No abstract

This paper deals with blind separation of finite alphabet sources where we have n sources and only one observation. The method is applied directly in time (spatial) domain and no transformation is needed. It follows a two stage procedure. In the first stage the mixing coefficients are estimated, and in the second stage the sources are separated using the estimated mixing coefficients. We also study restrictions of this method and conditions for which its performance is acceptable. Simulation results are presented to show the ability of this method to source separation in images and pulse amplitude modulation (PAM) signals.

In this paper, we present a fast method to extract the sources related to interictal epileptiform state. The method is based on general eigenvalue decomposition using two correlation matrices during: 1) periods including interictal epileptiform discharges (IED) as a reference activation model and 2) periods excluding IEDs or abnormal physiological signals as background activity. After extracting the most similar sources to the reference or IED state, IED regions are estimated by using multiobjective optimization. The method is evaluated using both realistic simulated data and actual intracerebral electroencephalography recordings of patients suffering from focal epilepsy. These patients are seizure-free after the resective surgery. Quantitative comparisons of the proposed IED regions with the visually inspected ictal onset zones by the epileptologist and another method of identification of IED regions reveal good performance.