Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls

Estimating cognitive or affective state from neurophysiological signals and designing applications that make use of this information requires expertise in many disciplines such as neurophysiology, machine learning, experimental psychology, and human factors. This makes it difficult to perform resear...

Full description

Saved in:
Bibliographic Details
Published inFrontiers in neuroscience Vol. 9; p. 136
Main Authors Brouwer, Anne-Marie, Zander, Thorsten O, van Erp, Jan B F, Korteling, Johannes E, Bronkhorst, Adelbert W
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Research Foundation 30.04.2015
Frontiers Media S.A
Subjects
Affective Computing
Automation
BCI
Brain research
Cognitive ability
Communication
EEG
Emotional behavior
Interdisciplinary aspects
Learning algorithms
Mental State Monitoring
neuroergonomics
Neurophysiology
Neuroscience
Neurosciences
Physiology
Psychology
Psychophysiology
Signal processing
Workloads
Netherlands
affective computing
EEG
mental state estimation
physiological computing
applied neuroscience
neuroergonomics
passive BCI
Online AccessGet full text

Cover

More Information
Summary:Estimating cognitive or affective state from neurophysiological signals and designing applications that make use of this information requires expertise in many disciplines such as neurophysiology, machine learning, experimental psychology, and human factors. This makes it difficult to perform research that is strong in all its aspects as well as to judge a study or application on its merits. On the occasion of the special topic "Using neurophysiological signals that reflect cognitive or affective state" we here summarize often occurring pitfalls and recommendations on how to avoid them, both for authors (researchers) and readers. They relate to defining the state of interest, the neurophysiological processes that are expected to be involved in the state of interest, confounding factors, inadvertently "cheating" with classification analyses, insight on what underlies successful state estimation, and finally, the added value of neurophysiological measures in the context of an application. We hope that this paper will support the community in producing high quality studies and well-validated, useful applications.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-3
content type line 23
ObjectType-Review-1
ObjectType-Article-1
ObjectType-Feature-2
This article was submitted to Neuroprosthetics, a section of the journal Frontiers in Neuroscience
Edited by: José Del R. Millán, Ecole Polytechnique Fédérale de Lausanne, Switzerland
Reviewed by: Fabio Babiloni, Sapienza University of Rome, Italy; Stephen Fairclough, Liverpool John Moores University, UK; Javier Minguez, University of Zaragoza, Spain
ISSN:1662-4548
1662-453X
1662-453X
DOI:10.3389/fnins.2015.00136