Classification of EEG Multiple Imagination Tasks Based on Independent Component Analysis and Relevant Vector Machines
Classification of EEG Multiple Imagination Tasks Based on Independent Component Analysis and Relevant Vector Machines
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DOI：
10.1109/imbioc.2019.8777887
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Shanting Zhang;Rui Xu;Abdelkader Nasreddine Belkacem;Duk Shin;Kun Wang;Zhongpeng Wang;Lu Yu;Zhifeng Qiao;Changming Wang;Chao Chen
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文献类型：
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作者：
Shanting Zhang;Rui Xu;Abdelkader Nasreddine Belkacem;Duk Shin;Kun Wang;Zhongpeng Wang;Lu Yu;Zhifeng Qiao;Changming Wang;Chao Chen
为了解决脑机接口（BCI）中的特征提取问题，采用偶极子定位方法对偶极子的位置、大小和方向进行定位，从而定位高级神经活动的活跃部分，去除眼电图等一系列生理和电伪影。利用公共空间模式和相关向量机提取脑电信号的有效成分，对多个运动图像任务进行分类。结果表明，脑电偶极子定位与共同空间模式相结合可以有效提高脑电信号的信噪比，提取出更明显的特征。相关向量机具有较好的分类效果，是完成运动图像信号分类识别的有效方法。
To solve the problem of feature extraction in braincomputer interface (BCI), the position, size and direction of dipole are located by using dipole localization method, so as to locate the active part of advanced nerve activity and remove a series of physiological and electrical artifacts such as electro-ophthalmogram. The common space pattern and correlation vector machine are used to extract the effective components of EEG signals and classify multiple motor imagery tasks. The results show that the combination of EEG dipole localization and common spatial pattern can effectively improve the signal-to-noise ratio of EEG signals and extract more obvious features. The correlation vector machine provides better classification results and is an effective method to complete the classification and recognition of motor imagery signals.