Correlated Component Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface
Correlated Component Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface
复制标题
用于增强基于 SSVEP 的脑机接口性能的相关成分分析
DOI：
10.1109/tnsre.2018.2826541
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发表时间：
2018-05-01
影响因子：
4.9
通讯作者：
Xu, Peng
中科院分区：
文献类型：
--
作者：
Zhang, Yangsong;Guo, Daqing;Xu, Peng
为了提高基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)的性能，提出了一种新的SSVEP频率识别方法。介绍了相关成分分析(Corca)，它最初是为了找到在不同受试者之间一致并在他们之间最大程度相关的电极的线性组合。针对基于SSVEP的脑-机接口场景，我们提出了一种基于多个训练数据块的空间滤波器学习的CORCA算法。利用空间滤波器对多道脑电信号进行组合，去除背景噪声。我们使用来自35个被试的40类SSVEP基准数据集对所提出的基于COCA和基于任务相关成分分析(TRCA)的方法进行了比较。我们的实验验证了基于CORCA的方法的有效性，大量的比较结果表明，基于CORCA的方法显著优于基于TRCA的方法。优越的性能表明，该方法在目标数目较多的基于SSVEP的脑-机接口中具有良好的性能。
A new method for steady-state visual evoked potentials (SSVEPs) frequency recognition is proposed to enhance the performance of SSVEP-based brain-computer interface (BCI). Correlated component analysis (CORCA) is introduced, which originally was designed to find linear combinations of electrodes that are consistent across subjects and maximally correlated between them. We propose a CORCA algorithm to learn spatial filters with multiple blocks of individual training data for SSVEP-based BCI scenario. The spatial filters are used to remove background noises by combining the multichannel electroencephalogram signals. We conduct a comparison between the proposed CORCA-based and the task-related component analysis (TRCA) based methods using a 40-class SSVEP benchmark data set recorded from 35 subjects. Our experimental study validates the efficiency of the CORCA-based method, and the extensive comparison results indicate that the CORCA-based method significantly outperforms the TRCA-based method. Superior performance demonstrates that the proposed method holds the promising potential to achieve satisfactory performance for SSVEP-based BCI with a large number of targets.