Improvement in EEG Source Imaging Accuracy by Means of Wavelet Packet Transform and Subspace Component Selection

The electroencephalograph (EEG) source imaging (ESI) method is a non-invasive that provides high temporal resolution of brain electrical activity on the cortex. However, because accuracy EEG often affected by unwanted signals such as noise or other source-irrelevant signals, results ESI are incongruous with real sources activities. This study presents novel (WPESI) based wavelet packet transform (WPT) and subspace component selection to image cerebral activities First, original decomposed into several components WPT. Second, subspaces associated selected relevant reconstructed Finally, current density distribution in cortex obtained establishing boundary element model (BEM) from head MRI applying appropriate inverse calculation. In this study, localization proposed approach were better than those sLORETA (OESI) computer simulations visual evoked potential (VEP) experiments. For epilepsy patients, estimated algorithm conformed seizure onset zones. WPESI easy implement achieved favorable terms imaging. demonstrates for use localize epileptogenic foci scalp signals.