NM000196: eeg dataset, 12 subjects#

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

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 12 Recordings: 36 License: CC0-1.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

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)

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'])

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},
}

About This Dataset#

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

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

Dataset Overview

  • Code: Thielen2015

  • Paradigm: cvep

  • DOI: 10.34973/1ecz-1232

View full README

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

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

Dataset Overview

  • Code: Thielen2015

  • Paradigm: cvep

  • DOI: 10.34973/1ecz-1232

  • Subjects: 12

  • Sessions per subject: 1

  • Events: 1.0=101, 0.0=100

  • Trial interval: (0, 0.3) s

  • Runs per session: 3

  • File format: mat

  • Data preprocessed: True

Acquisition

  • Sampling rate: 2048.0 Hz

  • Number of channels: 64

  • Channel types: eeg=64

  • Channel names: AF3, AF4, AF7, AF8, AFz, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fp1, Fp2, Fpz, Fz, Iz, O1, O2, Oz, P1, P10, P2, P3, P4, P5, P6, P7, P8, P9, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP7, TP8

  • Montage: standard_1020

  • Hardware: Biosemi ActiveTwo

  • Reference: CMS/DRL

  • Sensor type: EEG

  • Line frequency: 50.0 Hz

  • Electrode type: active

Participants

  • Number of subjects: 12

  • Health status: patients

  • Clinical population: Healthy

  • Age: mean=24.0, std=2.3

  • Gender distribution: male=4, female=8

  • BCI experience: naive

  • Species: human

Experimental Protocol

  • Paradigm: cvep

  • Number of classes: 2

  • Class labels: 1.0, 0.0

  • Trial duration: 4.2 s

  • Study design: 6x6 matrix speller BCI using modulated Gold codes for visual stimulation; participants focused on target symbols while cells flashed according to pseudo-random bit-sequences

  • Feedback type: visual

  • Stimulus type: pseudo-random noise-code

  • Stimulus modalities: visual

  • Primary modality: visual

  • Synchronicity: synchronous

  • Mode: online

  • Training/test split: False

  • Instructions: participants visually attended cells containing target symbols during stimulation

HED Event Annotations

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

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) https://github.com/NeuroTechX/moabb

Dataset Information#

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

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: 36

  • Tasks: 1

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 2048.0

  • Duration (hours): 2.6154667154947915

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 3.5 GB

  • File count: 36

  • Format: BIDS

License & citation

  • License: CC0-1.0

  • DOI: —

Provenance

API Reference#

Use the NM000196 class to access this dataset programmatically.

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

Bases: EEGDashDataset

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, 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#