Hierarchical model with piecewise latent process for globally sparse / locally smooth brain generators imaging
Hierarchical model with piecewise latent process for globally sparse / locally smooth brain generators imaging
复制标题
具有分段潜在过程的分层模型，用于全局稀疏/局部平滑脑生成器成像
DOI：
10.1109/mlsp.2009.5306218
复制
发表时间：
2009
期刊：
影响因子：
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通讯作者：
A. Cichocki
中科院分区：
文献类型：
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作者：
A. Hazart;Olivier Ferony;A. Cichocki
EEG（脑电图）或 MEG（脑磁图）等无创测量技术提供了良好的时间分辨率，但缺乏空间分辨率。源重建是提高空间分辨率的解决方案。它需要解决一个不适定的反问题，其中的挑战是限制源空间，在平滑和稀疏约束之间做出折衷。我们提出了一种引入分段潜在过程的模型，以确保源的局部同质性和全局稀疏性。该方法是在贝叶斯框架中开发的，源重建表示为最小均方误差，使用马尔可夫链蒙特卡罗算法计算。除了源重建之外，该方法还提供了与分类问题相关的分段解决方案。主要贡献是这种概率模型的新颖应用及其与现有方法的比较。我们将该方法应用于模拟脑电图记录，并显示了潜在过程的积极影响。
Noninvasive measurement techniques like EEG (electroencephalography) or MEG (magnetoencephalography) provide a good time resolution but suffer of a lack of spatial resolution. Source reconstruction is a solution for increasing the spatial resolution. It requires to solve an ill-posed inverse problem where the challenge is to restrict the source space, making a compromise between smooth and sparse constraints. We propose a model that introduces a piecewise latent process to ensure local homogeneity and global sparsity of the source. The method is developed in a Bayesian framework and the source reconstruction is expressed as the minimum mean square error, computed with a Markov Chain Monte Carlo algorithm. In addition to the source reconstruction, the method also provides a segmented solution that can be relevant for classification issues. The main contribution is the novel application of such a probabilistic model and its comparison with existing approaches. We apply the method on simulated EEG recordings and show the positive influence of the latent process.
DOI：
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发表时间：
2002
期刊：
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影响因子：
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作者：
A. Dale;M. Sereno
通讯作者：
A. Dale;M. Sereno
DOI：
10.1016/0013-4694(95)00107-a
发表时间：
1995-10-01
期刊：
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY
影响因子：
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作者：
GORODNITSKY, IF;GEORGE, JS;RAO, BD
通讯作者：
RAO, BD