In this paper, hybrid brain-computer interface (hBCI) technologies for improving
classification accuracy and increasing the number of commands are reviewed. Hybridization …

Over the past several decades, electroencephalogram (EEG)-based brain-computer
interfaces (BCIs) have attracted attention from researchers in the field of neuroscience …

Multi-channel EEG data are usually necessary for spatial pattern identification in motor
imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some …

Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …

Canonical correlation analysis (CCA) has been one of the most popular methods for
frequency recognition in steady-state visual evoked potential (SSVEP)-based brain …

Regularization has been one of the most popular approaches to prevent overfitting in
electroencephalogram (EEG) classification of brain-computer interfaces (BCIs). The …

Background Common spatial pattern (CSP) has been most popularly applied to motor-
imagery (MI) feature extraction for classification in brain–computer interface (BCI) …

A potential limitation of a motor imagery (MI) based brain-computer interface (BCI) is that it
usually requires a relatively long time to record sufficient electroencephalogram (EEG) data …

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 …

Mu rhythm is a spontaneous neural response occurring during a motor imagery (MI) task
and has been increasingly applied to the design of brain–computer interface (BCI). Accurate …