Graph-Based Variability Estimation in Single-Trial Event-Related Neural Responses
Graph-Based Variability Estimation in Single-Trial Event-Related Neural Responses
复制标题
DOI：
10.1109/tbme.2009.2037139
复制
发表时间：
2010-05-01
影响因子：
4.6
通讯作者：
Clerc, Maureen
中科院分区：
文献类型：
--
作者：
Gramfort, Alexandre;Keriven, Renaud;Clerc, Maureen
由于低信噪比以及大脑反应的内在变异性，从多次试验脑磁图或脑电记录中提取信息是一项具有挑战性的工作。低信噪比的问题通常通过对多次重复的录音进行平均来解决，也称为试验，但试验之间反应的可变性导致结果有偏见，并限制了可解释性。本文提出了利用图形表示法来解码神经反应的变异性。与其他现有的处理单次试验数据的方法相比，我们的方法有几个优点：第一，它避免了对神经反应波形模型的先验定义；第二，它不利用平均数据进行参数估计；第三，它通过提供代价函数的全局最优解而不受初始化问题的困扰；最后，它是快速的。我们分两个步骤进行。首先，基于图拉普拉斯的流形学习算法提供了一种关于响应变异性的试验排序的有效方法，条件是这种变异性本身依赖于单个参数。其次，变异性的估计被表示为一个组合优化问题，可以使用图割非常有效地求解。为延迟估计提供了这第二步的详细信息和验证。在合成数据上进行了性能和稳健性实验，并给出了基于P300古怪实验的脑电数据的结果。
Extracting information from multitrial magnetoencephalography or electroencephalography (EEG) recordings is challenging because of the very low SNR, and because of the inherent variability of brain responses. The problem of low SNR is commonly tackled by averaging multiple repetitions of the recordings, also called trials, but the variability of response across trials leads to biased results and limits interpretability. This paper proposes to decode the variability of neural responses by making use of graph representations. Our approach has several advantages compared to other existing methods that process single-trial data: first, it avoids the a priori definition of a model for the waveform of the neural response; second, it does not make use of the average data for parameter estimation; third, it does not suffer from initialization problems by providing solutions that are global optimum of cost functions; and last, it is fast. We proceed in two steps. First, a manifold learning algorithm, based on a graph Laplacian, offers an efficient way of ordering trials with respect to the response variability, under the condition that this variability itself depends on a single parameter. Second, the estimation of the variability is formulated as a combinatorial optimization that can be solved very efficiently using graph cuts. Details and validation of this second step are provided for latency estimation. Performance and robustness experiments are conducted on synthetic data, and results are presented on EEG data from a P300 oddball experiment.