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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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) NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000199`
Title	Learning from label proportions for a visual matrix speller (ERP)
Author (year)	`Hubner2017`
Canonical	—
Importable as	`NM000199`, `Hubner2017`
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

Electrode Layout#

Electrode layout — EEG · 31 sensors — 31 channels

Dataset Statistics#

Age distribution (n=13, range 26–26 yr)

25

Channel counts: 31 ch (n=342 recordings)

Sampling frequencies: 1000.0 Hz (n=342 recordings)

Total recording duration: 16 h 24 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

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:: —

Also importable as: NM000199, Hubner2017.

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: 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)