NM000158: eeg dataset, 50 subjects#

Dataset [1]_ from the study on motor imagery [2]_

Access recordings and metadata through EEGDash.

Citation: Haijie Liu, Penghu Wei, Haochong Wang, Xiaodong Lv, Wei Duan, Meijie Li, Yan Zhao, Qingmei Wang, Xinyuan Chen, Gaige Shi, Bo Han, Junwei Hao (2022). Dataset [1]_ from the study on motor imagery [2]_.

Modality: eeg Subjects: 50 Recordings: 50 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000158

dataset = NM000158(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000158(cache_dir="./data", subject="01")

Advanced query

dataset = NM000158(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000158,
  title = {Dataset [1]_ from the study on motor imagery [2]_},
  author = {Haijie Liu and Penghu Wei and Haochong Wang and Xiaodong Lv and Wei Duan and Meijie Li and Yan Zhao and Qingmei Wang and Xinyuan Chen and Gaige Shi and Bo Han and Junwei Hao},
}

About This Dataset#

Dataset [1] from the study on motor imagery [2]

Dataset [1]_ from the study on motor imagery [2]_.

Dataset Overview

  • Code: Liu2024

  • Paradigm: imagery

  • DOI: 10.1038/s41597-023-02787-8

View full README

Dataset [1] from the study on motor imagery [2]

Dataset [1]_ from the study on motor imagery [2]_.

Dataset Overview

  • Code: Liu2024

  • Paradigm: imagery

  • DOI: 10.1038/s41597-023-02787-8

  • Subjects: 50

  • Sessions per subject: 1

  • Events: left_hand=1, right_hand=2

  • Trial interval: (0, 4) s

  • File format: MAT and EDF

  • Data preprocessed: True

  • Contributing labs: Xuanwu Hospital Capital Medical University

Acquisition

  • Sampling rate: 500.0 Hz

  • Number of channels: 29

  • Channel types: eeg=29, eog=2

  • Channel names: C3, C4, CP3, CP4, Cz, F3, F4, F7, F8, FC3, FC4, FCz, FP1, FP2, FT7, FT8, Fz, HEOL, O1, O2, Oz, P3, P4, Pz, T3, T4, T5, T6, TP7, TP8, VEOR

  • Montage: 10-10

  • Hardware: ZhenTec NT1 wireless multichannel EEG acquisition system

  • Reference: CPz

  • Ground: FPz

  • Sensor type: semi-dry Ag/AgCl

  • Line frequency: 50.0 Hz

  • Impedance threshold: 20 kOhm

  • Cap manufacturer: Xi’an ZhenTec Intelligence Technology Co., Ltd.

  • Cap model: ZhenTec NT1

  • Electrode type: semi-dry

  • Electrode material: Ag/AgCl semi-dry electrodes based on highly absorbable porous sponges dampened with 3% NaCl solution

  • Auxiliary channels: EOG (2 ch, horizontal, vertical)

Participants

  • Number of subjects: 50

  • Health status: acute stroke patients

  • Clinical population: acute stroke patients (1-30 days post-stroke)

  • Age: mean=56.7, std=10.57, min=31.0, max=77.0

  • Gender distribution: male=39, female=11

Experimental Protocol

  • Paradigm: imagery

  • Number of classes: 2

  • Class labels: left_hand, right_hand

  • Trial duration: 8.0 s

  • Trials per class: left_hand=20, right_hand=20

  • Study design: Imagining grasping a spherical object with left or right hand while watching a video of gripping motion. Each trial: instruction stage (prompt), MI stage (4s video-guided imagery), break stage (rest).

  • Feedback type: none

  • Stimulus type: video and audio

  • Stimulus modalities: visual, audio

  • Synchronicity: cue-based

  • Mode: offline

  • Training/test split: True

  • Instructions: Subject sat approximately 80 cm from computer screen. Computer played audio instructions. Patients imagined grasping spherical object with prompted hand during 4s video playback.

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: 4.0 s

Data Structure

  • Trials: 40

  • Trials per class: left_hand=20, right_hand=20

  • Trials context: 40 trials per subject total (20 left-hand, 20 right-hand), alternating. Each trial: 8s total (instruction + 4s MI + break). Training/test split: 60%/40%.

Preprocessing

  • Data state: preprocessed

  • Preprocessing applied: True

  • Steps: baseline removal (mean removal method), FIR filtering (0.5-40 Hz)

  • Highpass filter: 0.5 Hz

  • Lowpass filter: 40.0 Hz

  • Bandpass filter: [0.5, 40.0]

  • Filter type: FIR

  • Epoch window: [0.0, 8.0]

  • Notes: Preprocessed with EEGLAB toolbox in MATLAB R2019b. Filtered data split into trials x channels x time-samples format by marker ‘1’. Some motion artifacts present in subjects 4, 5, 13, 14, 18, 24, 28, 33, 42, 43, 47, 48, 49.

