Two-Stage Frequency Recognition Method Based on Correlated Component Analysis for SSVEP-Based BCI
Two-Stage Frequency Recognition Method Based on Correlated Component Analysis for SSVEP-Based BCI
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DOI：
10.1109/tnsre.2018.2848222
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发表时间：
2018-05
影响因子：
4.9
通讯作者：
Yangsong Zhang;E. Yin;Fali Li;Yu Zhang;Toshihisa Tanaka;Qibin Zhao;Yan Cui;Peng Xu;D. Yao-D.-Y
中科院分区：
文献类型：
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作者：
Yangsong Zhang;E. Yin;Fali Li;Yu Zhang;Toshihisa Tanaka;Qibin Zhao;Yan Cui;Peng Xu;D. Yao-D.-Y
典型相关分析(CCA)是基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)系统中频率识别的一种最新方法。各种扩展方法已经被开发出来，其中CCA和基于个体模板的CCA相结合的方法取得了最好的效果。然而，CCA要求正则向量是正交的，这对于脑电信号分析来说可能不是一个合理的假设。本文提出用相关分量分析(CORRCA)代替CCA来实现频率识别。CORRCA可以放松CCA中正则向量的约束，为两个多道脑电信号生成相同的投影向量。在此基础上，提出了一种基于基本CORRCA方法的两阶段法(简称TSCORRCA)。在35个被试的基准数据集上的实验结果表明，CORRCA的性能明显优于CCA，而TSCORRCA在比较的方法中获得了最好的性能。本文论证了基于CORRCA的方法在实现基于SSVEP的高性能脑机接口系统方面具有很大的潜力。
A canonical correlation analysis (CCA) is a state-of-the-art method for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems. Various extended methods have been developed, and among such methods, a combination method of CCA and individual-template-based CCA has achieved the best performance. However, the CCA requires the canonical vectors to be orthogonal, which may not be a reasonable assumption for the EEG analysis. In this paper, we propose using the correlated component analysis (CORRCA) rather than CCA to implement frequency recognition. CORRCA can relax the constraint of canonical vectors in CCA and generate the same projection vector for two multichannel EEG signals. Furthermore, we propose a two-stage method based on the basic CORRCA method (termed TSCORRCA). Evaluated on a benchmark data set of 35 subjects, the experimental results demonstrate that CORRCA significantly outperformed CCA, and TSCORRCA obtained the best performance among the compared methods. This paper demonstrates that CORRCA-based methods have a great potential for implementing high-performance SSVEP-based BCI systems.