Leibniz Institute for Neurobiology, Brenneckestraße 6, 39118 Magdeburg, Germany.
J Neurosci Methods. 2011 Jul 15;199(1):119-28. doi: 10.1016/j.jneumeth.2011.04.019. Epub 2011 Apr 22.
We present a new paradigm for the adaptive estimation of evoked brain responses in single trials, based upon the combination of the matching pursuit (MP) algorithm and template matching, and referred to as Template Matching Pursuit (TMP). In contrast to the classical template matching with invariant single-trial morphology and to previous approaches using MP with strong similarity constraint on functions in sequential trials, this adaptive approach allows for a wide variety of waveforms, and its universality is retained by parametrizing all relevant waveforms in terms of Gabor functions. A survey of single-trial estimates obtained for 10 subjects (∼4000 individual trials in total) confirms the validity of the assumption of a good approximation of single-trial waveforms. Owing to the fully parametric approach, we can easily perform also any quantitative analysis of such a huge dataset. As an example we take the trial-to-trial variability of the peak amplitude and latency of the auditory M100 component. This methodology provides estimates of diversified morphologies, which makes it free from the limitations inherent to any restrictive model. This seems advantageous in the context of the ongoing debate as to the neural mechanisms of average evoked brain responses.
我们提出了一种新的范例，用于在单次试验中自适应估计诱发电脑反应，该范例基于匹配追踪（MP）算法和模板匹配的组合，并称为模板匹配追踪（TMP）。与具有不变的单试形态的经典模板匹配以及先前使用对连续试次中的函数具有强相似性约束的 MP 的方法不同，这种自适应方法允许各种波形，并且通过将所有相关波形参数化为 Gabor 函数来保留其普遍性。对 10 个受试者（总共约 4000 个个体试验）的单次试验估计的调查证实了对单个试验波形进行良好逼近的假设的有效性。由于采用了完全参数化的方法，我们还可以轻松地对如此庞大的数据集进行任何定量分析。作为一个例子，我们采用听觉 M100 成分的峰值幅度和潜伏期的试验间变异性。该方法提供了多样化形态的估计，从而使其不受任何限制性模型固有的限制。在有关平均诱发电脑反应的神经机制的持续辩论中，这似乎具有优势。
