Multi-Frequency Canonical Correlation Analysis (MFCCA): An Extended Decoding Algorithm for Multi-Frequency SSVEP

Stimulation methods that utilise more than one stimulation frequency have been developed in steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) for the purpose of increasing the number of targets that can be presented simultaneously. However, there is no unified decoding algorithm that can be 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 "order" was defined as the sum of absolute values of the coefficients in the linear interaction. 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 multi-frequency CCA (MFCCA) has a 20% improvement in decoding accuracy on average at order 2. Although the proposed methods were only tested with two input frequencies, the technique is capable of handling more than two simultaneous input frequencies.