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首页> 美国卫生研究院文献>Computational and Mathematical Methods in Medicine >An Efficient Frequency Recognition Method Based on Likelihood Ratio Test for SSVEP-Based BCI
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An Efficient Frequency Recognition Method Based on Likelihood Ratio Test for SSVEP-Based BCI

机译：基于SSVEP的BCI的基于似然比检验的有效频率识别方法

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摘要

An efficient frequency recognition method is very important for SSVEP-based BCI systems to improve the information transfer rate (ITR). To address this aspect, for the first time, likelihood ratio test (LRT) was utilized to propose a novel multichannel frequency recognition method for SSVEP data. The essence of this new method is to calculate the association between multichannel EEG signals and the reference signals which were constructed according to the stimulus frequency with LRT. For the simulation and real SSVEP data, the proposed method yielded higher recognition accuracy with shorter time window length and was more robust against noise in comparison with the popular canonical correlation analysis- (CCA-) based method and the least absolute shrinkage and selection operator- (LASSO-) based method. The recognition accuracy and information transfer rate (ITR) obtained by the proposed method was higher than those of the CCA-based method and LASSO-based method. The superior results indicate that the LRT method is a promising candidate for reliable frequency recognition in future SSVEP-BCI.
机译：对于基于SSVEP的BCI系统而言，有效的频率识别方法对于提高信息传输速率（ITR）非常重要。为了解决这个方面，第一次，利用似然比测试（LRT）提出了一种针对SSVEP数据的新颖的多通道频率识别方法。这种新方法的本质是计算多通道脑电信号与参考信号之间的关联，这些参考信号是根据LRT的刺激频率而构建的。对于仿真和真实的SSVEP数据，与基于流行的规范相关分析（CCA-）的方法以及最小的绝对收缩和选择算子相比，该方法具有较高的识别准确度和较短的时间窗长度，并且对噪声的鲁棒性更高。 （LASSO-）为基础的方法。通过该方法获得的识别精度和信息传输率（ITR）高于基于CCA和LASSO的方法。优异的结果表明，LRT方法是未来SSVEP-BCI中可靠的频率识别的有前途的候选方法。

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