AI and Brain–Computer Interfaces in Neurocognitive Rehabilitation

This chapter discusses the evolving context of artificial intelligence (AI) and brain–computer interfaces (BCIs) and their implementation in neurocognitive rehabilitation. As the neuroscience, clinical psychology, and neurotechnology fields have become increasingly interconnected, AI–BCI systems are introducing new approaches to testing, enabling, and enhancing cognitive functional abilities in neurodevelopmental and neurocognitive disorder patients. These machine learning, neural signal processing, and adaptive interface-driven technologies are no longer speculative abstractions but are being put to meaningful use in both clinical and assistive care settings. Aimed at clinicians and researchers alike, this chapter provides a detailed overview of current applications, backed by clinical evidence and technological advancement. It starts with an introduction to the convergence of AI and BCI, followed by in-depth sections on therapeutic applications, clinical integrations into real-world practice, and future innovations. A typical case study is focused on brain–machine interface (BMI)-based assistive communication systems, particularly for individuals with special needs, including those with autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), global developmental delay/intellectual disability (GDD/ID), specific learning disabilities (SLDs), and cerebral palsy (CP). These communication systems are a lifeline to non-verbal and minimally verbal individuals, enabling them to communicate through neural intent and machine learning-based prediction models. The chapter also addresses the operational and ethical issues of implementing smart neurotechnology, such as data privacy, accessibility of systems, and the risk of abuse through excessive reliance on automated decision-making. The chapter concludes with forecasts of worldwide trends and future directions, highlighting the manner in which AI–BCI systems will transform rehabilitation science and special education over the next decade. By bridging clinical practice and research progress, the chapter is meant to be at once a guide to practical use and a visionary path through the promise of AI and BCI in neurocognitive medicine.