Single-trial evoked brain responses modeled by multivariate matching pursuit.

We present a new approach to the analysis of brain evoked electromagnetic potentials and fields. Multivariate version of the matching pursuit algorithm (MMP) performs an iterative, exhaustive search for waveforms, which optimally fit to signal structures, persistent in all the responses (trials) with the same time of occurrence, frequency, phase, and time width, but varying amplitude. The search is performed in a highly redundant time--frequency dictionary of Gabor functions, i.

SignalML: metaformat for description of biomedical time series.

This paper introduces a complete and elegant solution to the problem of inherent incompatibility of different formats used for digital storage of biomedical time series (in particular EEG) and their annotations. We define a simple XML-based language, in which information on the structure of binary data files can be simply and efficiently coded. In most cases, description of an existing format takes relatively few lines of XML code.