Enhancing Universal Access – EEG based Learnability Assessment In: Universal Access to Applications and Services

Christian Stickel, Josef Fink, Andreas Holzinger

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Abstract

This paper presents initial research on a new learnability assessment methodology. We propose the use of electroencephalography (EEG) to further improve usability testing. We discovered whether and to what extend there is a correlation between brainwave patterns and the learnability of the software used. Our central hypothesis is that learnability can be assessed by analyzing the rise and fall of specific frequency bands in electroencephalographic recordings. In order to collect empirical evidence for our hypothesis, we conducted an experiment with N=32 participants. We developed a test environment comprising a low-cost EEG system and developed software for analysis and testing. Based on our findings, we consider our EEG-based learnability test applicable, either as a pre-test – in order to determine whether further testing is necessary – or as an augmenting method during standard usability testing. The users’emotions, registered on the EEG, can be applied as a baseline for detecting possible usabilitydifficulties and employed in the development of a biological rapid-usability method for accessibility assessment.
Original languageEnglish
Title of host publicationUniversal Access in Human-Computer Interaction. Applications and Services
Place of PublicationBerlin, Heidelberg, New York
PublisherSpringer
Pages813-822
Volume4556
Edition1
ISBN (Print)978-3-540-73282-2
DOIs
Publication statusPublished - 2007

Publication series

NameLecture Notes in Computer Science
PublisherSpringer

Fingerprint

Electroencephalography
Testing
Frequency bands
Costs
Experiments

Keywords

  • Biological Usability Testing
  • EEG
  • methodology

ASJC Scopus subject areas

  • Computer Science Applications

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)
  • Experimental

Cite this

Stickel, C., Fink, J., & Holzinger, A. (2007). Enhancing Universal Access – EEG based Learnability Assessment In: Universal Access to Applications and Services. In Universal Access in Human-Computer Interaction. Applications and Services (1 ed., Vol. 4556, pp. 813-822). (Lecture Notes in Computer Science). Berlin, Heidelberg, New York: Springer. https://doi.org/10.1007/978-3-540-73283-9_88

Enhancing Universal Access – EEG based Learnability Assessment In: Universal Access to Applications and Services. / Stickel, Christian; Fink, Josef; Holzinger, Andreas.

Universal Access in Human-Computer Interaction. Applications and Services. Vol. 4556 1. ed. Berlin, Heidelberg, New York : Springer, 2007. p. 813-822 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Stickel, C, Fink, J & Holzinger, A 2007, Enhancing Universal Access – EEG based Learnability Assessment In: Universal Access to Applications and Services. in Universal Access in Human-Computer Interaction. Applications and Services. 1 edn, vol. 4556, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, New York, pp. 813-822. https://doi.org/10.1007/978-3-540-73283-9_88
Stickel C, Fink J, Holzinger A. Enhancing Universal Access – EEG based Learnability Assessment In: Universal Access to Applications and Services. In Universal Access in Human-Computer Interaction. Applications and Services. 1 ed. Vol. 4556. Berlin, Heidelberg, New York: Springer. 2007. p. 813-822. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-540-73283-9_88
Stickel, Christian ; Fink, Josef ; Holzinger, Andreas. / Enhancing Universal Access – EEG based Learnability Assessment In: Universal Access to Applications and Services. Universal Access in Human-Computer Interaction. Applications and Services. Vol. 4556 1. ed. Berlin, Heidelberg, New York : Springer, 2007. pp. 813-822 (Lecture Notes in Computer Science).
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