AI-Driven Adaptive Brain-Computer Interfaces for Personalized Neurorehabilitation

Abstract

This chapter per the authors examines advances in brain–computer interface (BCI) technologies for neurorehabilitation, emphasizing the integration of artificial intelligence to address challenges in adaptability, patient variability, and neural signal decoding. It reviews how machine learning and deep learning enable adaptive, personalized therapies for conditions like stroke, Parkinson's disease, and dementia. Evidence from clinical practice highlights improved outcomes with hybrid systems combining EEG and fNIRS, as well as the use of robotics and virtual environments. Technical, ethical, and regulatory challenges—such as data privacy, bias, and transparency are critically reviewed, and practical recommendations for clinical adoption are provided. The chapter offers a roadmap for deploying intelligent BCIs to enhance patient independence and quality of life in rehabilitation.

Ähnliche Arbeiten