000 trials of 4 different motor-imagery-based BCI tasks.

Methodology

Participants completed 7-11 online BCI training sessions. Each session consisted of 450 trials across 3 tasks (LR, UD, 2D) with 6 runs total. Each trial: 2s inter-trial interval, 2s target presentation, up to 6s feedback control. Online control used spatial filtering (Laplacian around C3/C4), autoregressive model (order 16) for spectrum estimation, alpha power (12 Hz ± 1.5 Hz) for control signal. Horizontal motion controlled by lateralized alpha power (C4-C3), vertical motion by total alpha power (C4+C3). Control signals normalized to zero mean and unit variance. Cursor position updated every 40 ms.

References

Stieger, J. R., Engel, S. A., & He, B. (2021). Continuous sensorimotor rhythm based brain computer interface learning in a large population. Scientific Data, 8(1), 98. https://doi.org/10.1038/s41597-021-00883-1 Notes .. versionadded:: 1.1.0 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: 60 ch (n=598 recordings)

Sampling frequencies: 1000.0 Hz (n=598 recordings)

Total recording duration: 615 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 60 ch · EEG · 1000 Hz · 62 subjects, 598 recordings

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

Showing one representative recording out of 62 subjects and 598 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.NM000339(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Stieger2021

Canonical—

Importable asNM000339 · Stieger2021

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

class eegdash.dataset.NM000339(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population

Study:: nm000339 (NeMAR)
Author (year):: Stieger2021
Canonical:: —

Also importable as: NM000339, Stieger2021.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 62; recordings: 598; 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/nm000339 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000339 DOI: https://doi.org/10.1038/s41597-021-00883-1

Examples

>>> from eegdash.dataset import NM000339
>>> dataset = NM000339(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 — NM000339.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 nm000339 to reproduce the tutorial on this dataset.

Citation

James R. Stieger, Stephen A. Engel, Bin He (2021). Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population. 10.1038/s41597-021-00883-1

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.1038/s41597-021-00883-1.

Related & sibling datasets

NM000147EEG · 44 subj DS006803EEG · 63 subj DS004147EEG · 12 subj DS006802EEG · 24 subj DS004017EEG · 21 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-NC-4.0 · 10.1038/s41597-021-00883-1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000339`
Title	Stieger et al. 2021 — Continuous sensorimotor rhythm based brain computer interface learning in a large population
Author (year)	`Stieger2021`
Canonical	—
Importable as	`NM000339`, `Stieger2021`
Year	2021
Authors	James R. Stieger, Stephen A. Engel, Bin He
License	CC-BY-NC-4.0
Citation / DOI	doi:10.1038/s41597-021-00883-1
Source links	OpenNeuro \| NeMAR \| Source URL

NM000339: eeg dataset, 62 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#