Regularization using geometric information between sensors capturing features from brain signals
Regularization using geometric information between sensors capturing features from brain signals
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DOI：
10.1109/icassp.2012.6287985
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
Hiroshi Higashi;A. Cichocki;Toshihisa Tanaka
中科院分区：
文献类型：
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作者：
Hiroshi Higashi;A. Cichocki;Toshihisa Tanaka
我们提出了一种基于几何结构的正则化方法，用于脑数据记录传感器阵列的特征提取。研究的目的是使用传感器之间的距离作为寻找空间权重的几何信息来添加惩罚项。在传感器邻域的定义下，导出了正则化项。本文对基于脑机接口（BCI）的常见空间模式（CSP）特征提取方法进行了评价。对人工信号进行了正则化仿真，证明了CSP过程。结果表明，该方法在提取特定脑点产生的成分方面优于标准CSP方法。此外，使用基于运动图像的BCI数据集的分类实验结果表明，即使我们只使用5个样本，该方法的分类精度也比标准CSP方法提高了27%。
We propose a regularization based on geometric structure for feature extraction in a sensor array for brain data recordings. The purpose of the study is to add a penalty term using distances between sensors as the geometric information for finding spatial weights. The regularization term is derived under the definition of neighbors of sensors. We evaluate the proposed regularization in common spatial pattern (CSP) which is a well-known feature extraction method for EEG based brain computer interface (BCI). We have demonstrated the CSP procedure with the regularization by simulation for artificial signals. The results show that the proposed method works better than standard CSP in extracting of a component generated in a certain brain spot. Moreover, the classification experimental results using dataset of motor imagery based BCI suggest that the proposed method achieved maximum improvement by 27% in the classification accuracy over the standard CSP in a setting of even when we use only five samples.