NM000199: eeg dataset, 13 subjects#

Learning from label proportions for a visual matrix speller (ERP)

Access recordings and metadata through EEGDash.

Citation: David Hübner, Thibault Verhoeven, Konstantin Schmid, Klaus-Robert Müller, Michael Tangermann, Pieter-Jan Kindermans (2017). Learning from label proportions for a visual matrix speller (ERP).

Modality: eeg Subjects: 13 Recordings: 342 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000199

dataset = NM000199(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000199(cache_dir="./data", subject="01")

Advanced query

dataset = NM000199(
    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{nm000199,
  title = {Learning from label proportions for a visual matrix speller (ERP)},
  author = {David Hübner and Thibault Verhoeven and Konstantin Schmid and Klaus-Robert Müller and Michael Tangermann and Pieter-Jan Kindermans},
}

About This Dataset#

Learning from label proportions for a visual matrix speller (ERP)

Learning from label proportions for a visual matrix speller (ERP) dataset from Hübner et al 2017 [1]_.

Dataset Overview

  • Code: Huebner2017

  • Paradigm: p300

  • DOI: 10.1371/journal.pone.0175856

View full README

Learning from label proportions for a visual matrix speller (ERP)

Learning from label proportions for a visual matrix speller (ERP) dataset from Hübner et al 2017 [1]_.

Dataset Overview

  • Code: Huebner2017

  • Paradigm: p300

  • DOI: 10.1371/journal.pone.0175856

  • Subjects: 13

  • Sessions per subject: 3

  • Events: Target=10002, NonTarget=10001

  • Trial interval: [-0.2, 0.7] s

  • Runs per session: 9

  • Session IDs: session_1

  • File format: BrainVision

Acquisition

  • Sampling rate: 1000.0 Hz

  • Number of channels: 31

  • Channel types: eeg=31, misc=6

  • Channel names: C3, C4, CP1, CP2, CP5, CP6, Cz, EOGvu, F10, F3, F4, F7, F8, F9, FC1, FC2, FC5, FC6, Fp1, Fp2, Fz, O1, O2, P10, P3, P4, P7, P8, P9, Pz, T7, T8, x_EMGl, x_GSR, x_Optic, x_Pulse, x_Respi

  • Montage: standard_1020

  • Hardware: BrainAmp DC

  • Reference: nose

  • Ground: FCz

  • Sensor type: passive Ag/AgCl

  • Line frequency: 50.0 Hz

  • Impedance threshold: 20.0 kOhm

  • Cap manufacturer: EasyCap

  • Auxiliary channels: EOG (1 ch, vertical), pulse, respiration

Participants

  • Number of subjects: 13

  • Health status: healthy

  • Age: mean=26.0, std=1.5

  • Gender distribution: female=5, male=8

  • BCI experience: mostly naive

  • Species: human

Experimental Protocol

  • Paradigm: p300

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Trial duration: 25.0 s

  • Study design: Visual ERP speller copy-spelling task using a 6x7 grid with learning from label proportions (LLP) classifier. Two sequences with different target/non-target ratios: sequence 1 (3 targets/8 stimuli), sequence 2 (2 targets/18 stimuli). Unsupervised calibrationless approach.

  • Feedback type: visual

  • Stimulus type: character matrix

  • Stimulus modalities: visual

  • Primary modality: visual

  • Synchronicity: synchronous

  • Mode: online

  • Training/test split: False

  • Instructions: Copy-spelling task: subjects spelled the sentence ‘FRANZY JAGT IM KOMPLETT VERWAHRLOSTEN TAXI QUER DURCH FREIBURG’ three times

  • Stimulus presentation: soa_ms=250, stimulus_duration_ms=100, grid_size=6x7, highlighting_method=salient (brightness enhancement, rotation, enlargement, trichromatic grid overlay), viewing_distance_cm=80, screen_size_inches=24

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: 42

  • Stimulus onset asynchrony: 250.0 ms

Data Structure

  • Trials: 12852

  • Trials context: 68 highlighting events per character, 63 characters per sentence, 3 sentences = 68*63*3 = 12852 EEG epochs per subject. Each epoch is a Target (10002) or NonTarget (10001) event.

