NM000236: eeg dataset, 21 subjects#

Dataset of an EEG-based BCI experiment in Virtual Reality using P300

Access recordings and metadata through EEGDash.

Citation: Grégoire Cattan, Anton Andreev, Pedro Luiz Coelho Rodrigues, Marco Congedo (2019). Dataset of an EEG-based BCI experiment in Virtual Reality using P300.

Modality: eeg Subjects: 21 Recordings: 2520 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000236

dataset = NM000236(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000236(cache_dir="./data", subject="01")

Advanced query

dataset = NM000236(
    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{nm000236,
  title = {Dataset of an EEG-based BCI experiment in Virtual Reality using P300},
  author = {Grégoire Cattan and Anton Andreev and Pedro Luiz Coelho Rodrigues and Marco Congedo},
}

About This Dataset#

Dataset of an EEG-based BCI experiment in Virtual Reality using P300

Dataset of an EEG-based BCI experiment in Virtual Reality using P300.

Dataset Overview

View full README

Dataset of an EEG-based BCI experiment in Virtual Reality using P300

Dataset of an EEG-based BCI experiment in Virtual Reality using P300.

Dataset Overview

  • Code: Cattan2019-VR

  • Paradigm: p300

  • DOI: https://doi.org/10.5281/zenodo.2605204

  • Subjects: 21

  • Sessions per subject: 1

  • Events: Target=2, NonTarget=1

  • Trial interval: [0, 1.0] s

  • Runs per session: 60

  • Session IDs: PC, VR

  • File format: mat, csv

  • Contributing labs: GIPSA-lab

Acquisition

  • Sampling rate: 512.0 Hz

  • Number of channels: 16

  • Channel types: eeg=16

  • Channel names: Fp1, Fp2, Fc5, Fz, Fc6, T7, Cz, T8, P7, P3, Pz, P4, P8, O1, Oz, O2

  • Montage: 10-10

  • Hardware: g.USBamp (g.tec, Schiedlberg, Austria)

  • Software: OpenVibe

  • Reference: right earlobe

  • Ground: AFZ

  • Sensor type: wet electrodes

  • Line frequency: 50.0 Hz

  • Online filters: no digital filter applied

  • Cap manufacturer: EasyCap

  • Cap model: EC20

Participants

  • Number of subjects: 21

  • Health status: healthy

  • Age: mean=26.38, std=5.78, min=19.0, max=44.0

  • Gender distribution: male=14, female=7

  • BCI experience: varied gaming experience: some played video games occasionally, some played First Person Shooters; varied VR experience from none to repetitive

Experimental Protocol

  • Paradigm: p300

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Study design: randomized session order (PC vs VR); limit eye blinks, head movements and face muscular contractions

  • Feedback type: visual

  • Stimulus type: flashing white crosses in 6x6 matrix

  • Stimulus modalities: visual

  • Primary modality: visual

  • Mode: offline

  • Training/test split: False

  • Instructions: focus on a red-squared target symbol while groups of six symbols flash

  • Stimulus presentation: description=6x6 matrix of white crosses; groups of 6 symbols flash; each symbol flashes exactly 2 times per repetition, platform=Unity engine exported to PC and VR

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

Target

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Target

NonTarget

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target

Paradigm-Specific Parameters

  • Detected paradigm: p300

  • Number of targets: 1

  • Number of repetitions: 12

Data Structure

  • Trials: {‘target’: 120, ‘non_target’: 600}

  • Trials per class: target=120, non_target=600

  • Blocks per session: 12

  • Trials context: per session: 12 blocks × 5 repetitions × 12 flashes per repetition (2 target, 10 non-target)

Preprocessing

  • Data state: raw EEG with software tagging via USB (note: tagging introduces jitter and latency - mean 38ms in PC, 117ms in VR)

  • Preprocessing applied: False

  • Notes: mean tagging latency: ~38 ms in PC, ~117 ms in VR due to different hardware/software setup; these latencies should be used to correct ERPs

