Combining canonical correlation analysis and infinite reference for frequency recognition of steady-state visual evoked potential recordings: a comparison with periodogram method.
Combining canonical correlation analysis and infinite reference for frequency recognition of steady-state visual evoked potential recordings: a comparison with periodogram method.
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DOI：
10.3233/bme-141109
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发表时间：
2014
影响因子：
1
通讯作者：
Yin Tian;Fali Li;Peng Xu;Zhen Yuan;Dechun Zhao;Haiyong Zhang
中科院分区：
文献类型：
--
作者：
Yin Tian;Fali Li;Peng Xu;Zhen Yuan;Dechun Zhao;Haiyong Zhang
稳态视觉诱发电位(SSVEP)是视觉系统对重复的视觉刺激以恒定频率闪烁做出的反应，在利用头皮脑电记录研究脑活动中具有重要意义。然而，以往的工作一般没有考虑对SSVEP研究的参考影响。为了提高SSVEP记录的频率识别精度，提出了一种典型相关分析与无限参考相结合的新方法。与广泛使用的周期图法(PM)相比，ICCA在小范围内提取频率时能够达到更高的识别精度。此外，识别结果表明，ICCA是研究基于SSVEP的脑机接口(BCI)的一种非常稳健的工具。
Steady-state visual evoked potentials (SSVEP) are the visual system responses to a repetitive visual stimulus flickering with the constant frequency and of great importance in the study of brain activity using scalp electroencephalography (EEG) recordings. However, the reference influence for the investigation of SSVEP is generally not considered in previous work. In this study a new approach that combined the canonical correlation analysis with infinite reference (ICCA) was proposed to enhance the accuracy of frequency recognition of SSVEP recordings. Compared with the widely used periodogram method (PM), ICCA is able to achieve higher recognition accuracy when extracts frequency within a short span. Further, the recognition results suggested that ICCA is a very robust tool to study the brain computer interface (BCI) based on SSVEP.