Motor Imagery (MI) classification with electroencephalography (EEG) is a critical aspect of
Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to …

The second leading cause of death and one of the most common causes of disability in the
world is stroke. Researchers have found that brain–computer interface (BCI) techniques can …

Electroencephalogram (EEG) based motor trajectory decoding for efficient control of brain–
computer interface (BCI) systems has been an active area of research. The systems include …

This paper uses EEG data to introduce an approach for classifying right and left-hand
classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity …

Extracting features from EEG signals through time, frequency and spatial-domain gives rise
to the problem of neglecting the property of nonlinear manifold structure of the data, and …

Common Spatial Pattern (CSP) has been recognized as a standard and powerful method for
the identification of Electroencephalography (EEG)-based Motor Imagery (MI) tasks when …

Currently, P300-BCIs are mostly used for spelling tasks, where the number of commands is
equal to the number of stimuli that evoke event-related potentials (ERPs). Increasing this …

Objective. Research on the decoding of brain signals to control external devices is rapidly
emerging due to its versatile potential applications, including neuroprosthetic control and …

Motor imagery (MI)-based brain-computer interface (BCI) provides a promising solution for
the limb rehabilitation of stroke patients. However, due to the inadequate cognition of brain …

Background: Recently, brain–computer interfaces (BCIs) have attracted worldwide attention
for their great potential in clinical and real-life applications. To implement a complete BCI …