Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as
state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis …

We performed an electronic database search of published works from 2012 to mid-2021 that
focus on human gait studies and apply machine learning techniques. We identified six key …

This report is the second part of a comprehensive two-part series aimed at reviewing an
extensive and diverse toolkit of novel methods to explore brain health and function. While …

Technology-supported rehabilitation therapy for neurological patients has gained increasing
interest since the last decades. The literature agrees that the goal of robots should be to …

This research presents a brain-computer interface (BCI) framework for brain signal
classification using deep learning (DL) and machine learning (ML) approaches on functional …

Brain–computer interfaces are used for direct two-way communication between the human
brain and the computer. Brain signals contain valuable information about the mental state …

Brain–computer interfaces (BCIs) have garnered significant attention in recent years due to
their potential applications in medical, assistive, and communication technologies. Building …

Machine Learning (ML) offers unique and powerful tools for mental health practitioners to
improve evidence-based psychological interventions and diagnoses. Indeed, by detecting …

Motor impairment has a profound impact on a significant number of individuals, leading to a
substantial demand for rehabilitation services. Through brain–computer interfaces (BCIs) …

With the emergence of an increasing number of functional near-infrared spectroscopy
(fNIRS) devices, the significant deterioration in measurement caused by motion artifacts has …