Over the last few decades, the Brain-Computer Interfaces have been gradually making their
way to the epicenter of scientific interest. Many scientists from all around the world have …

FNIRS pre-processing and processing methodologies are very important—how a researcher
chooses to process their data can change the outcome of an experiment. The purpose of this …

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

In this study, a channel and feature selection methodology is devised for brain‐computer
interface (BCI) applications using functional near‐infrared spectroscopy (fNIRS). A graph …

In recent years, major depressive disorder (MDD) has been shown to negatively impact
physical recovery in a variety of patients. Functional near-infrared spectroscopy (fNIRS) is a …

Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease. It is imperative to
develop a proper treatment for this neurological disease in the aging society. This …

The proposed study is based on a feature and channel selection strategy that uses
correlation filters for brain–computer interface (BCI) applications using …

In recent decades, the brain–computer interface (BCI) has emerged as a leading area of
research. The feature selection is vital to reduce the dataset's dimensionality, increase the …

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology
for monitoring cerebral hemodynamic responses. Enhancing fNIRS classification can …

Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties
around the world. Detecting it earlier and more effectively can significantly reduce the lethal …