Regularization using similarities of signals observed in nearby sensors for feature extraction of brain signals
Regularization using similarities of signals observed in nearby sensors for feature extraction of brain signals
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DOI：
10.1109/embc.2013.6611273
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
Hiroshi Higashi;Toshihisa Tanaka
中科院分区：
文献类型：
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作者：
Hiroshi Higashi;Toshihisa Tanaka
为了解决脑信号特征提取中使用少量训练样本学习到的空间权重的不确定性，提出了一种利用相邻传感器观测到的信号相似性进行正则化的方法。开始推导正则化，定义传感器之间的距离。在一定距离下，提出的正则化算法使空间权重提取出附近传感器的相似信号。将所提出的正则化方法应用于已知的基于脑电的脑机接口空间权值确定的公共空间模式（CSP）方法。在使用运动想象过程中脑电信号数据集的分类实验中，即使仅使用5个样本，该方法的分类准确率也比标准CSP方法提高了28%。
In order to solve uncertainty of spatial weights learned with small amount of training samples for feature extraction from brain signals, a regularization using similarity of signals observed in sensors that are located near each other is proposed. Deriving the regularization is begun defining a distance between the sensors. Under the distance, the proposed regularization works so that the spatial weights extracts similar signals in the nearby sensors. The proposed regularization is applied to the well known common spatial pattern (CSP) method that finds spatial weights for EEG based brain machine interface. In the classification experiment using a dataset of EEG signals during motor imagery, the proposed method achieved maximum improvement by 28% in the classification accuracy over the standard CSP in a setting of even when only five samples are used.