EEGdash›NeMAR›NM000306

Iss. 306 · 15 subjects · 30 recordings · CC-BY-4.0

Dataset Brief · Jia2019

NM000306: eeg dataset, 15 subjects#

Jia2019: Motor-imagery EEG dataset for stroke patients (Jia 2019)

Access recordings and metadata through EEGDash.

Citation: Tianyu Jia (2019). Jia2019: Motor-imagery EEG dataset for stroke patients (Jia 2019). 10.82901/nemar.nm000306

Modality: eeg Subjects: 15 Recordings: 30 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

15-participant EEG dataset — Jia2019: Motor-imagery EEG dataset for stroke patients (Jia 2019).

Data & curation Tianyu Jia
Senior author Tianyu Jia
Year 2019 · Distributed via NeMAR

EEG · 63 ch512 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 NM000306

dataset = NM000306(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 = NM000306(cache_dir="./data", subject="01")

Advanced query

dataset = NM000306(
    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{nm000306,
  title = {Jia2019: Motor-imagery EEG dataset for stroke patients (Jia 2019)},
  author = {Tianyu Jia},
  doi = {10.82901/nemar.nm000306},
  url = {https://doi.org/10.82901/nemar.nm000306},
}

§ 02Study · The README

About This Dataset#

Motor-imagery EEG dataset for stroke patients (Jia 2019) [1]_.

Code: Jia2019

Paradigm: imagery DOI: 10.6084/m9.figshare.7636301 Subjects: 15 Sessions per subject: 1 Events: left_hand=1, right_hand=2 Trial interval: [0, 6.8] s Runs per session: 2 File format: MAT Data preprocessed: True

DOI

Jia2019

Acquisition

Sampling rate: 512.0 Hz Number of channels: 63 Channel types: eeg=63 Line frequency: 50.0 Hz

View full README

DOI

Jia2019

Acquisition

Sampling rate: 512.0 Hz Number of channels: 63 Channel types: eeg=63 Line frequency: 50.0 Hz

Participants

Number of subjects: 15 Health status: stroke Clinical population: stroke

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trials per class: left_hand=40, right_hand=40 Study design: Cue-based left-hand vs right-hand motor imagery in stroke patients; for each patient one hand is paretic and the other unaffected, and both were imagined. Synchronicity: cue-based Mode: offline

HED Event Annotations

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

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

right_hand

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

Paradigm-Specific Parameters

Detected paradigm: motor_imagery Imagery tasks: left_hand, right_hand

Preprocessing

Data state: epoched Preprocessing applied: True

Cross-Validation

Evaluation type: within_subject

Tags

Pathology: Stroke Modality: Motor Type: Motor Imagery

Documentation

Description: EEG from 15 stroke patients performing left- vs right-hand motor imagery, 63 channels (10-10) at 512 Hz, 40 trials per class. DOI: 10.6084/m9.figshare.7636301 License: CC-BY-4.0 Investigators: Tianyu Jia Country: CN Repository: Figshare Data URL: https://doi.org/10.6084/m9.figshare.7636301 Publication year: 2019 Keywords: motor imagery, BCI, brain-computer interface, EEG, stroke, neurorehabilitation

References

Jia, T. (2019). EEG data of motor imagery for stroke. Figshare. DOI: https://doi.org/10.6084/m9.figshare.7636301 Wang, X., Zhao, Y., He, D., Xia, Q., Li, G., Wang, N., Peng, N., & Jiang, B. (2026). PA-TCNet: Pathology-Aware Temporal Calibration with Physiology-Guided Target Refinement for Cross-Subject Motor Imagery EEG Decoding in Stroke Patients. arXiv:2604.16554. DOI: https://doi.org/10.48550/arXiv.2604.16554 Notes .. versionadded:: 1.8 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) https://github.com/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.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000306-blue)](https://doi.org/10.82901/nemar.nm000306) Jia2019 ======= Motor-imagery EEG dataset for stroke patients (Jia 2019) [1]_. Dataset Overview —————-

