NM000212: eeg dataset, 16 subjects

BNCI 2015-007 Motion VEP (mVEP) Speller dataset

Access recordings and metadata through EEGDash.

Citation: Sulamith Schaeff, Matthias Sebastian Treder, Bastian Venthur, Benjamin Blankertz (2012). BNCI 2015-007 Motion VEP (mVEP) Speller dataset.

Modality: eeg Subjects: 16 Recordings: 32 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000212

dataset = NM000212(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 = NM000212(cache_dir="./data", subject="01")

Advanced query

dataset = NM000212(
    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{nm000212,
  title = {BNCI 2015-007 Motion VEP (mVEP) Speller dataset},
  author = {Sulamith Schaeff and Matthias Sebastian Treder and Bastian Venthur and Benjamin Blankertz},
}

About This Dataset

BNCI 2015-007 Motion VEP (mVEP) Speller dataset

BNCI 2015-007 Motion VEP (mVEP) Speller dataset.

Dataset Overview

• Code: BNCI2015-007

• Paradigm: p300

• DOI: 10.1088/1741-2560/9/4/045006

View full README

BNCI 2015-007 Motion VEP (mVEP) Speller dataset

BNCI 2015-007 Motion VEP (mVEP) Speller dataset.

Dataset Overview

• Code: BNCI2015-007

• Paradigm: p300

• DOI: 10.1088/1741-2560/9/4/045006

• Subjects: 16

• Sessions per subject: 1

• Events: Target=1, NonTarget=2

• Trial interval: [0, 0.7] s

• Runs per session: 2

• Session IDs: practice, calibration, copy_spelling, free_spelling

• File format: gdf

• Data preprocessed: True

Acquisition

• Sampling rate: 100.0 Hz

• Number of channels: 63

• Channel types: eeg=63

• Channel names: Fp1, Fp2, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, T7, T8, Cz, C1, C2, C3, C4, C5, C6, TP7, TP8, CPz, CP1, CP2, CP3, CP4, CP5, CP6, Pz, P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, POz, PO3, PO4, PO7, PO8, Oz, O1, O2

• Montage: 10-10

• Hardware: BrainAmp EEG amplifier

• Software: Pyff, VisionEgg, MATLAB

• Reference: linked mastoids

• Ground: forehead

• Sensor type: active electrode

• Line frequency: 50.0 Hz

• Online filters: hardware bandpass filter 0.016–250 Hz

• Impedance threshold: 10.0 kOhm

• Cap manufacturer: Brain Products

• Electrode type: actiCap active electrode system

Participants

• Number of subjects: 16

• Health status: patients

• Clinical population: Healthy

• Age: mean=23.8, min=21, max=30

• Gender distribution: male=10, female=6

• Handedness: normal or corrected-to-normal vision

• BCI experience: naive

• Species: human

Experimental Protocol

• Paradigm: p300

• Task type: visual_speller

• Number of classes: 2

• Class labels: Target, NonTarget

• Trial duration: 30.0 s

• Study design: Three different Cake Speller modifications: Overt Cake Speller (gaze toward target), Covert Cake Speller (central fixation, covert attention), Motion Center Speller (foveal stimulation). Two-level selection (group-level and symbol-level) from 30 symbols.

• Study domain: gaze-independent communication

• Feedback type: visual

• Stimulus type: motion VEP (mVEP)

• Stimulus modalities: visual

• Primary modality: visual

• Synchronicity: synchronous

• Mode: online

• Training/test split: True

• Instructions: Copy-spelling and free-spelling with attention to target symbols. Participants counted moving bar/pattern presentations in target location.

• Stimulus presentation: soa_ms=200 ms (Cake Spellers) or 266 ms (Motion Center Speller), stimulus_duration_ms=100 ms, isi_ms=100 ms, repetitions=10 repetitions per level, total_presentations=120 per selection (2 levels × 10 repetitions × 6 groups/symbols)

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: 6

• Number of repetitions: 10

• Inter-stimulus interval: 100.0 ms

• Stimulus onset asynchrony: 200.0 ms

Data Structure

• Trials: 120

• Blocks per session: 4

• Trials context: per_selection (2 levels × 10 repetitions × 6 groups/symbols)

Preprocessing

• Data state: filtered

• Preprocessing applied: True

• Steps: downsampling, low-pass filter, baseline correction, artifact rejection

• Highpass filter: 0.016 Hz

• Lowpass filter: 250.0 Hz

• Bandpass filter: {‘low_cutoff_hz’: 0.016, ‘high_cutoff_hz’: 250.0}

• Filter type: hardware bandpass, Chebyshev low-pass for offline

• Artifact methods: min-max criterion (70 μV), variance criterion

• Re-reference: linked mastoids

• Downsampled to: 100.0 Hz

• Epoch window: [-0.2, 1.0]

• Notes: For offline analysis: downsampled to 200 Hz, low-pass filtered (42 Hz passband, 49 Hz stopband). For online: downsampled to 100 Hz. Artifact rejection: min-max ≥70 μV. Nontarget epochs filtered to avoid overlap with targets (3 preceding and 4 following stimuli must be nontargets).

