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Bayesian Analysis of Single Trial Cortical Event-Related Components

机译：单次皮质事件相关成分的贝叶斯分析

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摘要

A common technique in neurophysiology is the recording of electric potentials generated by cortical neuronal ensembles in relation to a specific event. The understanding of event-related potentials requires the identification of signals that are relatively phase-locked to a stimulus or event onset (event-related potentials) as well as non-phase locked activities. It is now widely accepted that the recorded phase-locked signal itself is not a homogeneous signal, but instead a combination of different components, which can vary in amplitude and latency from trial to trial. We approach the problem of identifying event-related component waveforms and their trial-to-trial variability from a Bayesian perspective. We employ a signal model consisting of a set of unknown source waveforms each with their own set of trial-to-trial amplitudes and latencies. Differential variability of the sources from trial to trial aids significantly in the identification of the component waveforms. The posterior probability density is derived for a specified number of event-related components using data from single or multiple sensors. The Maximum A Posteriori (MAP) solution is used to obtain the event-related component waveforms and their single trial parameters. The approach is demonstrated using a data set consisting of intracortically recorded local field potentials (LFP) in monkeys performing a visuomotor pattern discrimination task.
机译：神经生理学中的一种常用技术是记录与特定事件有关的由皮层神经元集合产生的电势。对与事件相关的电位的理解要求识别相对于刺激或事件发作相对锁定的信号（与事件相关的电位）以及非相位锁定的活动。现在，已被广泛接受的是，记录的锁相信号本身不是同质信号，而是不同分量的组合，不同分量的幅度和等待时间可能因试验而异。我们从贝叶斯的角度探讨识别事件相关分量波形及其试验间差异的问题。我们采用的信号模型由一组未知的源波形组成，每个波形都有自己的一组试验到试验的幅度和延迟。从试验到试验的不同来源之间的差异性在确定分量波形方面有很大帮助。使用来自单个或多个传感器的数据，为指定数量的事件相关组件得出后验概率密度。最大后验（MAP）解决方案用于获取事件相关的分量波形及其单个试验参数。使用由执行视觉运动模式识别任务的猴子皮层内记录的局部场电势（LFP）组成的数据集演示了该方法。

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