Bootstrap significance of low SNR evoked response

In order to obtain adequate signal to noise ratio (SNR), stimulus-evoked brain signals are averaged over a large number of trials. However, in certain applications, e.g. fetal magnetoencephalography (MEG), this approach fails due to underlying conditions (inherently small signals, non-stationary/poorly characterized signals, or limited number of trials). The resulting low SNR makes it difficult to reliably identify a response by visual examination of the averaged time course, even after pre-processing to attenuate interference. The purpose of this work was to devise an intuitive statistical significance test for low SNR situations, based on non-parametric bootstrap resampling. We compared a two-parameter measure of p-value and statistical power with a bootstrap equal means test and a traditional rank test using fetal MEG data collected with a light flash stimulus. We found that the two-parameter measure generally agreed with established measures, while p-value alone was overly optimistic. In an extension of our approach, we compared methods to estimate the background noise. A method based on surrogate averages resulted in the most robust estimate. In summary we have developed a flexible and intuitively satisfying bootstrap-based significance measure incorporating appropriate noise estimation.