**1. Introduction**

The development of in vivo neuroimaging technology has led to an incredible amount of digital information concerning the brain. Neuroimaging techniques are being increasingly used to study human cognitive processes [1] and create brain–machine interfaces [2], as well as to identify and diagnose certain brain disorders [3]. Currently, neuroscientists and physicians actively use various methods of brain scanning, including electroencephalography (EEG), magnetoencephalography (MEG), functional near-infrared spectroscopy (fNIRS), electrocorticography (ECoG), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI). Recent advances in signal processing and machine learning applied to neuroimaging data using various signal-processing methods have led to impressive progress towards solving several practical problems in medicine, healthcare, neuroscience, biomedical engineering, brain–machine interfaces, cognitive science, etc.
