Estimation of single-trial multicomponent ERPs: Differentially variable component analysis (dVCA)
Estimation of single-trial multicomponent ERPs: Differentially variable component analysis (dVCA)
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DOI：
10.1007/s00422-003-0433-7
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发表时间：
2003-12
影响因子：
1.9
通讯作者：
W. Truccolo;K. Knuth;Ankoor S. Shah;S. Bressler;C. Schroeder;M. Ding
中科院分区：
文献类型：
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作者：
W. Truccolo;K. Knuth;Ankoor S. Shah;S. Bressler;C. Schroeder;M. Ding
提出了一种估计单次试验、多分量、事件相关电位参数的贝叶斯推理框架。单次试验记录被建模为正在进行的活动和多分量波形的线性组合，这些波形相对锁定于某些感觉或运动事件。假设每个分量具有与试验相关的幅度缩放因子和延迟移位的试验不变波形。该模型的最大Posteriorisolution通过迭代算法实现，从中估计组件的波形，单次试验幅度缩放因子和延迟移位。基于其差异可变性，可以从单通道记录中导出多个分量，这与其他分量分析技术（例如，独立分量分析）相比，其中估计的分量数量等于或小于记录通道的数量。此外，我们表明，通过从每个单次试验记录中减去估计的单次试验成分，可以估计正在进行的活动，从而提供有关任务相关的大脑动力学的额外信息。我们在模拟数据上测试了这种方法，我们将其命名为差分变量成分分析（dVCA），并将其应用于一个实验数据集，该数据集由执行视觉运动模式识别任务的猴子的皮质内记录的局部场电位组成。
A Bayesian inference framework for estimating the parameters of single-trial, multicomponent, event-related potentials is presented. Single-trial recordings are modeled as the linear combination of ongoing activity and multicomponent waveforms that are relatively phase-locked to certain sensory or motor events. Each component is assumed to have a trial-invariant waveform with trial-dependent amplitude scaling factors and latency shifts. AMaximum a Posteriorisolution of this model is implemented via an iterative algorithm from which the component’s waveform, single-trial amplitude scaling factors and latency shifts are estimated. Multiple components can be derived from a single-channel recording based on their differential variability, an aspect in contrast with other component analysis techniques (e.g., independent component analysis) where the number of components estimated is equal to or smaller than the number of recording channels. Furthermore, we show that, by subtracting out the estimated single-trial components from each of the single-trial recordings, one can estimate the ongoing activity, thus providing additional information concerning task-related brain dynamics. We test this approach, which we name differentially variable component analysis (dVCA), on simulated data and apply it to an experimental dataset consisting of intracortically recorded local field potentials from monkeys performing a visuomotor pattern discrimination task.