Functional connectivity and effective connectivity of the human brain, representing statistical
dependence and directed information flow between cortical regions, significantly contribute …

A review of physical and mathematical methods for reconstructing the functional networks of
the brain based on recorded brain activity is presented. Various methods are considered, as …

Background Electroencephalography (EEG)-based brain-computer interface (BCI) systems
are mainly divided into three major paradigms: motor imagery (MI), event-related potential …

Motor imagery classification from EEG signals is essential for motor rehabilitation with a
Brain-Computer Interface (BCI). Most current works on this issue require a subject-specific …

With the rapid development of deep learning, more and more deep learning-based motor
imagery electroencephalograph (EEG) decoding methods have emerged in recent years …

In motor imagery (MI) based brain–computer interface (BCI) signal analysis, mu and beta
rhythms of electroencephalograms (EEGs) are widely investigated due to their high temporal …

Age-related changes in the human brain functioning crucially affect the motor system,
causing increased reaction time, low ability to control and execute movements, difficulties in …

In building a practical and robust brain-computer interface (BCI), the classification of motor
imagery (MI) from electroencephalography (EEG) across multiple days is a long-standing …

Identification of brain activity associated with motor execution and, more importantly, with
motor imagery is necessary for the development of brain–computer interfaces. Most of recent …

Objective. Brain–computer interfaces (BCIs) enable a direct communication pathway
between the human brain and external devices, without relying on the traditional peripheral …