EEGdash›NeMAR›NM000123

Iss. 123 · 12 subjects · 30 recordings · CC-BY-4.0

Dataset Brief · Kalunga2016 – SSVEP Exo dataset

NM000123: eeg dataset, 12 subjects#

Kalunga2016 – SSVEP Exo dataset

Citation: Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthélemy, Karim Djouani, Eric Monacelli, Yskandar Hamam (2019). Kalunga2016 – SSVEP Exo dataset. 10.82901/nemar.nm000123

12-participant EEG dataset — Kalunga2016 – SSVEP Exo dataset.

Data & curation Emmanuel K. Kalunga · Sylvain Chevallier · Quentin Barthélemy · Karim Djouani · Eric Monacelli · Yskandar Hamam
Year 2019 · Distributed via NeMAR

EEG · 8 ch256 HzBIDS 1.9.0Task · ssvepHealthyVisualPerception

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

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},
  doi = {10.82901/nemar.nm000123},
  url = {https://doi.org/10.82901/nemar.nm000123},
}

§ 02Study · The README

About This Dataset#

SSVEP Exo dataset.

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

DOI

SSVEP Exo dataset

View full README

DOI

SSVEP Exo dataset

     ├─ 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) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 8 ch (n=30 recordings)

Sampling frequencies: 256.0 Hz (n=30 recordings)

Total recording duration: 2 h 35 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 8 ch · EEG · 256 Hz · 12 subjects, 30 recordings

Live trace viewer — sub-12 · ses-0 · task-ssvep · run-3

Showing one representative recording out of 12 subjects and 30 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 8 sensors — 8 channels

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

§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000123(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Kalunga2016

Canonical—

Importable asNM000123 · Kalunga2016

Sourceeegdash/dataset/registry.py · [source ↗]

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

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 DOI: https://doi.org/10.82901/nemar.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: 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)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000123.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000123 to reproduce the tutorial on this dataset.

Citation

Emmanuel K. Kalunga, Sylvain Chevallier, Quentin Barthélemy, Karim Djouani, Eric Monacelli, … (2019). Kalunga2016 – SSVEP Exo dataset. 10.82901/nemar.nm000123

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000123.

Related & sibling datasets

NM000118EEG · 9 subj NM000121EEG · 11 subj NM000176EEG · 5 subj NM000122EEG · 12 subj NM000126EEG · 34 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000123

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

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	10.82901/nemar.nm000123
Source links	OpenNeuro \| NeMAR \| Source URL

NM000123: eeg dataset, 12 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#