Decoding intrinsic fluctuations of engagement from EEG signals during fingertip motor tasks.

Abstract

Maintaining a high mental engagement is critical for motor rehabilitation interventions. Achieving a flow experience, often conceptualized as a highly engaged mental state, is an ideal goal for motor rehabilitation tasks. This paper proposes a virtual reality-based fine fingertip motor task in which the difficulty is maintained to match individual abilities. The aim of this study is to decode the intrinsic fluctuations of flow experience from electroencephalogram (EEG) signals during the execution of a motor task, addressing a gap in flow research that overlooks these fluctuations. To resolve the conflict between sparse self-reported flow sampling and the high dimensionality of neural signals, we use motor behavioral measures to represent flow and label the EEG data, thereby increasing the number of samples. A machine learning-based neural decoder is then established to classify each trial into high-flow or low-flow using spectral power and coherence features extracted from the EEG signals. Cross-validation reveals that the classification accuracy of the neural decoder can exceed 80%. Notably, we highlight the contributions of high-frequency bands in EEG activities to flow decoding. Additionally, EEG feature analyses reveal significant increases in the power of parietal-occipital electrodes and global coherence values, specifically in the alpha and beta bands, during high-flow durations. This study validates the feasibility of decoding the intrinsic flow fluctuations during fine motor task execution with a high accuracy. The methodology and findings in this work lay a foundation for future applications in manipulating flow experience and enhancing engagement levels in motor rehabilitation practice.