Multi-Frequency Canonical Correlation Analysis (MFCCA): A Generalised Decoding Algorithm for Multi-Frequency SSVEP

TL;DR

This paper introduces Multi-Frequency CCA (MFCCA), a decoding algorithm for multi-frequency SSVEP that improves accuracy by exploiting frequency interactions without requiring user-specific training.

Contribution

The paper extends canonical correlation analysis to explicitly handle multi-frequency SSVEP by incorporating frequency interactions and a novel order concept, enhancing decoding accuracy.

Findings

20% average accuracy improvement at order 2

Maintains training-free and general applicability

Effective for multi-frequency SSVEP decoding

Abstract

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.