Topic: AI-Powered Brain-Computer Interfaces: Methods and Applications

Brain-Computer Interfaces (BCIs) have emerged as a transformative technology enabling direct communication between the human brain and external devices. They hold tremendous potential in fields such as neurorehabilitation, assistive robotics, neuroergonomics, and human-artificial intelligence (AI) symbiosis. Recent breakthroughs in AI—including deep learning, reinforcement learning, and generative modelling—have significantly enhanced neural signal decoding, adaptive control, and closed-loop feedback mechanisms. These advancements are paving the way for next-generation BCIs that are more robust, accurate, and personalized.

This Special Issue aims to gather cutting-edge research on AI-powered BCIs, spanning innovative algorithms, multimodal data fusion, hardware-software co-design, and real-world applications. We welcome original research articles and reviews that advance the state of the art in this rapidly evolving domain.

Topics of interest include, but are not limited to:

● Deep learning and reinforcement learning for neural decoding and intention recognition;

● Adaptive and personalized BCI systems for rehabilitation and assistive technologies;

● Multimodal fusion of electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), electromyogram (EMG), and other biosignals;

● AI-driven closed-loop BCI architectures and feedback optimization;

● Robust, explainable, and trustworthy AI for BCIs;

● Applications in neurorehabilitation, exoskeletons, virtual reality (VR)/augmented reality (AR)/extended reality (XR) interaction, and human–robot collaboration.

Brain-computer interface (BCI), artificial intelligence, neural decoding, multimodal fusion, adaptive control, neurorehabilitation, human-AI collaboration