Two-level Feature Extraction Method for Multi-class Motor Imagery
Two-level Feature Extraction Method for Multi-class Motor Imagery
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发表时间：
2016
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通讯作者：
朱俊青;佘青山;马玉良;Meng Ming;Jun-Qing Zhu;Qing-Shan She;Yu-Liang Ma;Zhi-Zeng Luo;程龙 本文责任编委
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作者：
朱俊青;佘青山;马玉良;Meng Ming;Jun-Qing Zhu;Qing-Shan She;Yu-Liang Ma;Zhi-Zeng Luo;程龙 本文责任编委
共同空间模式（CSP）是基于运动想象的脑机接口（BCI）中常用的特征提取方法。然而，多类任务的分类准确率明显低于两类任务的分类准确率。通过引入堆叠降噪自动编码器（Stacked denoising autoencoders，SDA），提出了一种多类运动想象脑电信号（Electroencephalogram，EEG）的两级特征提取方法.首先，采用一对多CSP（OVR-CSP）算法将脑电信号转换到低维空间，使信号方差的分辨力最大化。然后，利用SDA网络提取更高层次的抽象特征，以更有效地表征类别属性。最后，使用Softmax分类器对运动想象任务进行分类。在BCI竞赛IV的数据集2a的四类运动想象任务的分类实验中，该方法获得了0.69的平均Kappa值。结果表明，该方法是有效和稳健的。
Common spatial pattern (CSP) is a popular method of feature extraction for motor imagery based brain-computer interface (BCI). However, the classification accuracy of multi-class tasks is obviously lower than that of two-class tasks with CSP. By employing the stacked denoising autoencoders (SDA), a two-level feature extraction method for multi-class motor imagery electroencephalogram (EEG) is proposed. Firstly, one versus rest CSP (OVR-CSP) is adopted to convert EEG into low dimensional space in which the discrimination of signal variances is maximized. Then, SDA network is used to extract the higher level abstract features which can characterize the category attributes more effectively. Finally, the motor imagery tasks are classified with Softmax classifier. In the classification experiment with four-class motor imagery tasks from Data-sets 2a of the BCI competition IV, this method achieves the average Kappa value of 0.69. The results show that the proposed method is effective and robust.