AI Augmented Brain User Interfaces for IoT: Towards a Seamless Human Technology Symbiosis

Abstract

The synergy of Artificial Intelligence (AI), Brain-User Interfaces (BUI), and the Internet of Things (IoT) is transforming human-machine interfaces through natural and adaptive control of smart environments. The integrated technology has far-reaching implications for assistive technologies, neuroprosthetics, and cognitive augmentation. Current BUI systems, however, are confronted with limitations like low classification accuracy, high latency, and noise sensitivity, among others that affect deployment feasibility. Overcoming these limitations, this study formulates an AI-assisted BUI model that leverages deep learning models, Edge AI processing, and 5G wireless networking for real-time neural signal processing. Vision transformer for EEG (ViT-EEG), Convolutional Neural Networks (CNNs), and Reinforcement Learning (RL) are integrated into this system to improve EEG classification and IoT device control. In comparison with traditional approaches involving extensive retraining for new users, this model utilizes real-time adaptive learning to adapt interactions according to individualized neural patterns. Techniques like Independent Component Analysis (ICA) and Wavelet Transform (WT) are applied to signal artifact mitigation, providing deep learning models’ high-quality inputs. Edge AI is leveraged using hardware components comprising NVIDIA Jetson Nano and Raspberry Pi 5, thereby minimizing dependence on cloud servers and achieving incredibly low latency (180 ms). In addition, 5G and Multi-access Edge Computing (MEC) facilitate the efficient execution of neural commands. Adding Deep Q-Networks (DQN), continuous system adaptation is achieved, and classification accuracy and user interaction efficiency are improved. Experimental outcomes drawn from various EEG datasets and IoT realworld environments showed excellent performance where ViT-EEG had 96.8% accuracy, and reinforcement learning enhanced accuracy from 90.2 to 97.3% after 100 training iterations. The system has vast applications from assistive technology, neurorehabilitation, industrial automation, and military operations. Despite its strength, there remain challenges regarding data security, privacy, and scalability. Future work will concentrate on multi-modal biosignal fusion, privacy-preserving AI, and energyefficient models, leading to next-generation, real-time AI-based BUI-IoT systems that improve human-machine interaction.