Signal Processing

• Classifiers: LDA with shrinkage of covariance matrix

• Feature extraction: signed square values of point-biserial correlation coefficients

• Frequency bands: analyzed=[100.0, 800.0] Hz

• Spatial filters: LDA spatial filter

Cross-Validation

• Method: train on calibration, test on copy-spelling and free-spelling

• Evaluation type: within_session

Performance (Original Study)

• N200 Latency Overt Ms: 164.0

• N200 Latency Covert Ms: 180.0

• N200 Latency Motion Center Ms: 198.0

• P300 Latency Range Ms: 300-500

• N200 Latency Range Ms: 100-250

BCI Application

• Applications: speller, communication

• Environment: laboratory

• Online feedback: True

Tags

• Pathology: Healthy

• Modality: Visual

• Type: P300, VEP

Documentation

• Description: Exploring motion VEPs for gaze-independent communication

• DOI: 10.1088/1741-2560/9/4/045006

• Associated paper DOI: 10.1088/1741-2560/11/2/026009

• License: CC-BY-NC-ND-4.0

• Investigators: Sulamith Schaeff, Matthias Sebastian Treder, Bastian Venthur, Benjamin Blankertz

• Senior author: Benjamin Blankertz

• Contact: benjamin.blankertz@tu-berlin.de

• Institution: Berlin Institute of Technology

• Department: Neurotechnology Group

• Country: Germany

• Repository: BNCI Horizon

• Publication year: 2012

• Funding: DFG grant; grant nos s; BMBF grant; grant no MU MU

• Ethics approval: Declaration of Helsinki

• Keywords: motion visually evoked potentials, mVEP, BCI, speller, gaze-independent, covert attention, P300, N200

References

Treder, M. S., Purwins, H., Miklody, D., Sturm, I., & Blankertz, B. (2012). Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification. Journal of Neural Engineering, 11(2), 026009. https://doi.org/10.1088/1741-2560/11/2/026009 Notes .. versionadded:: 1.2.0 See Also BNCI2015_008 : Center Speller P300 dataset (gaze-independent) BNCI2015_009 : AMUSE auditory spatial P300 dataset BNCI2015_010 : RSVP visual speller (gaze-independent visual paradigm) 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	NM000212
Title	BNCI 2015-007 Motion VEP (mVEP) Speller dataset
Author (year)	Schaeff2015
Canonical	BNCI2015
Importable as	NM000212, Schaeff2015, BNCI2015
Year	2012
Authors	Sulamith Schaeff, Matthias Sebastian Treder, Bastian Venthur, Benjamin Blankertz
License	CC-BY-NC-ND-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: 16

• Recordings: 32

• Tasks: 1

Channels & sampling rate

• Channels: 63

• Sampling rate (Hz): 100.0

• Duration (hours): 19.95450555555556

Tags

• Pathology: Healthy

• Modality: Visual

• Type: Attention

Files & format

• Size on disk: 1.3 GB

• File count: 32

• Format: BIDS

License & citation

• License: CC-BY-NC-ND-4.0

• DOI: —

Provenance

• Source: nemar

• OpenNeuro: nm000212

• NeMAR: nm000212

API Reference

Use the NM000212 class to access this dataset programmatically.

class eegdash.dataset.NM000212(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

BNCI 2015-007 Motion VEP (mVEP) Speller dataset

Study:

nm000212 (NeMAR)

Author (year):

Schaeff2015

Canonical:

—

Also importable as: NM000212, Schaeff2015.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 16; recordings: 32; 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/nm000212 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000212

Examples

>>> from eegdash.dataset import NM000212
>>> dataset = NM000212(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

save(path, overwrite=False)

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

• eegdash.dataset.EEGDashDataset

• eegdash.dataset

• OpenNeuro dataset page

• NeMAR dataset page

• eegdash.dataset.DS001785

• eegdash.dataset.DS001787

• eegdash.dataset.DS001810

• eegdash.dataset.DS001849

• eegdash.dataset.DS001971