NM000235: eeg dataset, 31 subjects#

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

Access recordings and metadata through EEGDash.

Citation: Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu (2025). Eye-BCI multimodal MI/ME dataset from Guttmann-Flury et al 2025.

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

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000235

dataset = NM000235(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 = NM000235(cache_dir="./data", subject="01")

Advanced query

dataset = NM000235(
    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{nm000235,
  title = {Eye-BCI multimodal MI/ME dataset from Guttmann-Flury et al 2025},
  author = {Eva Guttmann-Flury and Xinjun Sheng and Xiangyang Zhu},
}

About This Dataset#

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

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

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

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	`NM000235`
Title	Eye-BCI multimodal MI/ME dataset from Guttmann-Flury et al 2025
Author (year)	`GuttmannFlury2025_Eye_BCI`
Canonical	—
Importable as	`NM000235`, `GuttmannFlury2025_Eye_BCI`
Year	2025
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): 6.996371388888889

Electrode Layout#

Electrode layout — EEG · 62 sensors — 62 channels

Dataset Statistics#

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

25

Sex distribution

11

20

Female Male Total: 31

Channel counts: 66 ch (n=63 recordings)

Sampling frequencies: 1000.0 Hz (n=63 recordings)

Total recording duration: 6 h 59 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 NM000235 class to access this dataset programmatically.

class eegdash.dataset.NM000235(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

Study:: nm000235 (NeMAR)
Author (year):: GuttmannFlury2025_Eye_BCI
Canonical:: —

Also importable as: NM000235, GuttmannFlury2025_Eye_BCI.

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/nm000235 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000235

Examples

>>> from eegdash.dataset import NM000235
>>> dataset = NM000235(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)