The Evolution of Brain-Computer Interfaces Towards Collaborative Intelligence

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ID: 333653
2026
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Abstract
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.
Reference Key
openalex_W7223273819 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yang Yu, Yingxin Liu, Zhipeng Fan, Zongtan Zhou, Ling‐Li Zeng, Dewen Hu
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Journal national science review
Year 2026
DOI
10.1093/nsr/nwag667
URL
https://doi.org/10.1093/nsr/nwag667
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