Electroencephalography (EEG) has been a staple method for identifying certain health
conditions in patients since its discovery. Due to the many different types of classifiers …

Electroencephalogram (EEG) measures the neuronal activities in the form of electric
currents that are generated due to the synchronized activity by a group of specialized …

Alcoholism is a severe disorder that leads to brain problems and associated cognitive,
emotional and behavioral impairments. This disorder is typically diagnosed by a …

This review investigates cutting-edge electroencephalography (EEG) signal processing
techniques, focusing on noise reduction, artifact removal, and feature extraction. The study …

The diagnosis of depression almost exclusively depends on doctor-patient communication
and scale analysis, which have the obvious disadvantages such as patient denial, poor …

Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common forms
of neurodegenerative diseases. The literature suggests that effective brain connectivity …

Abstract Parkinson's disease (PD), a neurodegenerative disorder characterized by rest
tremors, muscular rigidity, and bradykinesia, has become a global health concern. Currently …

Electroencephalogram (EEG) signals are well used to characterize the brain states and
actions. In this paper, a novel empirical wavelet transform (EWT) based machine learning …

Hybrid algorithms have attracted more and more attention in the field of optimization
algorithms. In this paper, three hybrid algorithms are proposed to solve feature selection …

Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …