A Hybrid Brain-Computer Interface for IoT Automation: Integrating Functional Near-Infrared Spectroscopy and Neuromorphic Computing

Abstract

Brain-computer interfaces (BCIs) have the potential to change the nature of human-machine interaction, particularly in healthcare and IoT applications. However, challenges such as poor signal fidelity, energy consumption and removal of noise preclude their wider usage. The work presented in this study entails Hybrid Brain-Computer Interface Models for resolving these issues via combinations of Graph Signal Processing (GSP), Particle Filters and Energy Harvesting technologies in such a way that GSP compresses EEG signal dimension while preserving all its significant features to improve classification. Non-Gaussian noise is minimized and the signals are corrected by particle filters with energy harvesting such as solar-powered MPPT systems guaranteeing constant work with the least amount of energy. The hybrid model uses all the proposed methods synergistically, improving classification by a margin of 92.5%, achieving a reduction in power consumption by 30%, and reducing noise by 60%. The improvement gives it better data transfer efficiency and system robustness. These results suggest that merging different advanced signal-processing techniques with energy-efficient solutions might enhance the overall performance and reliability of BCI systems. The hybrid BCI model offers a promising framework for the implementation of real-time applications in healthcare and IoT, potentially further improved by energy optimization and machine learning algorithms for classification.