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EEG feature extraction based on wavelet packet decomposition for brain computer interface

机译：基于小波包分解的脑计算机接口脑电特征提取

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摘要

In the study of brain computer interfaces, a novel method was proposed in this paper for the feature extraction of electroencephalogram (EEG). It was based on wavelet packet decomposition (WPD). The energy of special sub-bands and corresponding coefficients of wavelet packet decomposition were selected as features which have maximal separability according to the Fisher distance criterion. The eigenvector was obtained for classification by combining the effective features from different channels; its performance was evaluated by separability and pattern recognition accuracy using the datasets of BCI 2003 Competition. The classification results have proved the effectiveness of the proposed method. This technology provides another useful way to EEG feature extraction in BCIs.
机译：在脑计算机接口的研究中，提出了一种新的脑电图特征提取方法。它基于小波包分解（WPD）。根据Fisher距离准则，选择特殊子带的能量和相应的小波包分解系数作为具有最大可分离性的特征。通过结合不同通道的有效特征获得特征向量进行分类。使用BCI 2003竞赛的数据集通过可分离性和模式识别准确性评估其性能。分类结果证明了该方法的有效性。这项技术为BCI中的EEG特征提取提供了另一种有用的方法。

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