利用微分幅度和潜伏期变异性分离多个诱发反应
Separation of multiple evoked responses using differential amplitude and latency variability
摘要：
在神经电生理学中，人们记录由同步活动的神经元集合响应外部呈现的刺激而产生的电势或磁场。这些诱发反应通常是由多个发生器在存在持续的背景活动的情况下产生的。虽然源定位技术或电流源密度估计通常用于识别发电机，盲源分离技术的应用，以获得独立的组件已经变得更加流行。我们通过将贝叶斯方法应用于更生理现实的源模型来解决这个问题。由于普遍认为单次试验的振幅和潜伏期不同，因此我们将这种可变性纳入模型中。而不是不切实际的假设，这些皮层组件是相互独立的，我们的算法利用的差异幅度和潜伏期变化的诱发波形，以确定皮层组件。该算法被施加到皮层内记录的局部场电位在猴子执行视觉任务。
英文摘要：
In neuroelectrophysiology one records electric potentials or magnetic fields generated by ensembles of synchronously active neurons in response to externally presented stimuli. These evoked responses are often produced by multiple generators in the presence of ongoing background activity. While source localization techniques or current source density estimation are usually used to identify generators, application of blind source separation techniques to obtain independent components has become more popular. We approach this problem by applying the Bayesian methodology to a more physiologically-realistic source model. As it is generally accepted that single trials vary in amplitude and latency, we incorporate this variability into the model. Rather than making the unrealistic assumption that these cortical components are independent of one another, our algorithm utilizes the differential amplitude and latency variability of the evoked waveforms to identify the cortical components. The algorithm is applied to intracortically-recorded local field potentials in monkeys performing a visuomotor task.
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