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.

翻译：利用一个以上刺激频率的刺激方法,已经为稳定状态直观生成潜力(SSVEP)大脑-计算机界面(BCIs)开发了刺激方法,目的是增加可以同时显示的目标数量,然而,没有统一解码算法可以不经对每个个人用户或案例的培训而使用,并应用于一大批多频刺激的SSVEP设置。本文扩展了广泛使用的CA(CA)相源分析解码器,以便通过利用多个刺激频率之间的相互作用,明确容纳多频 SSSVEP。采用了一种秩序概念,定义为输入频率线性组合中系数的绝对值之和,以帮助设计多频共和集的系数(MFCCA),随后使用该序列的概率分布来提高解码准确性。结果显示,与标准CCA配方相比,拟议的MFCA在平均2号解码精度方面提高了20%,同时保持其一般性和无训练特性。