Explainable Computational Intelligence for Neurological Disorders

Artificial intelligence and Explainable computational intelligence are continuously reshaping the detection, monitoring and data-rich diagnostics in neurological disorders. This book brings together recent advances in neuro-focused AI systems, biomedical signal interpretation, multi-modal analytics and explainable AI. The chapters progress from foundational perspectives on AI-enabled neurological and biomedical data to recent machine learning and deep learning frameworks for ECG and EEG-based disease screening, and finally toward assistive and translational technologies such as sign language recognition, virtual reality interventions, regenerative neurology, and cancer AI analytics. This book:Offers a detailed perspective on explainable AI-based systems for neurological diagnosis, and clinical decision support. Discusses key physiological and neural biomarkers, including heart rate variability, skin conductance, entropy analytics, ECG, and EEG signals. Includes recent AI model designs from wearable sensor analytics and brain connectivity for assistive systems. Includes machine learning approaches for conditions such as Alzheimer’s, epilepsy (seizure prediction and surgery outcomes), and other neurological disorders. Explores translational technologies and therapies, for different neuro disorders, along with multi-modal AI insights for early tumor detection. The book is suitable for research scholars, clinicians, and postgraduate students working in biomedical signal analysis, neuro-AI modelling, wearable diagnostics, and intelligent assistive systems.