Objective. Most current electroencephalography (EEG)-based brain–computer interfaces
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …

Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG
signals tend to be represented by a vector or a matrix to facilitate data processing and …

Objective: This study proposes and evaluates a novel data-driven spatial filtering approach
for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high …

The robust analysis of neural signals is a challenging problem. Here, we contribute a
convolutional neural network (CNN) for the robust classification of a steady-state visual …

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 …

Implementing a complex spelling program using a steady-state visual evoked potential
(SSVEP)-based brain–computer interface (BCI) remains a challenge due to difficulties in …

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

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI)
which enables paralyzed people to directly communicate with and control external devices …

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

Objective: In the steady-state visual evoked potential (SSVEP)-based brain computer
interfaces (BCIs), spatial filtering, which combines the multi-channel …