Code: Jia2019 Paradigm: imagery DOI: 10.6084/m9.figshare.7636301 Subjects: 15 Sessions per subject: 1 Events: left_hand=1, right_hand=2 Trial interval: [0, 6.8] s Runs per session: 2 File format: MAT Data preprocessed: True

Acquisition#

Sampling rate: 512.0 Hz Number of channels: 63 Channel types: eeg=63 Line frequency: 50.0 Hz

Participants#

Number of subjects: 15 Health status: stroke Clinical population: stroke

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trials per class: left_hand=40, right_hand=40 Study design: Cue-based left-hand vs right-hand motor imagery in stroke patients; for each patient one hand is paretic and the other unaffected, and both were imagined. Synchronicity: cue-based Mode: offline

HED Event Annotations#

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

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

└─ Imagine
├─ Move └─ Left, Hand

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

└─ Imagine
├─ Move └─ Right, Hand

Paradigm-Specific Parameters#

Detected paradigm: motor_imagery Imagery tasks: left_hand, right_hand

Preprocessing#

Data state: epoched Preprocessing applied: True

Cross-Validation#

Evaluation type: within_subject

Tags#

Pathology: Stroke Modality: Motor Type: Motor Imagery

Documentation#

Description: EEG from 15 stroke patients performing left- vs right-hand motor imagery, 63 channels (10-10) at 512 Hz, 40 trials per class. DOI: 10.6084/m9.figshare.7636301 License: CC-BY-4.0 Investigators: Tianyu Jia Country: CN Repository: Figshare Data URL: https://doi.org/10.6084/m9.figshare.7636301 Publication year: 2019 Keywords: motor imagery, BCI, brain-computer interface, EEG, stroke, neurorehabilitation

References#

Jia, T. (2019). EEG data of motor imagery for stroke. Figshare. DOI: https://doi.org/10.6084/m9.figshare.7636301 Wang, X., Zhao, Y., He, D., Xia, Q., Li, G., Wang, N., Peng, N., & Jiang, B. (2026). PA-TCNet: Pathology-Aware Temporal Calibration with Physiology-Guided Target Refinement for Cross-Subject Motor Imagery EEG Decoding in Stroke Patients. arXiv:2604.16554. DOI: https://doi.org/10.48550/arXiv.2604.16554 Notes .. versionadded:: 1.8 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) https://github.com/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.

License: CC-BY-4.0

Authors:

Tianyu Jia

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000306

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 63 ch (n=30 recordings)

Sampling frequencies: 512.0 Hz (n=30 recordings)

Total recording duration: 2 h 25 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 63 ch · EEG · 512 Hz · 15 subjects, 30 recordings

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

Showing one representative recording out of 15 subjects and 30 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.NM000306(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000306

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

class eegdash.dataset.NM000306(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Jia2019: Motor-imagery EEG dataset for stroke patients (Jia 2019)

Study:: nm000306 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000306.

Modality: eeg; Subject type: Unknown. Subjects: 15; recordings: 30; 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/nm000306 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000306 DOI: https://doi.org/10.82901/nemar.nm000306

Examples

>>> from eegdash.dataset import NM000306
>>> dataset = NM000306(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 — NM000306.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 nm000306 to reproduce the tutorial on this dataset.

Citation

Tianyu Jia (2019). Jia2019: Motor-imagery EEG dataset for stroke patients (Jia 2019). 10.82901/nemar.nm000306

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000306.

Related & sibling datasets

NM000319EEG · 8 subj NM000385EEG · 14 subj NM000293EEG · 11 subj NM000318EEG · 20 subj NM000325EEG · 15 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.nm000306

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000306`
Title	Jia2019: Motor-imagery EEG dataset for stroke patients (Jia 2019)
Author (year)	—
Canonical	—
Importable as	`NM000306`
Year	2019
Authors	Tianyu Jia
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000306
Source links	OpenNeuro \| NeMAR \| Source URL

NM000306: eeg dataset, 15 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Paradigm-Specific Parameters#

Preprocessing#

Cross-Validation#

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#