authors:
  -
    firstname: Lu
    lastname: Wang-Nöth
    affiliation: brainboost GmbH, Augsburgerstraße 4, 80337 Munich, Germany; Institute for Applied Computer Science, Bundeswehr University Munich, Werner-Heisenberg-Weg 39, 85579 Neubiberg, Germany
    id: 'ORCID:0009-0002-7443-121X'
  -
    firstname: Phillip
    lastname: Heiler
    affiliation: brainboost GmbH, Augsburgerstraße 4, 80337 Munich, Germany
  -
    firstname: Hai
    lastname: Huang
    affiliation: Institute for Applied Computer Science, Bundeswehr University Munich, Werner-Heisenberg-Weg 39, 85579 Neubiberg, Germany
    id: 'ORCID:0000-0001-8745-8142'
  -
    firstname: Daniel
    lastname: Lichtenstern
    affiliation: brainboost GmbH, Augsburgerstraße 4, 80337 Munich, Germany
  -
    firstname: Alexandra
    lastname: Reichenbach
    affiliation: Center for Machine Learning, Heilbronn University, Max-Planck-Str. 39, 74081 Heilbronn, Germany
    id: 'ORCID:0000-0003-4199-3005'
  -
    firstname: Luis
    lastname: Flacke
    affiliation: brainboost GmbH, Augsburgerstraße 4, 80337 Munich, Germany
  -
    firstname: Linus
    lastname: Maisch
    affiliation: brainboost GmbH, Augsburgerstraße 4, 80337 Munich, Germany
  -
    firstname: Helmut
    lastname: Mayer
    affiliation: Institute for Applied Computer Science, Bundeswehr University Munich, Werner-Heisenberg-Weg 39, 85579 Neubiberg, Germany
    id: 'ORCID:0000-0002-9439-2695'    
    
    
title: 'How Many Data are Enough? Optimization of Data Collection for Artifact Detection in EEG Recordings'
description: "
This dataset, used for EMG artifact detection in EEG recordings, contains both artifact-contaminated 
signals and resting-state eyes-open (EO) signals. It includes 932 numpy files of EEG recordings from seven subjects, 
consisting of 664 artifact-containing epochs and 268 EO epochs.\n Each subject (identified by a subjectID ranging 
from 5 to 11; note that subjects 1 to 4 are not included in this dataset) participated in seven isometric contraction 
artifact tasks, each lasting 5 seconds and repeated 10 times, as well as five continuous movement tasks, each lasting 
10 seconds and repeated 5 times. This results in 95 artifact-containing epochs per subject, with the exception of 
subject 7, who had one less repetition for the \"kh_a\" artifact task. \n

Additionally, each subject provided EO recordings as well, which were segmented into alternating 10-second and 
5-second epochs without overlap. On average, each subject contributed 38 ± 7 EO epochs. \n

Epochs were extracted from the original EDF files for each subject. All subjects, except subject 5, had one EDF 
file containing all the necessary epochs. For subject 5, the epochs were spread across two EDF files. Each numpy 
file represents a single epoch.\n 
"

keywords:
  - Neuroscience
  - EEG
  - EMG
  - Artifact Detection
  - Data Collection Optimization

license:
  name: 'Creative Commons CC0 1.0 Public Domain Dedication'
  url: 'https://creativecommons.org/publicdomain/zero/1.0/'
funding:
  - 'Federal Ministry for Economic Affairs and Climate Action of Germany, ZIM KK5211501BM0'
references:
  -
    id: 'doi:tba'
    reftype: IsSupplementTo
    citation: Lu Wang-Nöth, Philipp Heiler, Hai Huang, Daniel Lichtenstern, Alexandra Reichenbach, Luis Flacke, Linus Maisch, Helmut Mayer: How Many Data are Enough? Optimization of Data Collection for Artifact Detection in EEG Recordings. Journal of Neural Engineering. To be submitted.
resourcetype: Dataset
templateversion: 1.2