Effective Age Group Classification with ERP-Based Channel Selection in Audio-Visual Tasks

This preliminary study examines the temporal dynamics of multisensory integration in early to mid-adulthood, identifying five Regions of Interest (ROIs) and analyzing integration times from 0-500ms. Age-related differences in multisensory processing, especially in the middle-aged group, are noted. The use of ERP-based channel selection played a crucial role in enhancing age group classification accuracy, achieving 96.6% accuracy with the Random Forest classifier using a small set of selected 13 channels. Results show early integration consistently occurs within 200-325ms across age groups, with audio stimuli integrating slower than visual stimuli, and AV integration times falling in between. The study finds delayed early integration in audio-leading conditions (A50V) and faster integration in visualleading conditions (V50A). This optimized channel selection improves the ergonomics of EEG-based age group classification and simplifies the clustering process. It demonstrates the effectiveness of using minimal electrodes and straightforward features for multisensory integration tasks during early to mid-adulthood.