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Reducing Multi-Sensor Data To A Single Time Course That Reveals Experimental Effects, Aaron Schurger, Sebastien Marti, Stanislas Dehaene

Psychology Faculty Articles and Research

Background: Multi-sensor technologies such as EEG, MEG, and ECoG result in high-dimensional data sets. Given the high temporal resolution of such techniques, scientific questions very often focus on the time-course of an experimental effect. In many studies, researchers focus on a single sensor or the average over a subset of sensors covering a “region of interest” (ROI). However, single-sensor or ROI analyses ignore the fact that the spatial focus of activity is constantly changing, and fail to make full use of the information distributed over the sensor array.

Methods: We describe a technique that exploits the optimality and …