Bayesian Inference of Hidden Cognitive Performance and Arousal States in Presence of Music
<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 emp...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10474164/ |
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| Summary: | <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. |
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| ISSN: | 2644-1276 |