Optimized Brain-Computer Interface for Smart Environments: Employing Transfer Learning and Edge AI for IoT Control

Abstract

The quick evolution of Brain-Computer Interfaces (BCIs) has opened up new horizons for IoT device control in smart spaces. Nevertheless, conventional BCI systems are plagued with high latency, computational inefficiency, and limited resources, making real-time processing challenging. In this research, an optimized BCI system based on Transfer Learning and Edge AI is proposed to improve neural signal classification and facilitate real-time IoT control. Transfer Learning utilizes pre-trained neural networks to classify EEG signals with little training data, while Edge AI provides low-latency processing by performing computations on edge devices directly, minimizing cloud-based model dependence. Experimental results demonstrate that the proposed framework has 92.0% classification accuracy, much higher than traditional CNNs (82.1%), and decreases latency from 35.0 ms to 24.0 ms and power consumption from 25.0 MJ/inference to 18.0 MJ/inference. These advancements showcase the efficacy of the framework in assistive technology, home automation, and industrial control systems. Upcoming advancements are federated learning for privacy-preserving model updates, optimizations of deep neural networks, and real-world deployments across varied smart environments. This research showcases the promise of Transfer Learning and Edge AI in creating scalable, efficient, and real-time BCI applications for smart human-machine interaction.