Jair Montoya-Martínez, Antonio Artés-Rodríguez, M. Pontil
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引用次数: 0
Abstract
We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy EEG measurements, commonly named as the EEG inverse problem. We propose a new method based on the factorization of the BES as a product of a sparse coding matrix and a dense latent source matrix. This structure is enforced by minimizing a regularized functional that includes the ℓ21-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We have evaluated our approach under a simulated scenario consisting on estimating a synthetic BES matrix with 5124 sources. We compare the performance of our method respect to the Lasso, Group Lasso, Sparse Group Lasso and Trace norm regularizers.
我们考虑从噪声脑电测量中估计脑电源(BES)矩阵,通常称为脑电逆问题。我们提出了一种基于将BES分解为稀疏编码矩阵和密集潜在源矩阵乘积的新方法。这种结构是通过最小化一个正则泛函来实现的,该泛函包含编码矩阵的l21范数和潜在源矩阵的Frobenius范数的平方。我们开发了一种交替优化算法来解决由此产生的非光滑-非凸最小化问题。我们在一个模拟场景下评估了我们的方法,该场景包括估算具有5124个源的合成BES矩阵。我们比较了我们的方法在Lasso、Group Lasso、Sparse Group Lasso和Trace范数正则化方面的性能。