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Neurological injuries such as stroke, spinal cord injury, and neurodegenerative diseases often result in long-term motor impairments that challenge conventional rehabilitation methods. Reinforcement learning (RL), when integrated with biosignal-driven brain-computer interfaces (BCIs), offers a promising framework for personalized, adaptive neurorehabilitation. This chapter presents a comprehensive review of RL applications in BCIs across three domains: electroencephalography (EEG), electromyography (EMG), and multimodal systems. In EEG-based rehabilitation, RL enables closed-loop systems that adapt therapy in real-time based on neural oscillatory patterns, promoting neuroplasticity and motor recovery. EMG-based systems leverage RL for precise gesture recognition, adaptive exoskeleton control, and immersive virtual reality therapy. Multimodal approaches—such as EEG-fMRI integration—enhance the spatial and temporal resolution of brain state estimation and support novel neurofeedback paradigms. Through thematic categorization, case studies, and architectural insights, this chapter outlines the current state of RL-based neurorehabilitation, identifies key challenges and highlights future directions for scalable, intelligent, and patient specific intervention strategies.

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Reinforcement Learning-Driven Brain-Computer Interfaces for Neurorehabilitation: Trends, Methods, and Challenges

  • 章节

  • 发表日期：

    2026年

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  • Parthan Olikkal,
  • Sruthi Sundharram,
  • Sina Makhdoomi Kaviri,
  • Shivansh Sharma,
  • Ramana Vinjamuri

摘要

Neurological injuries such as stroke, spinal cord injury, and neurodegenerative diseases often result in long-term motor impairments that challenge conventional rehabilitation methods. Reinforcement learning (RL), when integrated with biosignal-driven brain-computer interfaces (BCIs), offers a promising framework for personalized, adaptive neurorehabilitation. This chapter presents a comprehensive review of RL applications in BCIs across three domains: electroencephalography (EEG), electromyography (EMG), and multimodal systems. In EEG-based rehabilitation, RL enables closed-loop systems that adapt therapy in real-time based on neural oscillatory patterns, promoting neuroplasticity and motor recovery. EMG-based systems leverage RL for precise gesture recognition, adaptive exoskeleton control, and immersive virtual reality therapy. Multimodal approaches—such as EEG-fMRI integration—enhance the spatial and temporal resolution of brain state estimation and support novel neurofeedback paradigms. Through thematic categorization, case studies, and architectural insights, this chapter outlines the current state of RL-based neurorehabilitation, identifies key challenges and highlights future directions for scalable, intelligent, and patient specific intervention strategies.