NM000227: eeg dataset, 31 subjects#

Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025

Access recordings and metadata through EEGDash.

Citation: Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu (2019). Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025.

Modality: eeg Subjects: 31 Recordings: 63 License: CC0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000227

dataset = NM000227(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000227(cache_dir="./data", subject="01")

Advanced query

dataset = NM000227(
    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{nm000227,
  title = {Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025},
  author = {Eva Guttmann-Flury and Xinjun Sheng and Xiangyang Zhu},
}

About This Dataset#

Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025

Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025.

Dataset Overview

  • Code: GuttmannFlury2025-ME

  • Paradigm: imagery

  • DOI: 10.1038/s41597-025-04861-9

View full README

Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025

Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025.

Dataset Overview

  • Code: GuttmannFlury2025-ME

  • Paradigm: imagery

  • DOI: 10.1038/s41597-025-04861-9

  • Subjects: 31

  • Sessions per subject: 3

  • Events: left_hand=1, right_hand=2

  • Trial interval: [0, 4] s

  • File format: BDF

Acquisition

  • Sampling rate: 1000.0 Hz

  • Number of channels: 66

  • Channel types: eeg=64, eog=1, stim=1

  • Channel names: FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6, PO8, O1, OZ, O2, CB1, CB2

  • Montage: standard_1005

  • Hardware: Neuroscan Quik-Cap 65-ch, SynAmps2

  • Reference: right mastoid (M1)

  • Ground: forehead

  • Sensor type: Ag/AgCl

  • Line frequency: 50.0 Hz

  • Online filters: {‘highpass_time_constant_s’: 10}

Participants

  • Number of subjects: 31

  • Health status: healthy

  • Age: mean=28.3, min=20.0, max=57.0

  • Gender distribution: female=11, male=20

  • Species: human

Experimental Protocol

  • Paradigm: imagery

  • Number of classes: 2

  • Class labels: left_hand, right_hand

  • Trial duration: 7.5 s

  • Study design: Multi-paradigm BCI (MI/ME/SSVEP/P300). MI and ME: 2-class hand grasping, 40 trials/session, up to 3 sessions per subject.

  • Feedback type: none

  • Stimulus type: visual rectangle cue

  • Stimulus modalities: visual

  • 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: 4.0 s

Data Structure

  • Trials: 2520

  • Trials context: 63 sessions x 40 trials = 2520 (MI only, default)

BCI Application

  • Applications: motor_control

  • Environment: laboratory

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: Motor

  • Type: Research

Documentation

  • DOI: 10.1038/s41597-025-04861-9

  • License: CC0

  • Investigators: Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu

  • Institution: Shanghai Jiao Tong University

  • Country: CN

  • Publication year: 2025

References

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

NM000227

Title

Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025

Author (year)

GuttmannFlury2025_Eye

Canonical

GuttmannFlury2025_ME

Importable as

NM000227, GuttmannFlury2025_Eye, GuttmannFlury2025_ME

Year

2019

Authors

Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu

License

CC0

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: 31

  • Recordings: 63

  • Tasks: 1

Channels & sampling rate

  • Channels: 66

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 7.093593611111111

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 4.7 GB

  • File count: 63

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: —

Provenance

API Reference#

Use the NM000227 class to access this dataset programmatically.

class eegdash.dataset.NM000227(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025

Study:

nm000227 (NeMAR)

Author (year):

GuttmannFlury2025_Eye

Canonical:

GuttmannFlury2025_ME

Also importable as: NM000227, GuttmannFlury2025_Eye, GuttmannFlury2025_ME.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 31; recordings: 63; 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/nm000227 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000227

Examples

>>> from eegdash.dataset import NM000227
>>> dataset = NM000227(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#