Real-time estimation of EEG-based engagement in different tasks.

Abstract

<i>Objective.</i>Recent trends in brain-computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG).<i>Approach.</i>Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects' perceived engagement was quantified using a questionnaire.<i>Main results.</i>The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects' perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (<i>p</i>= 0.025 and<i>p</i><0.001, respectively); greater during medium and fast compared to slow Tetris speed (<i>p</i><0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (<i>r</i>_<i>rm</i>= 0.44,<i>p</i><0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states.<i>Significance.</i>This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications.