A spatiotemporal framework for MEG/EEG evoked response amplitude and latency variability estimation.
A spatiotemporal framework for MEG/EEG evoked response amplitude and latency variability estimation.
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DOI：
10.1109/tbme.2009.2032533
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发表时间：
2010-03
期刊：
影响因子：
--
通讯作者：
Wakai RT
中科院分区：
文献类型：
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作者：
Limpiti T;Van Veen BD;Wakai RT
本文提出了一个时空框架，用于从诱发反应的MEG/EEG数据中估计单次试验反应潜伏期和振幅。空间和时间基础被用来捕捉在试验中一致的诱发反应的各个方面。假设试验幅值独立，但具有相同的底层正态分布，均值和方差未知。假设试验延迟是确定的，但是未知的。我们假设噪声在空间上与未知的协方差矩阵相关。我们介绍了一种广义的期望最大化算法，称为TriViAL（幅度和延迟的试验可变性），它计算幅度、延迟、基系数和噪声协方差矩阵的最大似然（ML）估计。该方法还通过扫描琐碎算法在对应于皮层表面不同位置的空间基上进行ML源定位。源位置被标识为与大似然值相对应的位置。仿真数据和人体诱发反应实验验证了该算法的有效性。通过手指的触觉刺激来验证定位性能。利用已知的M100听觉反应潜伏期对刺激音调频率的依赖性，该算法在估计潜伏期变异性方面的有效性得到了证明。我们还证明，当信号模型中包含延迟时，响应幅度的估计得到改善。
This paper presents a spatio-temporal framework for estimating single-trial response latencies and amplitudes from evoked response MEG/EEG data. Spatial and temporal bases are employed to capture the aspects of the evoked response that are consistent across trials. Trial amplitudes are assumed independent but have the same underlying normal distribution with unknown mean and variance. The trial latency is assumed to be deterministic but unknown. We assume the noise is spatially correlated with unknown covariance matrix. We introduce a generalized expectation-maximization algorithm called TriViAL (Trial Variability in Amplitude and Latency) which computes the maximum likelihood (ML) estimates of the amplitudes, latencies, basis coefficients, and noise covariance matrix. The proposed approach also performs ML source localization by scanning the TriViAL algorithm over spatial bases corresponding to different locations on the cortical surface. Source locations are identified as the locations corresponding to large likelihood values. The effectiveness of the TriViAL algorithm is demonstrated using simulated data and human evoked response experiments. The localization performance is validated using tactile stimulation of the finger. The efficacy of the algorithm in estimating latency variability is shown using the known dependence of the M100 auditory response latency to stimulus tone frequency. We also demonstrate that estimation of response amplitude is improved when latency is included in the signal model.