NM000347: eeg dataset, 37 subjects

HefmiIch2025

Access recordings and metadata through EEGDash.

Citation: Jian Shi, Danyang Chen, Xingwei Zhao, Zhixian Zhao, Shengjie Li, Yeguang Xu, Tao Ding, Zheng Zhu, Peng Zhang, Qing Ye, Yingxin Tang, Ping Zhang, Bo Tao, Zhouping Tang (2025). HefmiIch2025. 10.1038/s41597-025-06100-7

Modality: eeg Subjects: 37 Recordings: 98 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (100%)

Quickstart

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000347

dataset = NM000347(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 = NM000347(cache_dir="./data", subject="01")

Advanced query

dataset = NM000347(
    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{nm000347,
  title = {HefmiIch2025},
  author = {Jian Shi and Danyang Chen and Xingwei Zhao and Zhixian Zhao and Shengjie Li and Yeguang Xu and Tao Ding and Zheng Zhu and Peng Zhang and Qing Ye and Yingxin Tang and Ping Zhang and Bo Tao and Zhouping Tang},
  doi = {10.1038/s41597-025-06100-7},
  url = {https://doi.org/10.1038/s41597-025-06100-7},
}

About This Dataset

HefmiIch2025

Hybrid EEG-fNIRS MI dataset for ICH from Shi et al 2025.

Dataset Overview

Code: HefmiIch2025 Paradigm: imagery DOI: 10.1038/s41597-025-06100-7

View full README

HefmiIch2025

Hybrid EEG-fNIRS MI dataset for ICH from Shi et al 2025.

Dataset Overview

Code: HefmiIch2025 Paradigm: imagery DOI: 10.1038/s41597-025-06100-7 Subjects: 37 Sessions per subject: 3 Events: left_hand=1, right_hand=2 Trial interval: [0, 10] s File format: MAT (pre-epoched) Data preprocessed: True

Acquisition

Sampling rate: 256.0 Hz Number of channels: 32 Channel types: eeg=32 Channel names: FC1, AF3, AF4, CP1, CP2, CP6, Cz, C3, C4, T7, T8, FC2, FC5, FC6, Pz, CP5, PO3, PO4, Oz, Fp2, Fp1, Fz, F3, F4, F7, F8, P3, P4, P7, P8, O1, O2 Montage: biosemi32 Hardware: g.HIamp (g.tec medical engineering GmbH) Line frequency: 50.0 Hz Online filters: {}

Participants

Number of subjects: 37 Health status: mixed (17 healthy, 20 ICH patients) Clinical population: intracerebral hemorrhage (ICH) Age: min=20.0, max=65.0 Gender distribution: female=8, male=29 Handedness: right-handed Species: human

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 27.0 s Study design: 2-class hand MI (left/right grasping) for ICH rehabilitation. 17 healthy + 20 ICH patients, 1-6 sessions per subject. Feedback type: none Stimulus type: directional arrow + auditory beep Stimulus modalities: visual, auditory Primary modality: visual Synchronicity: synchronous 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 Cue duration: 2.0 s Imagery duration: 10.0 s

Data Structure

Trials: 3330 Trials context: 37 subjects x ~3 sessions x 30 trials = ~3330

Signal Processing

Classifiers: CSP+SVM, FBCSP+SVM, EEGBaseNet, TF+SVM Feature extraction: CSP, FBCSP, time-frequency features Frequency bands: preprocessing=[0.5, 30.0] Hz Spatial filters: CSP, FBCSP

Cross-Validation

Method: 5-fold Folds: 5 Evaluation type: within_subject

BCI Application

Applications: rehabilitation Environment: clinical Online feedback: False

Tags

Pathology: Healthy, Stroke Modality: Motor Type: Clinical, Research

Documentation

DOI: 10.1038/s41597-025-06100-7 License: CC-BY-NC-ND-4.0 Investigators: Jian Shi, Danyang Chen, Xingwei Zhao, Zhixian Zhao, Shengjie Li, Yeguang Xu, Tao Ding, Zheng Zhu, Peng Zhang, Qing Ye, Yingxin Tang, Ping Zhang, Bo Tao, Zhouping Tang Institution: Huazhong University of Science and Technology Country: CN Data URL: https://figshare.com/articles/dataset/28955456 Publication year: 2025

References

Shi, J., Chen, D., et al. (2025). HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage. Scientific Data. https://doi.org/10.1038/s41597-025-06100-7 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

Dataset Information

Dataset ID	NM000347
Title	HefmiIch2025
Author (year)	HefmiIch2025
Canonical	HEFMI_ICH, HEFMIICH
Importable as	NM000347, HefmiIch2025, HEFMI_ICH, HEFMIICH
Year	2025
Authors	Jian Shi, Danyang Chen, Xingwei Zhao, Zhixian Zhao, Shengjie Li, Yeguang Xu, Tao Ding, Zheng Zhu, Peng Zhang, Qing Ye, Yingxin Tang, Ping Zhang, Bo Tao, Zhouping Tang
License	CC-BY-NC-ND-4.0
Citation / DOI	doi:10.1038/s41597-025-06100-7
Source links	OpenNeuro | NeMAR | Source URL

Copy-paste BibTeX

@dataset{nm000347,
  title = {HefmiIch2025},
  author = {Jian Shi and Danyang Chen and Xingwei Zhao and Zhixian Zhao and Shengjie Li and Yeguang Xu and Tao Ding and Zheng Zhu and Peng Zhang and Qing Ye and Yingxin Tang and Ping Zhang and Bo Tao and Zhouping Tang},
  doi = {10.1038/s41597-025-06100-7},
  url = {https://doi.org/10.1038/s41597-025-06100-7},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details

Subjects & recordings

• Subjects: 37

• Recordings: 98

• Tasks: 1

Channels & sampling rate

• Channels: 32

• Sampling rate (Hz): 256.0

• Duration (hours): 31.19656032986111

Tags

• Pathology: Other

• Modality: Multisensory

• Type: Motor

Files & format

• Size on disk: 2.6 GB

• File count: 98

• Format: BIDS

License & citation

• License: CC-BY-NC-ND-4.0

• DOI: doi:10.1038/s41597-025-06100-7

Provenance

• Source: nemar

• OpenNeuro: nm000347

• NeMAR: nm000347

API Reference

Use the NM000347 class to access this dataset programmatically.

class eegdash.dataset.NM000347(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

HefmiIch2025

Study:

nm000347 (NeMAR)

Author (year):

HefmiIch2025

Canonical:

HEFMI_ICH, HEFMIICH

Also importable as: NM000347, HefmiIch2025, HEFMI_ICH, HEFMIICH.

Modality: eeg; Experiment type: Motor; Subject type: Other. Subjects: 37; recordings: 98; 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/nm000347 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000347 DOI: https://doi.org/10.1038/s41597-025-06100-7

Examples

>>> from eegdash.dataset import NM000347
>>> dataset = NM000347(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

save(path, overwrite=False)

Save the dataset to disk.

Parameters:

• path (str or Path) – Destination file path.

• overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also

• eegdash.dataset.EEGDashDataset

• eegdash.dataset

• OpenNeuro dataset page

• NeMAR dataset page

• eegdash.dataset.DS001785

• eegdash.dataset.DS001787

• eegdash.dataset.DS001810

• eegdash.dataset.DS001849

• eegdash.dataset.DS001971