EEGdash›NeMAR›NM000212

Iss. 212 · 16 subjects · 32 recordings · CC-BY-NC-ND-4.0

Dataset Brief · BNCI 2015-007 Motion VEP (mVEP) Speller dataset

NM000212: eeg dataset, 16 subjects#

Name: BNCI 2015-007 Motion VEP (mVEP) Speller dataset
Published: 2012-01-01
License: CC-BY-NC-ND-4.0

BNCI 2015-007 Motion VEP (mVEP) Speller dataset

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

16-participant EEG dataset — BNCI 2015-007 Motion VEP (mVEP) Speller dataset.

Data & curation Sulamith Schaeff · Matthias Sebastian Treder · Bastian Venthur · Benjamin Blankertz
Year 2012 · Distributed via NeMAR
Funding DFG grant · grant nos s · + 2 more

EEG · 63 ch100 HzBIDS 1.9.0Task · p300HealthyVisualAttention

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 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},
}

§ 02Study · The README

About This Dataset#

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

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

BNCI 2015-007 Motion VEP (mVEP) Speller dataset

Target

├─ Sensory-event
├─ Experimental-stimulus

View full README

BNCI 2015-007 Motion VEP (mVEP) Speller dataset

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) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=16, range 24–24 yr, mean 23.0 yr)

20

Other · 16

Channel counts: 63 ch (n=32 recordings)

Sampling frequencies: 100.0 Hz (n=32 recordings)

Total recording duration: 19 h 57 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 63 ch · EEG · 100 Hz · 16 subjects, 32 recordings

Live trace viewer — sub-13 · ses-0 · task-p300 · run-0

Showing one representative recording out of 16 subjects and 32 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 · 63 sensors — 63 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.NM000212(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Schaeff2015

Canonical—

Importable asNM000212 · Schaeff2015

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

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

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)[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 — NM000212.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 nm000212 to reproduce the tutorial on this dataset.

Citation

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

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000194EEG · 12 subj NM000198EEG · 13 subj NM000249EEG · 13 subj NM000219EEG · 18 subj NM000260EEG · 23 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-NC-ND-4.0 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000212`
Title	BNCI 2015-007 Motion VEP (mVEP) Speller dataset
Author (year)	`Schaeff2015`
Canonical	—
Importable as	`NM000212`, `Schaeff2015`
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

NM000212: eeg dataset, 16 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#