Real-Time Brain–Computer Interface Framework for Human–Machine Interaction

Abstract

Brain-computer interfaces are a new technology class that allows the brain to talk to machines directly without using nerves or muscles. A combination of this technology with the latest AI, signal processing, and neurophysiological monitoring starts a completely new era in improving both people and machines. During this time, cognitive and physical capabilities may be enhanced beyond what natural limits allow. Traditional ways of interacting with computers, such as by speech or manually, are not effective due to sensory-motor latencies and a lack of adaptation. BCIs, however, make use of patterns of brain activity that are visible through electroencephalography, functional near-infrared spectroscopy, or invasive neural implants to make the free flow of information easier and control adaptable. This paper presents a comprehensive paradigm for human-machine augmentation with non-invasive BCIs, emphasizing real-time neural decoding, adaptive feedback mechanisms, and hybrid integration with machine learning. Two investigations and a theory show that BCIs could be effective in rehabilitation, assistive automation, cognitive enhancement, and immersive online contexts. The proposed paradigm aims at the improvement of user-centered augmentation technologies that merge human intention with machine intelligence, fostering a symbiotic interaction of people with AI.