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首页> 外文期刊>IEEE transactions on neural systems and rehabilitation engineering >Certainty-Based Reduced Sparse Solution for Dense Array EEG Source Localization
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Certainty-Based Reduced Sparse Solution for Dense Array EEG Source Localization

机译：基于确定性的精简阵列密集脑电信号源稀疏解决方案

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摘要

The EEG source localization is an ill-posed problem. It involves estimation of the sources which outnumbers the number of measurements. For a given measurement at a given time all sources are not active this makes the problem as sparse inversion problem. This paper presents a new approach for dense array EEG source localization. This paper aims at reducing the solution space to only most certain sources and thereby reducing the problem of ill-posedness. This employs a two-stage method, where the first stage finds the most certain sources that are likely to produce the observed EEG by using a statistical measure of sources, the second stage solves the inverse problem by restricting the solution space to only most certain sources and their neighbors. This reduces the solution space for other source localization methods hence improvise their accuracy in localizing the active neurological sources in the brain. This method has been validated and applied to real 256 channel data and the results were analyzed.
机译：脑电图源定位是一个不适定的问题。它涉及对源的估计，其数量超过测量的数量。对于在给定时间的给定测量，所有源均未激活，这使该问题成为稀疏反演问题。本文提出了一种密集阵列脑电信号源定位的新方法。本文旨在将解决方案空间仅减少到大多数特定来源，从而减少不适定问题。这采用了两阶段方法，其中第一阶段通过使用统计量的来源来找到最有可能产生观察到的EEG的特定来源，第二阶段通过将解空间限制为仅大多数特定来源来解决反问题。和他们的邻居。这减少了其他来源定位方法的解决方案空间，因此提高了它们在大脑中定位活动神经源的准确性。该方法已经过验证并应用于实际的256通道数据，并对结果进行了分析。

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