Abstract
We present an automated annotation method, which infers emotional arousal and valence tags based on physiological signals. This is performed with the help of binary detectors trained to recognize high/low arousal or passive/negative valence. The arousal and valence detectors were created from representative datasets containing evoked emotional reactions to widely used audio-visual stimuli. Next, these detectors were used to annotate physiological signals collected during different types of cognitive activity in the context of acute stress scenarios. We show that the automatically generated tags are correlated with work efficiency during various cognitive activities. The availability of such an automated annotation method would facilitate future studies on the influence of individual differences concerning work performance and the ability to cope with acute stress and cognitive overload. Such functionality could be essential for creating adaptive human-machine interfaces that account for the person’s current emotions, cognitive load, and acute stress level.
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Acknowledgements
This research was supported by the Bulgarian National Science Fund (BNSF), with grant agreement FNI № KP-06-PN37/18, entitled “Investigation on intelligent human-machine interaction interfaces, capable of recognising high-risk emotional and cognitive conditions”.
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Markova, V., Ganchev, T., Markov, M. (2021). Automated Annotation of Valence and Arousal During Cognitive Activity. In: Rojas, I., Castillo-Secilla, D., Herrera, L.J., Pomares, H. (eds) Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021. Lecture Notes in Computer Science(), vol 12940. Springer, Cham. https://doi.org/10.1007/978-3-030-88163-4_5
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DOI: https://doi.org/10.1007/978-3-030-88163-4_5
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