Signal Processing

  • Classifiers: xDAWN, Riemannian

  • Feature extraction: Covariance/Riemannian, xDAWN

Cross-Validation

  • Evaluation type: cross_session

BCI Application

  • Applications: speller

  • Environment: PC and Virtual Reality (VRElegiant HMD with Huawei Ascend Mate 7 smartphone)

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Perception

Documentation

  • Description: EEG recordings of 21 subjects doing a visual P300 experiment on PC and VR to compare BCI performance and user experience

  • DOI: 10.5281/zenodo.2605204

  • Associated paper DOI: hal-02078533v3

  • License: CC-BY-4.0

  • Investigators: Grégoire Cattan, Anton Andreev, Pedro Luiz Coelho Rodrigues, Marco Congedo

  • Senior author: Marco Congedo

  • Institution: GIPSA-lab

  • Department: GIPSA-lab, CNRS, University Grenoble-Alpes, Grenoble INP

  • Address: GIPSA-lab, 11 rue des Mathématiques, Grenoble Campus BP46, F-38402, France

  • Country: FR

  • Repository: Zenodo

  • Data URL: https://doi.org/10.5281/zenodo.2605204

  • Publication year: 2019

  • Funding: IHMTEK Company (Interaction Homme-Machine Technologie)

  • Ethics approval: Ethical Committee of the University of Grenoble Alpes (Comité d’Ethique pour la Recherche Non-Interventionnelle)

  • Acknowledgements: promoted by the IHMTEK Company

  • Keywords: Electroencephalography (EEG), P300, Brain-Computer Interface (BCI), Virtual Reality (VR), experiment

Abstract

Dataset contains electroencephalographic recordings on 21 subjects doing a visual P300 experiment on PC and VR. The visual P300 is an event-related potential elicited by a visual stimulation, peaking 240–600 ms after stimulus onset. The experiment compares P300-based BCI on PC vs VR headset (passive HMD with smartphone) concerning physiological, subjective and performance aspects. EEG recorded with 16 electrodes. Experiment conducted at GIPSA-lab in 2018.

Methodology

Two randomized sessions (PC and VR). Each session: 12 blocks of 5 repetitions. Each repetition: 12 flashes of groups of 6 symbols, ensuring each symbol flashes exactly 2 times. Target flashes twice per repetition (2 target flashes), non-target flashes 10 times. Random feedback given after each repetition (70% expected accuracy). P300 interface: 6x6 matrix of white flashing crosses with red-squared target. VR used passive HMD (VRElegiant) with Huawei Mate 7 smartphone. IMU deactivated to prevent drift. Unity engine used for identical visual stimulation across PC and VR.

References

G. Cattan, A. Andreev, P. L. C. Rodrigues, and M. Congedo (2019). Dataset of an EEG-based BCI experiment in Virtual Reality and on a Personal Computer. Research Report, GIPSA-lab; IHMTEK. https://doi.org/10.5281/zenodo.2605204 .. versionadded:: 0.5.0 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

NM000236

Title

Dataset of an EEG-based BCI experiment in Virtual Reality using P300

Author (year)

Cattan2019_P300

Canonical

—

Importable as

NM000236, Cattan2019_P300

Year

2019

Authors

Grégoire Cattan, Anton Andreev, Pedro Luiz Coelho Rodrigues, Marco Congedo

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: 21

  • Recordings: 2520

  • Tasks: 1

Channels & sampling rate

  • Channels: 16

  • Sampling rate (Hz): 512.0

  • Duration (hours): 4.099188368055556

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 373.3 MB

  • File count: 2520

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000236 class to access this dataset programmatically.

class eegdash.dataset.NM000236(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Dataset of an EEG-based BCI experiment in Virtual Reality using P300

Study:

nm000236 (NeMAR)

Author (year):

Cattan2019_P300

Canonical:

—

Also importable as: NM000236, Cattan2019_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 21; recordings: 2520; 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/nm000236 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000236

Examples

>>> from eegdash.dataset import NM000236
>>> dataset = NM000236(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#