NM000156: eeg dataset, 37 subjects#

Name: Hybrid EEG-fNIRS MI dataset for ICH from Shi et al 2025
Published: 2025-01-01
License: CC-BY-NC-ND-4.0

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

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

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

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000156

dataset = NM000156(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 = NM000156(cache_dir="./data", subject="01")

Advanced query

dataset = NM000156(
    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{nm000156,
  title = {Hybrid EEG-fNIRS MI dataset for ICH from Shi et al 2025},
  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},
}

About This Dataset#

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

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

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

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) NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000156`
Title	Hybrid EEG-fNIRS MI dataset for ICH from Shi et al 2025
Author (year)	—
Canonical	—
Importable as	`NM000156`
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	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

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): 31.22144314236111

Electrode Layout#

Electrode layout — EEG · 32 sensors — 32 channels

Dataset Statistics#

Channel counts: 32 ch (n=98 recordings)

Sampling frequencies: 256.0 Hz (n=98 recordings)

Total recording duration: 31 h

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the NM000156 class to access this dataset programmatically.

class eegdash.dataset.NM000156(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

Study:: nm000156 (NeMAR)
Author (year):: nan
Canonical:: —

Also importable as: NM000156, nan.

Modality: eeg. 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/nm000156 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000156

Examples

>>> from eegdash.dataset import NM000156
>>> dataset = NM000156(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)