A linear feature space for simultaneous learning of spatio-spectral filters in BCI
A linear feature space for simultaneous learning of spatio-spectral filters in BCI
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DOI：
10.1016/j.neunet.2009.06.035
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发表时间：
2009-11-01
期刊：
影响因子：
7.8
通讯作者：
Farquhar, J.
中科院分区：
文献类型：
--
作者：
Farquhar, J.
它示出了如何两个最常见的类型的特征映射用于单次试验脑电图（EEG）的分类。即空间和频率滤波。因此，通过首先将数据映射到该空间，简单的线性分类器可以直接学习最佳空间+频率滤波器。显著如果分类器的损失函数是凸的，则学习这些滤波器是凸最小化问题。它还示出了如何饼处理的数据，使得所得到的决策函数是强大的EEG数据中固有的偏见。此外，根据最大利润矩阵分解的想法，它示出了如何跟踪规范可以用来选择具有低秩的解决方案。低秩解是优选的，因为它们反映了关于我们期望看到的EEG信号类型的先验信息，也就是说，可分类的信息仅包含在几个空间/光谱对中，它们也更容易解释。将这种特征空间变换与模拟和真实的想象运动脑机接口（BCI）数据上的公共空间模式进行比较，并显示出最先进的性能（C）。2009爱思唯尔有限公司版权所有
It is shown how two of the most common types of feature mapping used for classification of single trial Electroencephalography (EEG). i.e. spatial and frequency filtering. can be equivalently performed as linear operations in the space of frequency-specific detector covariance tensors Thus by first mapping the data to this space, a simple linear classifier can directly learn optimal spatial + frequency filters. Significantly. if the classifier's loss function is convex, learning these filters is a convex minimisation problem. It is also shown how to pie-process the data such that the resulting decision function is robust to the biases inherent in EEG data. Further, based upon ideas from Max Margin Matrix Factorisation, it is shown how the trace norm can be used to select Solutions which have low rank. Low rank Solutions are preferred as they reflect prior information about the types of EEG signals we expect to see, i.e. that the classifiable information is contained in only a few spatio/spectral pairs They are also easier to interpret This feature-space transformation is compared with the Common-Spatial-Patterns on simulated and real Imagined Movement Brain Computer Interface (BCI) data and shown to give state-of-the-art performance (C) 2009 Elsevier Ltd. All rights reserved