Distribution-based minimum-norm estimation with multiple trials

Abstract

The goal of this study is to develop a source imaging method for electroencephalography and magnetoencephalography by analyzing a distance measure based on a Euclidean norm of difference between pre- and post-stimulus brain activities. Conventional source imaging techniques generally detect evoked responses by averaging multiple trials at each source point. These methods are limited in their ability to fully analyze complex brain signals with a mixture of evoked and induced activities because they compare means or variances. In this article, we propose a novel approach for eliciting significant evoked and induced activity. To this aim, response and baseline ranges from each trial are separately mapped in an anatomically constrained source space by minimum-norm estimation. The extent within a distribution and the distance between distributions of brain activities at each source point are estimated from the set of trials. Then, this distance analysis determines the degree of difference between the response and baseline activities. The statistical significance of the distance comparison was computed using a nonparametric permutation test. In the evaluation of simulated data sets, the proposed method provided robust images of the simulated location ( p<0.05), whereas the average method did not detect the perturbed source. A total of 200 randomly selected locations were tested with a signal-to-noise ratio (SNR) of 2 dB, and the error between simulated points and the maximum-value-points analyzed using this method was 9±15 mm.