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The EEG source localization is an ill-posed problem. It involves estimationof the sources which outnumbers the number of measurements. For a givenmeasurement at given time all sources are not active which makes the problem assparse inversion problem. This paper presents a new approach for dense arrayEEG source localization. This paper aims at reducing the solution space to onlymost certain sources and thereby reducing the problem of ill-posedness. Thisemploys a two-stage method where the first stage finds the most certain sourcesthat are likely to produce the observed EEG by using a statistical measure ofsources, the second stage solves the inverse problem by restricting thesolution space to only most certain sources and their neighbors. This reducesthe solution space for other source localization methods hence improvise theiraccuracy in localizing the active neurological sources in the brain which isthe main goal. This method has been validated and applied to real 256 channeldata and the results were analyzed.

Certainty based Reduced Sparse Solution for Dense Array EEG Source Localization