EMS filtering improves the quality of single-trial time courses. Panel (A) presents data from a representative subject, comparing the ROI method (left) to the EMS filtering method (right). There are two columns of two raster plots, with time on the horizontal and trial # on the vertical. Amplitude is coded in color, going from blue (negative) to green (zero) to red (positive), and the color axis is scaled to the minimum and maximum amplitude in each data matrix. Below each pair of raster plots (top: lag-9 condition; bottom: control condition) is the time course of the difference between the means of the two conditions, mean(lag-9) – mean(control). No smoothing was applied to the images. Panel (B) shows the estimated mean signal-to-noise ratio as a function of the number of trials averaged together, for the ROI (blue) and EMS filtering (green) methods. Panel (C) shows the mean performance (10 subjects) of a univariate Gaussian naïve-Bayes (GNB) classifier tested on the output of a nested EMS filtering procedure (green; see methods). The performance of a GNB classifier applied to the mean over the ROI (blue) and the performance of a linear support-vector machine (red) are shown for comparison. Notice that the performance of a univariate decision rule (GNB) applied to the output of EMS filtering is comparable to the performance of a multivariate linear SVM applied to the original sensor data.
Schurger et al. BMC Neuroscience 2013 14:122 doi:10.1186/1471-2202-14-122