EEGdash›NeMAR›NM000196

Iss. 196 · 12 subjects · 36 recordings · CC0-1.0

Dataset Brief · c-VEP dataset from Thielen et al. (2015)

NM000196: eeg dataset, 12 subjects#

c-VEP dataset from Thielen et al. (2015)

Citation: Jordy Thielen, Philip van den Broek, Jason Farquhar, Peter Desain (2015). c-VEP dataset from Thielen et al. (2015).

12-participant EEG dataset — c-VEP dataset from Thielen et al. (2015).

Data & curation Jordy Thielen · Philip van den Broek · Jason Farquhar · Peter Desain
Year 2015 · Distributed via NeMAR
Funding BrainGain Smart Mix Program of the Netherlands Ministry of Economic Affairs · Netherlands Ministry of Education, Culture and Science (SSM06011)

EEG · 64 ch2048 HzBIDS 1.9.0Task · cvepHealthyVisualAttention

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 NM000196

dataset = NM000196(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 = NM000196(cache_dir="./data", subject="01")

Advanced query

dataset = NM000196(
    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{nm000196,
  title = {c-VEP dataset from Thielen et al. (2015)},
  author = {Jordy Thielen and Philip van den Broek and Jason Farquhar and Peter Desain},
}

§ 02Study · The README

About This Dataset#

c-VEP dataset from Thielen et al. (2015)

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

c-VEP dataset from Thielen et al. (2015)

1.0

├─ Sensory-event
├─ Experimental-stimulus

View full README

c-VEP dataset from Thielen et al. (2015)

1.0

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/intensity_1_0

0.0

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label/intensity_0_0

Paradigm-Specific Parameters

Detected paradigm: cvep
Code type: modulated Gold codes
Code length: 126
Number of targets: 36

Data Structure

Trials: 108
Trials context: 108 total per subject: 3 fixed-length copy-spelling runs x 36 trials per run, each trial 4.2 seconds (4 code cycles)

Preprocessing

Data state: preprocessed
Preprocessing applied: True
Steps: downsampling from 2048 Hz to 360 Hz, linear de-trending, common average referencing, spectral filtering
Highpass filter: 5 Hz
Lowpass filter: 100 Hz
Bandpass filter: {‘band1’: [5, 48], ‘band2’: [52, 100]}
Re-reference: car
Downsampled to: 360.0 Hz

Signal Processing

Classifiers: template matching, CCA
Feature extraction: correlation
Spatial filters: Canonical Correlation Analysis

Cross-Validation

Method: training-testing split
Evaluation type: within-subject

Performance (Original Study)

Accuracy Fixed Length: 86.0
Itr Fixed Length: 38.12
Spm Fixed Length: 6.93
Accuracy Early Stopping: 86.0
Itr Early Stopping: 48.37
Spm Early Stopping: 8.99

BCI Application

Applications: speller
Environment: laboratory
Online feedback: True

Tags

Pathology: Healthy
Modality: Visual
Type: Research

Documentation

DOI: 10.1371/journal.pone.0133797
License: CC0-1.0
Investigators: Jordy Thielen, Philip van den Broek, Jason Farquhar, Peter Desain
Senior author: Peter Desain
Contact: jordy.thielen@gmail.com; info@donders.ru.nl
Institution: Radboud University Nijmegen
Department: Donders Center for Cognition
Country: NL
Repository: GitHub
Data URL: https://public.data.ru.nl/dcc/DSC_2018.00047_553_v3
Publication year: 2015
Funding: BrainGain Smart Mix Program of the Netherlands Ministry of Economic Affairs; Netherlands Ministry of Education, Culture and Science (SSM06011)
Ethics approval: Ethical Committee of the Faculty of Social Sciences at the Radboud University Nijmegen
Keywords: Brain-Computer Interface, BCI, Broad-Band Visually Evoked Potentials, BBVEP, Gold codes, reconvolution, speller, visual stimulation

Abstract

Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.

Methodology

The study implements a novel BBVEP-based BCI using modulated Gold codes with a reconvolution approach for template generation. The reconvolution model decomposes responses into single-flash responses (short and long pulses) and predicts responses to unseen sequences. Two sets of Gold codes were used: set V for training (65 sequences) and set U for testing (65 sequences). Each sequence had 126 bits with duration of 1.05s. The classifier uses template matching with correlation, combined with Canonical Correlation Analysis for spatial filtering. Subset optimization (Platinum subset) selects the most distinguishable codes, and layout optimization arranges codes on the 6x6 grid to minimize cross-talk. An early stopping algorithm was implemented to reduce trial duration. Online experiments were conducted with 12 participants using a synchronous BCI paradigm.

References

Thielen, J. (Jordy), Jason Farquhar, Desain, P.W.M. (Peter) (2023): Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing. Version 2. Radboud University. (dataset). DOI: https://doi.org/10.34973/1ecz-1232 Thielen, J., Van Den Broek, P., Farquhar, J., & Desain, P. (2015). Broad-Band visually evoked potentials: re(con)volution in brain-computer interfacing. PLOS ONE, 10(7), e0133797. DOI: https://doi.org/10.1371/journal.pone.0133797 Notes .. versionadded:: 1.0.0 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) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=12, range 24–24 yr, mean 24.0 yr)

20

Other · 12

Channel counts: 64 ch (n=36 recordings)

Sampling frequencies: 2048.0 Hz (n=36 recordings)

Total recording duration: 2 h 36 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 2048 Hz · 12 subjects, 36 recordings

Live trace viewer — sub-12 · ses-0 · task-cvep · run-2

Showing one representative recording out of 12 subjects and 36 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 · 64 sensors — 64 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.NM000196(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Thielen2015

Canonical—

Importable asNM000196 · Thielen2015

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

class eegdash.dataset.NM000196(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

c-VEP dataset from Thielen et al. (2015)

Study:: nm000196 (NeMAR)
Author (year):: Thielen2015
Canonical:: —

Also importable as: NM000196, Thielen2015.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 36; 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/nm000196 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000196

Examples

>>> from eegdash.dataset import NM000196
>>> dataset = NM000196(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 — NM000196.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 nm000196 to reproduce the tutorial on this dataset.

Citation

Jordy Thielen, Philip van den Broek, Jason Farquhar, Peter Desain (2015). c-VEP dataset from Thielen et al. (2015).

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000205EEG · 14 subj NM000131EEG · 8 subj NM000136EEG · 31 subj NM000211EEG · 15 subj NM000206EEG · 15 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0-1.0 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000196`
Title	c-VEP dataset from Thielen et al. (2015)
Author (year)	`Thielen2015`
Canonical	—
Importable as	`NM000196`, `Thielen2015`
Year	2015
Authors	Jordy Thielen, Philip van den Broek, Jason Farquhar, Peter Desain
License	CC0-1.0
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000196: 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#