A Statistical Proposal for Selecting a Data-depending Threshold in Neurobiology

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.