Implementation and Performance Optimization of a Multimodal EEG Monitoring System on the RK3568 Platform

This study addresses the limitations of traditional EEG monitoring systems by designing and implementing a multimodal EEG monitoring system based on the RK3568 platform. By analyzing the characteristics of the RK3568 hardware platform, we constructed the system architecture, clarified multimodal data acquisition and processing workflows, and developed motion artifact suppression and multimodal feature fusion algorithms with optimized deployment. Performance evaluations demonstrate that the system exhibits significant advantages in computational latency, energy efficiency ratio, and core system performance. The motion artifact suppression module, feature fusion module, and end-to-end total latency show notable acceleration in NPU mode compared to CPU mode: the energy efficiency ratio improves by 20.4 times, the end-to-end total latency reduces by 73%, and epilepsy detection sensitivity increases by 14.2%. The system demonstrates outstanding advantages in rehabilitation therapy and exhibits broad application prospects.