A growing number of affective computing researches recently developed a computer system
that can recognize an emotional state of the human user to establish affective human …

Optimal feature extraction for multi-category motor imagery brain-computer interfaces (MI-
BCIs) is a research hotspot. The common spatial pattern (CSP) algorithm is one of the most …

Background In a visual oddball paradigm, attention to an event usually modulates the event-
related potential (ERP). An ERP-based brain-computer interface (BCI) exploits this neural …

Pattern classification algorithm is the crucial step in develo** brain–computer interface
(BCI) applications. In this paper, a hierarchical support vector machine (HSVM) algorithm is …

EEG‐based discrimination among motor imagery states has been widely studied for brain‐
computer interfaces (BCIs) due to the great potential for real‐life applications. However, in …

Extracting knowledge from electroencephalographic (EEG) signals has become an
increasingly important research area in biomedical engineering. In addition to its clinical …

Information theory is a probabilistic framework that allows the quantification of statistical non-
independence between signals of interest. In contrast to other methods used for this …

An electroencephalographic-based brain–computer interface (BCI) can provide a non-
muscular method of communication. A general model for P300-based BCI stimulus …

Common spatial pattern (CSP) as a spatial filtering method has been most widely applied to
electroencephalogram (EEG) feature extraction to classify motor imagery (MI) in brain …

Brain-Computer Interfaces (BCIs) seek to infer some task symbol, a task relevant instruction,
from brain symbols, classifiable physiological states. For example, in a motor imagery robot …