NM000123: eeg dataset, 12 subjects#

Name: Kalunga2016 – SSVEP Exo dataset
Published: 2019
License: CC-BY-4.0

Kalunga2016 – SSVEP Exo dataset

Access recordings and metadata through EEGDash.

Citation: Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthélemy, Karim Djouani, Eric Monacelli, Yskandar Hamam (2019). Kalunga2016 – SSVEP Exo dataset.

Modality: eeg Subjects: 12 Recordings: 30 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000123

dataset = NM000123(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 = NM000123(cache_dir="./data", subject="01")

Advanced query

dataset = NM000123(
    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{nm000123,
  title = {Kalunga2016 – SSVEP Exo dataset},
  author = {Emmanuel K. Kalunga and Sylvain Chevallier and Quentin Barthélemy and Karim Djouani and Eric Monacelli and Yskandar Hamam},
}

About This Dataset#

SSVEP Exo dataset

SSVEP Exo dataset.

Dataset Overview

Code: Kalunga2016
Paradigm: ssvep
DOI: 10.1016/j.neucom.2016.01.007

View full README

SSVEP Exo dataset

SSVEP Exo dataset.

Dataset Overview

Code: Kalunga2016
Paradigm: ssvep
DOI: 10.1016/j.neucom.2016.01.007
Subjects: 12
Sessions per subject: 1
Events: 13=2, 17=4, 21=3, rest=1
Trial interval: [2, 4] s
File format: fif

Acquisition

Sampling rate: 256.0 Hz
Number of channels: 8
Channel types: eeg=8
Channel names: Oz, O1, O2, POz, PO3, PO4, PO7, PO8
Montage: standard_1005
Hardware: g.tec MobiLab
Reference: right mastoid
Sensor type: EEG
Line frequency: 50.0 Hz

Participants

Number of subjects: 12
Health status: healthy
Species: human

Experimental Protocol

Paradigm: ssvep
Number of classes: 4
Class labels: 13, 17, 21, rest
Trial duration: 6.0 s
Study design: SSVEP
Feedback type: none
Stimulus type: flickering
Stimulus modalities: visual
Primary modality: visual
Synchronicity: synchronous
Mode: offline
Stimulus presentation: device=LED stimuli, frequencies=13 Hz, 17 Hz, 21 Hz, note=No phase synchronization required

HED Event Annotations

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

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/13

17

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/17

21

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/21

rest

├─ Experiment-structure
└─ Rest

Paradigm-Specific Parameters

Detected paradigm: ssvep
Stimulus frequencies: [13.0, 17.0, 21.0] Hz
Number of targets: 3

Data Structure

Trials: 32 trials per session (8 per visual stimulus, 8 for resting class)
Trials context: per session

Preprocessing

Preprocessing applied: False

Signal Processing

Classifiers: MDRM, CCA
Feature extraction: Covariance/Riemannian

Cross-Validation

Method: bootstrap
Evaluation type: cross_subject, cross_session

BCI Application

Applications: assistive_robotics
Environment: laboratory
Online feedback: False

Tags

Pathology: Healthy
Modality: Visual
Type: Perception

Documentation

Description: Online SSVEP-based BCI using Riemannian geometry for assistive robotics with shared control scheme
DOI: 10.1016/j.neucom.2016.01.007
License: CC-BY-4.0
Investigators: Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthélemy, Karim Djouani, Eric Monacelli, Yskandar Hamam
Senior author: Sylvain Chevallier
Institution: Universite de Versailles Saint-Quentin
Department: Laboratoire d’Ingénierie des Systèmes de Versailles
Address: 78140 Velizy, France
Country: FR
Repository: Zenodo
Data URL: https://zenodo.org/record/2392979
Publication year: 2016
Keywords: Riemannian geometry, Online, Asynchronous, Brain-Computer Interfaces, Steady State Visually Evoked Potentials

References

Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthelemy. “Online SSVEP-based BCI using Riemannian Geometry”. Neurocomputing, 2016. arXiv report: https://arxiv.org/abs/1501.03227 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 1.4.3 (Mother of All BCI Benchmarks) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000123`
Title	Kalunga2016 – SSVEP Exo dataset
Author (year)	`Kalunga2016`
Canonical	—
Importable as	`NM000123`, `Kalunga2016`
Year	2019
Authors	Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthélemy, Karim Djouani, Eric Monacelli, Yskandar Hamam
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: 12
Recordings: 30
Tasks: 1

Channels & sampling rate

Channels: 8
Sampling rate (Hz): 256.0
Duration (hours): 2.589654947916667

API Reference#

Use the NM000123 class to access this dataset programmatically.

class eegdash.dataset.NM000123(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Kalunga2016 – SSVEP Exo dataset

Study:: nm000123 (NeMAR)
Author (year):: Kalunga2016
Canonical:: —

Also importable as: NM000123, Kalunga2016.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 12; recordings: 30; 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/nm000123 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000123

Examples

>>> from eegdash.dataset import NM000123
>>> dataset = NM000123(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