Could we Predict Flow from Ear-EEG?

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.