Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from
neurophysiology, computer science, and engineering in an effort to establish real-time …

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

Canonical correlation analysis (CCA) has been widely used in the detection of the steady-
state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard …

Objective. Steady-state visual evoked potential (SSVEP) is an essential paradigm of
electroencephalogram based brain–computer interface (BCI). Previous studies in the BCI …

Goal: Traditional visual brain–computer interfaces (BCIs) preferred to use large-size stimuli
to attract the user's attention and elicit distinct electroencephalography (EEG) features …

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

Steady-state visual evoked potential (SSVEP) is one of the most commonly used control
signals in the brain–computer interface (BCI) systems. However, the conventional spatial …

The aim of this study was to design a dynamically optimized steady-state visually evoked
potential (SSVEP) brain–computer interface (BCI) system with enhanced performance …

Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and
designed reference signals of sine-cosine waves usually works well for steady-state visual …

Multichannel frequency recognition methods are prevalent in SSVEP-BCI systems. These
methods increase the convenience of the BCI system for users and require no calibration …