Spatio-Spectral CCA (SS-CCA): A novel approach for frequency recognition in SSVEP-based BCI.

Background: Steady-state visually evoked potentials (SSVEP) are one of the most important paradigms in the BCI Domain. Among the best methods for detecting frequency in the SSVEP-based BCI is the Canonical Correlation Analysis (CCA), which calculates canonical correlation between two sets of multidimensional variables, the electroencephalogram (EEG) and reference signals. Despite its efficiency and widespread application, CCA algorithm has some limitations.

Ensemble Regularized Common Spatio-Spectral Pattern (ensemble RCSSP) model for motor imagery-based EEG signal classification.

The Brain-Computer interface system provides a communication path among the brain and computer, and recently, it is the subject of increasing attention. One of the most common paradigms of BCI systems is motor imagery. Currently, to classify motor imagery EEG signals, Common Spatial Patterns (CSP) are extensively used.