Singh Prerna, Ghanshani Eva, Mahajan Pooja, Kumar Lalan, Gandhi Tapan Kumar
Bharti School of Telecommunication Technology and Management, Indian Institute of Technology Delhi, Delhi, India.
Department of Electrical Engineering, Indian Institute of Technology Delhi, Delhi, India.
Med Biol Eng Comput. 2025 Sep 22. doi: 10.1007/s11517-025-03445-4.
This preliminary study investigates the temporal dynamics of multisensory integration in early to mid-adulthood. Five regions of interest (ROIs) were identified, and integration times from 0 to 500 ms were analyzed. The impact of temporal asynchrony on audio-visual integration was assessed through behavioral analysis. Brain topography-based age-related differences in multisensory processing, particularly in the middle-aged group, were observed. Early integration consistently occurs between 200 and 325 ms across age groups. Audio stimuli integrate slower than visual stimuli, with AV integration times falling in between. Delayed integration is observed in audio-leading conditions (A50V), while faster integration occurs in visual-leading conditions (V50A). ERP-based channel selection significantly enhances age group classification accuracy. The random forest classifier achieves 98.3% accuracy using a small set of 13 selected channels during the A50V task. This optimized channel selection improves the ergonomics of EEG-based age group classification and simplifies the clustering process. The study demonstrates the effectiveness of using minimal electrodes and straightforward features for multisensory integration tasks in early to mid-adulthood.
这项初步研究调查了成年早期到中期多感官整合的时间动态。确定了五个感兴趣区域（ROI），并分析了从0到500毫秒的整合时间。通过行为分析评估了时间异步对视听整合的影响。观察到基于脑地形图的多感官处理中与年龄相关的差异，特别是在中年组中。各年龄组在200至325毫秒之间始终会出现早期整合。听觉刺激的整合比视觉刺激慢，视听整合时间介于两者之间。在听觉领先条件（A50V）下观察到整合延迟，而在视觉领先条件（V50A）下则出现更快的整合。基于ERP的通道选择显著提高了年龄组分类的准确性。在A50V任务期间，随机森林分类器使用一小套13个选定通道可实现98.3%的准确率。这种优化的通道选择提高了基于脑电图的年龄组分类的人体工程学性能，并简化了聚类过程。该研究证明了在成年早期到中期的多感官整合任务中使用最少电极和简单特征的有效性。
