Truthiness: Challenges associated with employing machine learning on neurophysiological sensor data

Mark Costa, Sarah Bratt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The use of neurophysiological sensors in HCI research is increasing in use and sophistication, largely because such sensors offer the potential benefit of providing “ground truth” in studies, and also because they are expected to underpin future adaptive systems. Sensors have shown significant promise in the efforts to develop measurements to help determine users’ mental and emotional states in real-time, allowing the system to use that information to adjust user experience. Most of the sensors used generate a substantial amount of data, a high dimensionality and volume of data that requires analysis using powerful machine learning algorithms. However, in the process of developing machine learning algorithms to make sense of the data and subject’s mental or emotional state under experimental conditions, researchers often rely on existing and imperfect measures to provide the “ground truth” needed to train the algorithms. In this paper, we highlight the different ways in which researchers try to establish ground truth and the strengths and limitations of those approaches. The paper concludes with several suggestions and specific areas that require more discussion.

Original languageEnglish (US)
Title of host publicationFoundations of Augmented Cognition
Subtitle of host publicationNeuroergonomics and Operational Neuroscience - 10th International Conference, AC 2016 and Held as Part of HCI International 2016, Proceedings
EditorsCali M. Fidopiastis, Dylan D. Schmorrow
PublisherSpringer Verlag
Pages159-164
Number of pages6
ISBN (Print)9783319399546
DOIs
StatePublished - Jan 1 2016
Event10th International Conference on Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience, AC 2016 and Held as Part of 18th International Conference on Human-Computer Interaction, HCI International 2016 - Toronto, Canada
Duration: Jul 17 2016Jul 22 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9743
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Conference on Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience, AC 2016 and Held as Part of 18th International Conference on Human-Computer Interaction, HCI International 2016
CountryCanada
CityToronto
Period7/17/167/22/16

Keywords

  • Cognitive data
  • fNIRS
  • Machine learning
  • Method validity
  • Neurophysiological sensors

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Costa, M., & Bratt, S. (2016). Truthiness: Challenges associated with employing machine learning on neurophysiological sensor data. In C. M. Fidopiastis, & D. D. Schmorrow (Eds.), Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience - 10th International Conference, AC 2016 and Held as Part of HCI International 2016, Proceedings (pp. 159-164). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9743). Springer Verlag. https://doi.org/10.1007/978-3-319-39955-3_15