The Evolution of Brain-Computer Interfaces Towards Collaborative Intelligence

Abstract

Brain-computer interfaces (BCIs) are evolving from unidirectional control toward deeper integration of biological and machine intelligence. This transition is constrained by two complementary bottlenecks: Intent Mismatch, or Brain-to-Machine Underreach, in which user intentions cannot be sufficiently decoded, represented, or executed by the machine; and State Mismatch, or Machine-to-Brain Overload, in which system demands exceed the user’s current cognitive, affective, or neural resources. This review uses these two mismatches as an organizing framework for examining the evolution of BCIs toward brain-machine collaborative intelligence, while distinguishing non-invasive and invasive modalities according to their signal characteristics and translational constraints. We review neural decoding, generative artificial intelligence, and shared control as approaches for reducing Intent Mismatch, and passive BCIs, adaptive interaction, neurofeedback, and neuromodulation as approaches for reducing State Mismatch. By integrating these solution paths, this review outlines a pathway toward adaptive, bidirectional, and human-centered BCI systems.