Logistic Regression for Single Trial EEG Classification
Logistic Regression for Single Trial EEG Classification
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DOI：
10.7551/mitpress/7503.003.0177
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发表时间：
2006-12
期刊：
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通讯作者：
Ryota Tomioka;K. Aihara;K. Müller
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作者：
Ryota Tomioka;K. Aihara;K. Müller
提出了一种新的基于正则化Logistic回归的单次脑电分类框架。在这个稳健的统计框架中，不需要任何先验特征提取或异常值去除。我们给出了回归函数的两种参数形式：(A)用满秩对称阵系数和(B)用两个秩=1的矩阵的差。在第一种情况下，问题是凸的，Logistic回归在产生式模型下是最优的。后一种情况被证明与通用空间模式(CSP)算法有关，CSP算法是脑机接口中的一种流行技术。回归系数也可以像CSP投影一样以地形图的方式映射到头皮上，这允许神经生理学解释。在162个BCI数据集上的仿真实验表明，与传统的基于CSP的分类器相比，该方法具有更好的分类精度和稳健性。
We propose a novel framework for the classification of single trial ElectroEncephaloGraphy (EEG), based on regularized logistic regression. Framed in this robust statistical framework no prior feature extraction or outlier removal is required. We present two variations of parameterizing the regression function: (a) with a full rank symmetric matrix coefficient and (b) as a difference of two rank=1 matrices. In the first case, the problem is convex and the logistic regression is optimal under a generative model. The latter case is shown to be related to the Common Spatial Pattern (CSP) algorithm, which is a popular technique in Brain Computer Interfacing. The regression coefficients can also be topographically mapped onto the scalp similarly to CSP projections, which allows neuro-physiological interpretation. Simulations on 162 BCI datasets demonstrate that classification accuracy and robustness compares favorably against conventional CSP based classifiers.