Flow and Optimal Difficulty in the Portable EEG: On the Potentiality of using Personalized Frequency Ranges for State Detection

Michael Knierim, Mario Nadj, Christof Weinhardt

Abstract

The experience of flow has been centrally linked to peak task performances and heightened well-being. To more effectively elicit these outcomes, flow is increasingly studied using neurophysiological measures. For example, portable EEG is employed to enable automatic state detection required for adaptive system design. However, so far, there is a lack of highly diagnostic findings, and moderately diagnostic ones relate more strongly to a central flow pre-condition – namely optimal task difficulties. Unfortunately, even these metrics might be infeasible in real-world scenarios and for portable EEG systems without midline electrodes. In this work, we discuss how frequency band personalization and separation could provide options to overcome these problems. Results from an experiment with a task manipulated in difficulty highlight that upper Alpha and Beta ranges show differentiating patterns to their lower frequency counterparts (i.e. within bands). These sub-bands could be used to detect instances of higher flow and optimized difficulty using portable EEG.

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