Neural activity in the human brain changes with age, often shifting from posterior to anterior regions. This is typically observed using Electroencephalography (EEG) data recorded during task-based paradigms. This study examines the prospect of observing similar changes during rest using resting state EEG data. Brain source localization using standardized Low-Resolution Electromagnetic Tomography (sLORETA), power distribution, and functional connectivity (FC) changes across different brain lobes have been observed. Age group classification using spherical and head harmonic domain, sensor, and source domain features has also been performed in conjunction with different deep learning models. The results show significant differences in connectivity between young and older adults, especially in the alpha band. Older adults exhibit decreased peak alpha power strength across all brain lobes, except the prefrontal and temporal regions. Brain activation declines more in the occipital and parietal lobes with age while remaining strong in the prefrontal, frontal, and temporal lobes. Age-related shifts in connectivity include increased connections in frontal and temporal regions and decreased connections in occipital, and parietal brain regions. The spherical and head harmonic decomposition effectively represents EEG signals and boosts the age group classification accuracy while offering dimensionality reduction benefits. Accuracy of up to 97.5% is achieved by utilizing higher-order decomposition features, thereby demonstrating the potential for age group classification of healthy aging.

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.