“Hello Computer, how am I feeling?”, case studies of neural technology to measure emotions

Daly, I and Williams, DAH ORCID: https://orcid.org/0000-0003-4793-8330 2020, '“Hello Computer, how am I feeling?”, case studies of neural technology to measure emotions' , in: Neuroergonomics , Cognitive Science and Technology , Springer, pp. 193-219.

PDF - Accepted Version
Download (631kB) | Preview


Emotion is a core part of the human experience. Many artistic and creative applications attempt to produce particular emotional experiences, for example, films, games, music, dance, and other visual arts. However, while emotional states are ubiquitous, they are also complex, proving difficult to describe to others by conventional psychometric means (e.g., traditional self-report mechanisms). Neural technology offers the potential to circumvent these difficulties by allowing the creation of a real-time, objective, metric of felt emotion to assist in emotionally driven experience design across a range of disciplines. This chapter discusses how neural technology based on the processing of the electroencephalogram may be used to measure human emotions in natural environments. We also present a set of case studies of applications that use neural technology to measure emotions. We are particularly interested in the use of neural technology to inform applications which can respond to the felt experience of the individual. We describe two case studies focused on driving scenarios and brain–computer music interfacing. The chapter concludes with a discussion of the challenges inherent in developing neural technology to measure emotion and a set of suggestions for future research directions in developing applications that use neural technology as an objective measure of emotion.

Item Type: Book Section
Editors: Nam, CS
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Publisher: Springer
Series Name: Cognitive Science and Technology
ISBN: 9783030347840 (online); 9783030347833 (print)
ISSN: 2195-3988
Related URLs:
Depositing User: DAH Williams
Date Deposited: 04 Mar 2020 09:32
Last Modified: 28 Feb 2022 02:30
URI: https://usir.salford.ac.uk/id/eprint/56576

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)


Downloads per month over past year