多频典型相关分析（MFCCA）：一种通用的多频SSVEP译码算法
Multi-Frequency Canonical Correlation Analysis (MFCCA): A Generalised Decoding Algorithm for Multi-Frequency SSVEP
摘要：
已经开发了利用多于一个刺激频率的刺激方法用于稳态视觉诱发电位（SSVEP）脑机接口（BCI），目的是增加可以同时呈现的目标的数量。然而，目前还没有一种统一的解码算法，可以在不对每个单独用户或情况进行训练的情况下使用，并应用于大类多频刺激的SSVEP设置。本文扩展了广泛使用的典型相关分析（CCA）解码器，以明确地适应多频SSVEP利用多个刺激频率之间的相互作用。为了辅助多频CCA（MFCCA）的设计，引入了阶数的概念，定义为输入频率线性组合中系数的绝对值之和。然后使用所得SSVEP响应中的阶的概率分布来提高解码精度。结果表明，与标准CCA相比，所提出的MFCCA在保持其通用性和免训练特性的同时，平均2阶译码精度提高了20%.
英文摘要：
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.
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