Bayesian Inference of Hidden Cognitive Performance and Arousal States in Presence of Music

Saman Khazaei, Md Rafiul Amin, Maryam Tahir, Rose T. Faghih

Research output: Contribution to journalArticlepeer-review

Abstract

<italic>Goal:</italic> Poor arousal management may lead to reduced cognitive performance. Specifying a model and decoder to infer the cognitive arousal and performance contributes to arousal regulation via non-invasive actuators such as music. <italic>Methods:</italic> We employ a Bayesian filtering approach within an expectation-maximization framework to track the hidden states during the <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>-back task in the presence of calming and exciting music. We decode the arousal and performance states from the skin conductance and behavioral signals, respectively. We derive an arousal-performance model based on the Yerkes-Dodson law. We design a performance-based arousal decoder by considering the corresponding performance and skin conductance as the observation. <italic>Results:</italic> The quantified arousal and performance are presented. The existence of Yerkes-Dodson law can be interpreted from the arousal-performance relationship. Findings display higher matrices of performance within the exciting music. <italic>Conclusions:</italic> The performance-based arousal decoder has a better agreement with the Yerkes-Dodson law. Our study can be implemented in designing non-invasive closed-loop systems.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalIEEE Open Journal of Engineering in Medicine and Biology
DOIs
StateAccepted/In press - 2024

Keywords

  • Affective computing
  • Bayes methods
  • Decoding
  • Filtering
  • Music
  • Skin
  • State-space methods
  • Task analysis
  • biomedical signal processing
  • estimation
  • state-space methods

ASJC Scopus subject areas

  • Biomedical Engineering

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