NM000265: eeg dataset, 31 subjects#

Guttmann-Flury et al. 2025 (Motor Imagery) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms

Access recordings and metadata through EEGDash.

Citation: Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu (2025). Guttmann-Flury et al. 2025 (Motor Imagery) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms. 10.1038/s41597-025-04861-9

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

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

BibTeX

@dataset{nm000265,
  title = {Guttmann-Flury et al. 2025 (Motor Imagery) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms},
  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) NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000265`
Title	Guttmann-Flury et al. 2025 (Motor Imagery) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms
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

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

Electrode Layout#

Electrode layout — EEG · 60 sensors — 60 channels

Dataset Statistics#

Age distribution (n=31, range 28–28 yr)

25

Sex distribution

11

20

Female Male Total: 31

Channel counts: 65 ch (n=126 recordings)

Sampling frequencies: 1000.0 Hz (n=126 recordings)

Total recording duration: 14 h 5 min

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 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

Guttmann-Flury et al. 2025 (Motor Imagery) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms

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: 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)