Signal Processing

  • Classifiers: CSP+LDA, FBCSP+SVM, TSLDA+DGFMDRM, TWFB+DGFMDM

  • Feature extraction: CSP, FBCSP, ERD/ERS, Riemannian geometry (SCMs on SPD manifolds), Tangent Space, Time-Frequency (Morlet wavelet), TWFB (Time Window Filter Bank)

  • Frequency bands: alpha=[8.0, 15.0] Hz; beta=[15.0, 30.0] Hz; analyzed=[8.0, 30.0] Hz

  • Spatial filters: CSP, FBCSP, Discriminant Geodesic Filtering

Cross-Validation

  • Method: 10-fold cross-validation

  • Folds: 10

  • Evaluation type: within_subject

Performance (Original Study)

  • Csp+Lda Accuracy: 55.57

  • Fbcsp+Svm Accuracy: 57.57

  • Tslda+Dgfmdrm Accuracy: 61.2

  • Twfb+Dgfmdm Accuracy: 72.21

  • Twfb+Dgfmdm Kappa: 0.4442

  • Twfb+Dgfmdm Precision: 0.7543

  • Twfb+Dgfmdm Sensitivity: 0.7845

BCI Application

  • Applications: rehabilitation

  • Environment: hospital

  • Online feedback: False

Tags

  • Pathology: Stroke

  • Modality: Motor

  • Type: Motor Imagery

Documentation

  • Description: EEG motor imagery dataset from 50 acute stroke patients performing left- and right-handed hand-grip imagination tasks. First open dataset addressing left- and right-handed motor imagery in acute stroke patients.

  • DOI: 10.1038/s41597-023-02787-8

  • License: CC-BY-4.0

  • Investigators: Haijie Liu, Penghu Wei, Haochong Wang, Xiaodong Lv, Wei Duan, Meijie Li, Yan Zhao, Qingmei Wang, Xinyuan Chen, Gaige Shi, Bo Han, Junwei Hao

  • Senior author: Junwei Hao

  • Contact: haojunwei@vip.163.com

  • Institution: Xuanwu Hospital Capital Medical University

  • Department: Department of Neurology

  • Address: Beijing, 100053, China

  • Country: CN

  • Repository: Figshare

  • Data URL: https://doi.org/10.6084/m9.figshare.21679035.v5

  • Publication year: 2024

  • Funding: National Natural Science Foundation of China (grant nos. 82090043 and 81825008)

  • Ethics approval: Ethics Committee of Xuanwu Hospital of Capital Medical University (No. 2021-236)

  • Keywords: motor imagery, BCI, brain-computer interface, stroke patients, EEG, rehabilitation, acute stroke, hand-grip imagery, databases, scientific data

Abstract

The brain-computer interface (BCI) is a technology that involves direct communication with parts of the brain and has evolved rapidly in recent years; it has begun to be used in clinical practice, such as for patient rehabilitation. Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. We collected data from 50 acute stroke patients with wireless portable saline EEG devices during the performance of two tasks: 1) imagining right-handed movements and 2) imagining left-handed movements. The dataset consists of four types of data: 1) the motor imagery instructions, 2) raw recording data, 3) pre-processed data after removing artefacts and other manipulations, and 4) patient characteristics. This is the first open dataset to address left- and right-handed motor imagery in acute stroke patients.

Methodology

50 acute stroke patients (1-30 days post-stroke) performed 40 trials of hand-grip motor imagery (20 left, 20 right). Each 8s trial included instruction, 4s video-guided imagery, and rest phases. EEG recorded with ZhenTec NT1 wireless system (29 EEG + 2 EOG channels) at 500 Hz. Data organized in EEG-BIDS format with raw (.mat) and preprocessed (.edf) versions. Clinical assessments: NIHSS (mean=4.16, SD=2.85), MBI (mean=70.94, SD=18.22), mRS (mean=2.66, SD=1.44). 23 patients right hemiplegia, 27 left hemiplegia.

References

Liu, Haijie; Lv, Xiaodong (2022). EEG datasets of stroke patients. figshare. Dataset. DOI: https://doi.org/10.6084/m9.figshare.21679035.v5 Liu, Haijie, Wei, P., Wang, H. et al. An EEG motor imagery dataset for brain computer interface in acute stroke patients. Sci Data 11, 131 (2024). DOI: https://doi.org/10.1038/s41597-023-02787-8 Notes To add the break and instruction events, set the break_events and instr_events parameters to True while instantiating the class. .. versionadded:: 1.1.1 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

NM000158

Title

Dataset [1]_ from the study on motor imagery [2]_

Author (year)

Liu2024

Canonical

—

Importable as

NM000158, Liu2024

Year

2022

Authors

Haijie Liu, Penghu Wei, Haochong Wang, Xiaodong Lv, Wei Duan, Meijie Li, Yan Zhao, Qingmei Wang, Xinyuan Chen, Gaige Shi, Bo Han, Junwei Hao

License

CC-BY-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: 50

  • Recordings: 50

  • Tasks: 1

Channels & sampling rate

  • Channels: 29

  • Sampling rate (Hz): 500.0

  • Duration (hours): 4.444416666666666

Tags

  • Pathology: Other

  • Modality: Multisensory

  • Type: Motor

Files & format

  • Size on disk: 673.7 MB

  • File count: 50

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000158 class to access this dataset programmatically.

class eegdash.dataset.NM000158(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Dataset [1]_ from the study on motor imagery [2]_

Study:

nm000158 (NeMAR)

Author (year):

Liu2024

Canonical:

—

Also importable as: NM000158, Liu2024.

Modality: eeg; Experiment type: Motor; Subject type: Other. Subjects: 50; recordings: 50; 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/nm000158 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000158

Examples

>>> from eegdash.dataset import NM000158
>>> dataset = NM000158(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, overwrite=False)[source]#

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#