Exploring the Variability of Single Trials in Somatosensory Evoked Responses Using Constrained Source Extraction and RMT

A. Koutras, G. K. Kostopoulos, A. A. Ioannides
2008 IEEE Transactions on Biomedical Engineering  
This paper describes the theoretical background of a new data-driven approach to encephalographic single-trial (ST) data analysis. Temporal constrained source extraction using sparse decomposition identifies signal topographies that closely match the shape characteristics of a reference signal, one response for each ST. The correlations between these ST topographies are computed for formal Correlation Matrix Analysis (CMA) based on Random Matrix Theory (RMT). The RMT-CMA provides clusters of
more » ... ilar ST topologies in a completely unsupervised manner. These patterns are then classified into deterministic set and noise using well established RMT results. The efficacy of the method is applied to EEG and MEG data of somatosensory evoked responses (SERs). The results demonstrate that the method can recover brain signals with time course resembling the reference signal and follow changes in strength and/or topography in time by simply stepping the reference signal through time. Index Terms-Constrained source extraction, electroencephalography (EEG), Independent Component Analysis (ICA), magnetoencephalography (MEG), random matrix theory.
doi:10.1109/tbme.2008.915708 pmid:18334387 fatcat:fzqafxjinreqjlikzituj6axuy