NM000265: eeg dataset, 31 subjects#

GuttmannFlury2025-MI

Access recordings and metadata through EEGDash.

Citation: Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu (2025). GuttmannFlury2025-MI. 10.1038/s41597-025-04861-9

Modality: eeg Subjects: 31 Recordings: 126 License: CC0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000265

dataset = NM000265(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000265(cache_dir="./data", subject="01")

Advanced query

dataset = NM000265(
    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{nm000265,
  title = {GuttmannFlury2025-MI},
  author = {Eva Guttmann-Flury and Xinjun Sheng and Xiangyang Zhu},
  doi = {10.1038/s41597-025-04861-9},
  url = {https://doi.org/10.1038/s41597-025-04861-9},
}

About This Dataset#

GuttmannFlury2025-MI

Eye-BCI multimodal MI/ME dataset from Guttmann-Flury et al 2025.

Dataset Overview

Code: GuttmannFlury2025-MI Paradigm: imagery DOI: 10.1038/s41597-025-04861-9

View full README

GuttmannFlury2025-MI

Eye-BCI multimodal MI/ME dataset from Guttmann-Flury et al 2025.

Dataset Overview

Code: GuttmannFlury2025-MI 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

Guttmann-Flury, E., Sheng, X., & Zhu, X. (2025). Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms. Scientific Data, 12, 587. https://doi.org/10.1038/s41597-025-04861-9 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

NM000265

Title

GuttmannFlury2025-MI

Author (year)

GuttmannFlury2025_MI

Canonical

—

Importable as

NM000265, GuttmannFlury2025_MI

Year

2025

Authors

Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu

License

CC0

Citation / DOI

doi:10.1038/s41597-025-04861-9

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000265,
  title = {GuttmannFlury2025-MI},
  author = {Eva Guttmann-Flury and Xinjun Sheng and Xiangyang Zhu},
  doi = {10.1038/s41597-025-04861-9},
  url = {https://doi.org/10.1038/s41597-025-04861-9},
}

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: 126

  • Tasks: 1

Channels & sampling rate

  • Channels: 65

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 14.089965

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 9.2 GB

  • File count: 126

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.1038/s41597-025-04861-9

Provenance

API Reference#

Use the NM000265 class to access this dataset programmatically.

class eegdash.dataset.NM000265(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

GuttmannFlury2025-MI

Study:

nm000265 (NeMAR)

Author (year):

GuttmannFlury2025_MI

Canonical:

—

Also importable as: NM000265, GuttmannFlury2025_MI.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 31; recordings: 126; 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/nm000265 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000265 DOI: https://doi.org/10.1038/s41597-025-04861-9

Examples

>>> from eegdash.dataset import NM000265
>>> dataset = NM000265(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#