论文标题
多频规范相关分析（MFCCA）：多频SSVEP的广义解码算法
Multi-Frequency Canonical Correlation Analysis (MFCCA): A Generalised Decoding Algorithm for Multi-Frequency SSVEP
论文作者
论文摘要
利用多种刺激频率的刺激方法已开发出用于稳态视觉诱发电位（SSVEP）脑部计算机界面（BCIS），目的是增加可以同时显示的目标数量。但是，没有统一的解码算法可以在没有为每个用户或案例培训的情况下使用，并应用于大量的多频刺激SSVEP设置。本文扩展了广泛使用的规范相关分析（CCA）解码器，以通过利用多个刺激频率之间的相互作用来明确适应多频SSVEP。引入了一个定义为在输入频率线性组合中系数的绝对值之和的概念，以协助多频CCA（MFCCA）的设计。然后使用该顺序在所得的SSVEP响应中的概率分布来提高解码精度。结果表明，与标准CCA公式相比，所提出的MFCCA平均在第2阶时平均解码准确性提高了20％，同时保持其一般性和无训练的特征。
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.
