Assessing Dynamic Flow Experience from EEG Signals: A Processing-based Approach

DOI

As an interaction experience goal, the flow experience is characterized by its subjectivity and dynamism. Exploring objective methods to assess dynamic flow states is significant in enhancing user experience design, evaluation, and optimization. This study aims to model the dynamics of the flow experience and quantify its intensity using electroencephalography signals (EEG) from the perspective of the process. To achieve this, an interactive task is designed to induce dynamic changes in flow, and EEG signals from participants were recorded simultaneously, to form a flow assessment dataset. Subsequently, a frequency-aware convolutional Transformer model (FA-ConFormer) was proposed to extract dynamic features from EEG, with particular optimization for capturing complex dynamic features in the frequency domain. Experimental results demonstrate that FA-ConFormer outperforms existing methods in flow state and intensity recognition, the visualization of the flow process dynamically depicting the onset, development, peak, and decline of flow with varying intensities, which help to deepen the understanding of flow experience.