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bayesian判决
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  bayesian decision rule
    Result Satisfying result can be achieved with small-laplacian spatial filter to improve SN rate, AR spectrum estimation to extract specified frequency band of channel C3 and C4 as features, and Bayesian decision rule to classify.
    结果 使用空间滤波器small laplacian提高信噪比,AR谱估计提取C3和C4导联特定波段能量作为特征,Bayesian判决进行分类,可以取得较好的识别率。
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  bayesian decision rule
Classification is done by the Bayesian decision rule.
      
A recently developed Bayesian decision rule is applied to determine the length of filters in MESA.
      
A well known approach is the weighted sum, where the weights are determined through a Bayesian decision rule.
      
An optimal Bayesian decision rule was used to classify the texture samples, based on their measured feature values.
      


Objective To select appropriate data pre-processing, feature-extraction and pattern recognition algorithms to improve the classification performance of independent Brain-Computer Interface (BCI).Method Using spatial filter, feature extration, and patten recognition algorithms, we processed the same BCI data set, compared and analyzed the performance of the results.Result Satisfying result can be achieved with small-laplacian spatial filter to improve SN rate, AR spectrum estimation to extract specified frequency...

Objective To select appropriate data pre-processing, feature-extraction and pattern recognition algorithms to improve the classification performance of independent Brain-Computer Interface (BCI).Method Using spatial filter, feature extration, and patten recognition algorithms, we processed the same BCI data set, compared and analyzed the performance of the results.Result Satisfying result can be achieved with small-laplacian spatial filter to improve SN rate, AR spectrum estimation to extract specified frequency band of channel C3 and C4 as features, and Bayesian decision rule to classify.Conclusion High SN rate and suitable features are the keys to improve classification accuracy.

目的 为独立式脑-计算机接口(brain computerinterface ,BCI)选择合适的数据预处理、特征提取和模式识别算法,提高系统的识别率。方法 分别使用不同的空间滤波算法、特征提取方法和模式识别算法,对同一组BCI数据进行处理,并对结果加以比较分析。结果 使用空间滤波器small laplacian提高信噪比,AR谱估计提取C3和C4导联特定波段能量作为特征,Bayesian判决进行分类,可以取得较好的识别率。结论 高信噪比以及合适的特征是提高识别率的关键因素。

 
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