  Canonical correlation analysis (CCA) is a state-of-the-art method for
frequency recognition in steady-state visual evoked potential (SSVEP)-based
brain-computer interface (BCI) systems. Various extended methods have been
developed, and among such methods, a combination method of CCA and
individual-template-based CCA (IT-CCA) has achieved excellent performance.
However, CCA requires the canonical vectors to be orthogonal, which may not be
a reasonable assumption for EEG analysis. In the current study, we propose
using the correlated component analysis (CORRCA) rather than CCA to implement
frequency recognition. CORRCA can relax the constraint of canonical vectors in
CCA, and generate the same projection vector for two multichannel EEG signals.
Furthermore, we propose a two-stage method based on the basic CORRCA method
(termed TSCORRCA). Evaluated on a benchmark dataset of thirty-five subjects,
the experimental results demonstrate that CORRCA significantly outperformed
CCA, and TSCORRCA obtained the best performance among the compared methods.
This study demonstrates that CORRCA-based methods have great potential for
implementing high-performance SSVEP-based BCI systems.