Preprocessing

  • Data state: raw

  • Preprocessing applied: False

Signal Processing

  • Classifiers: LLP (Learning from Label Proportions), shrinkage-LDA, EM-algorithm

  • Feature extraction: mean amplitude per time interval

  • Frequency bands: analyzed=[0.5, 8.0] Hz

Cross-Validation

  • Method: 5-fold chronological cross-validation

  • Folds: 5

  • Evaluation type: within_subject

Performance (Original Study)

  • Accuracy: 84.5%

  • Auc: 0.975

  • Online Spelling Accuracy: 84.5

  • Post Hoc Spelling Accuracy: 95.0

  • Accuracy After Rampup: 90.2

  • Supervised Auc: 0.975

  • Max Spelling Speed Chars Per Min: 2.4

BCI Application

  • Applications: speller, communication

  • Environment: laboratory

  • Online feedback: True

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Research

Documentation

  • DOI: 10.1371/journal.pone.0175856

  • License: CC-BY-4.0

  • Investigators: David Hübner, Thibault Verhoeven, Konstantin Schmid, Klaus-Robert Müller, Michael Tangermann, Pieter-Jan Kindermans

  • Senior author: Michael Tangermann

  • Contact: david.huebner@blbt.uni-freiburg.de; michael.tangermann@blbt.uni-freiburg.de; p.kindermans@tu-berlin.de

  • Institution: Albert-Ludwigs-University

  • Department: Brain State Decoding Lab, Cluster of Excellence BrainLinks-BrainTools, Department of Computer Science

  • Address: Freiburg, Germany

  • Country: DE

  • Repository: Zenodo

  • Data URL: http://doi.org/10.5281/zenodo.192684

  • Publication year: 2017

  • Funding: BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG), grant number EXC 1086; bwHPC initiative, grant INST 39/963-1 FUGG; European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 657679; Special Research Fund from Ghent University; BK21 program funded by Korean National Research Foundation grant No. 2012-005741

  • Ethics approval: Ethics Committee of the University Medical Center Freiburg; Declaration of Helsinki

  • Keywords: brain-computer interface, BCI, event-related potentials, ERP, P300, learning from label proportions, LLP, unsupervised learning, calibrationless, visual speller

Abstract

Using traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. This work introduces learning from label proportions (LLP) to the BCI community as a new unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The LLP estimates the mean target and non-target responses based on known proportions of these two classes in different groups of the data. We present a visual ERP speller to meet the requirements of LLP. For evaluation, we ran simulations on artificially created data sets and conducted an online BCI study with 13 subjects performing a copy-spelling task. Theoretical considerations show that LLP is guaranteed to minimize the loss function similar to a corresponding supervised classifier. LLP performed well in simulations and in the online application, where 84.5% of characters were spelled correctly on average without prior calibration.

Methodology

The experiment used a modified visual ERP speller with a 6×7 grid. Two distinct stimulus sequences with different target/non-target ratios were used: sequence 1 had 3 targets in 8 stimuli, sequence 2 had 2 targets in 18 stimuli. Each trial consisted of 4 sequences of length 8 and 2 sequences of length 18, totaling 68 highlighting events per character. The LLP algorithm exploited these known proportions to reconstruct mean target and non-target ERP responses without requiring labeled data. The classifier was reset at the start of each sentence and retrained after each character. Subjects spelled a German pangram sentence three times. One subject (S2) had prior EEG experience; others were naive. Sessions lasted about 3 hours including setup. Participants were compensated 8 Euros per hour.

References

Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., & Kindermans, P. J. (2017) Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees. PLOS ONE 12(4): e0175856. https://doi.org/10.1371/journal.pone.0175856 .. versionadded:: 0.4.5 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

NM000199

Title

Learning from label proportions for a visual matrix speller (ERP)

Author (year)

Hubner2017

Canonical

Huebner2017

Importable as

NM000199, Hubner2017, Huebner2017

Year

2017

Authors

David Hübner, Thibault Verhoeven, Konstantin Schmid, Klaus-Robert Müller, Michael Tangermann, Pieter-Jan Kindermans

License

CC-BY-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: 13

  • Recordings: 342

  • Tasks: 1

Channels & sampling rate

  • Channels: 31

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 16.410199166666665

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 5.1 GB

  • File count: 342

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000199 class to access this dataset programmatically.

class eegdash.dataset.NM000199(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Learning from label proportions for a visual matrix speller (ERP)

Study:

nm000199 (NeMAR)

Author (year):

Hubner2017

Canonical:

Huebner2017

Also importable as: NM000199, Hubner2017, Huebner2017.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 13; recordings: 342; 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/nm000199 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000199

Examples

>>> from eegdash.dataset import NM000199
>>> dataset = NM000199(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#