Could we Predict Flow from Ear-EEG?
2022 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)(2022)
Abstract
Advancements in wearable EEG could provide valuable foundations for studying flow experiences in everyday life. In this study, we report initial findings on using unobtrusive, comfortable around-the-ear EEG electrodes (cEEGrids) to monitor flow levels. Tree-based regression models show that flow reports across three different tasks can be predicted with a mean absolute error (MAE) of 11% across study participants. These results represent a potential starting point for further research with cEEGrids on the momentary capturing of flow in everyday life. Related limitations and propositions are discussed.
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Key words
Flow experience,Mental workload,Ear EEG,cEEGrids,Machine Learning,XGBoost
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