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

Published version
Peer-reviewed

Repository DOI


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Authors

Sieghartsleitner, Sebastian  ORCID logo  https://orcid.org/0009-0007-9222-9409
Schreiner, Leonhard  ORCID logo  https://orcid.org/0009-0004-3272-6866
Walchshofer, Martin 

Abstract

Objective. 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). 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. 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 rm = 0.44, p < 0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states. Significance. This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications.

Description

Keywords

brain–computer interface, tetris, d2 test, engagement, video, passive BCI, EEG

Journal Title

Journal of Neural Engineering

Conference Name

Journal ISSN

1741-2560
1741-2552

Volume Title

21

Publisher

IOP Publishing