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

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).&#xD;Approach: 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.&#xD;Main results: 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 (p = 0.025 and p < 0.001, respectively); greater during medium and fast compared to slow Tetris speed (p < 0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (r

Informations de copyright

Creative Commons Attribution license.