Around a third of the total population of Europe suffers from mental disorders. The use of
electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose …

Abstract Background and Objective: Rehabilitation after stroke is essential to minimize
permanent disability. Gamification, the integration of game elements into non-game …

This paper develops a rigorous argument for why the use of Shapley values in explainable
AI (XAI) will necessarily yield provably misleading information about the relative importance …

Artificial intelligence (AI) has become an integral part of our lives; from the recommendations
we receive on social media to the diagnoses made by medical professionals. However, as …

Abstract Explainable Artificial Intelligence (XAI) is widely considered to be critical for building
trust into the deployment of systems that integrate the use of machine learning (ML) models …

Background and Objectives: Neurological disorders like stroke, spinal cord injury (SCI), and
Parkinson's disease (PD) significantly affect global health, requiring accurate diagnosis and …

Brain microstructural changes already occur in the earliest phases of Alzheimer's disease
(AD) as evidenced in diffusion magnetic resonance imaging (dMRI) literature. This study …

Stroke is one of the leading causes of death and disability. To address this challenge,
microwave imaging has been proposed as a portable medical imaging modality. However …

Extensive research has been conducted on analyzing human movements, driven by its
diverse practical applications such as human–robot interaction, human learning, and clinical …

Objective This review aimed to summarize the use of machine learning for predicting the
potential benefits of stroke rehabilitation treatments, to evaluate the risk of bias of predictive …