In this paper we propose a new methodology for introducing thresholds in the analysis of neuro- biological databases. Often, in Neuroscience, absolute thresholds are adopted. This is done by cutting the data below (or above) predetermined values of the involved parameters, without an analysis of the distribution of the collected data concerning the phenomenon under investigation. Despite an absolute threshold could be rigorously defined in terms of physic parameters, it can be influenced by many different subjective aspects, including cognitive processes, and individual adaptation to the external stimuli. A possible related risk is that, mainly in experiments also de-pending on personal reactions, a significant portion of meaningful data, relevant for that specific task, could be neglected. In order to reduce these deviations, we are proposing to adopt a task-dependent approach, based on the comparison between the collected data and some database concerning a different task, assumed as a baseline. After giving the necessary theoretical back-ground, we test our methodology on real EEG data involving two subjects in a musical task. In addition to some natural results, new and unexpected neurological links can be emphasized and discussed.

中文翻译：

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选择神经生物学中依赖数据的阈值的统计建议。

在本文中，我们提出了一种在神经生物学数据库分析中引入阈值的新方法。通常，在神经科学中，采用绝对阈值。这是通过将数据切入所涉及参数的预定值以下（或之上）来完成的，而无需分析与所调查现象有关的收集数据的分布。尽管可以根据物理参数严格定义绝对阈值，但是它可能会受到许多不同的主观方面的影响，包括认知过程和对外部刺激的个体适应。一个可能的相关风险是，主要在实验中也要根据个人反应而定，可以忽略与该特定任务相关的有意义数据的很大一部分。为了减少这些偏差，我们建议采用一种基于任务的方法，该方法基于收集的数据与一些涉及不同任务的数据库之间的比较，并以此为基准。在提供必要的理论背景之后，我们在涉及音乐任务的两个对象的真实EEG数据上测试了我们的方法。除了某些自然结果外，还可以强调和讨论新的和意外的神经系统联系。