Subject-based feature extraction using fuzzy wavelet packet in brain-computer interfaces
Subject-based feature extraction using fuzzy wavelet packet in brain-computer interfaces
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DOI：
10.1016/j.sigpro.2006.12.018
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发表时间：
2007-07
期刊：
影响因子：
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通讯作者：
Banghua Yang;G. Yan;Ting Wu;Rongguo Yan
中科院分区：
文献类型：
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作者：
Banghua Yang;G. Yan;Ting Wu;Rongguo Yan
在本文中，我们讨论了脑机接口（BCI）中使用模糊小波包的基于主题的特征提取方法。该方法包括以下三个步骤：(1)对原始脑电信号进行小波包变换(WPT)分解，形成多个小波包基。 (2) 对于每个受试者和每个脑电图通道，使用基于模糊集标准的最佳基础算法来找到最适合该特定受试者和通道的基础； (3)最佳基础中包含的子带能量形成有效特征，用于区分三种类型的运动想象任务。将所提出的方法与之前的小波包方法进行比较，结果表明该方法优于之前的方法。
In this paper, we discuss a subject-based feature extraction method using the fuzzy wavelet packet in brain–computer interfaces (BCIs). The method includes the following three steps: (1) original electroencephalogram (EEG) signals are decomposed with the wavelet packet transform (WPT), which forms many wavelet packet bases; (2) for each subject and each EEG channel, the best basis algorithm based on a fuzzy set criterion is used to find the best-adapted basis for that particular subject and channel; and (3) subband energies included in the best basis form effective features, which are used to discriminate three types of motor imagery tasks. The proposed method is compared with the previous wavelet packet method and the results show that it outperforms the previous one.