  Stimulation methods that utilise more than one stimulation frequency have
been developed for steady-state visual evoked potential (SSVEP) brain-computer
interfaces (BCIs) with the purpose of increasing the number of targets that can
be presented simultaneously. However, there is no unified decoding algorithm
that can be used without training for each individual users or cases, and
applied to a large class of multi-frequency stimulated SSVEP settings. This
paper extends the widely used canonical correlation analysis (CCA) decoder to
explicitly accommodate multi-frequency SSVEP by exploiting the interactions
between the multiple stimulation frequencies. A concept of order, defined as
the sum of absolute value of the coefficients in the linear combination of the
input frequencies, was introduced to assist the design of Multi-Frequency CCA
(MFCCA). The probability distribution of the order in the resulting SSVEP
response was then used to improve decoding accuracy. Results show that,
compared to the standard CCA formulation, the proposed MFCCA has a 20%
improvement in decoding accuracy on average at order 2, while keeping its
generality and training-free characteristics.
