Objectives: We tried to estimate epileptic sources by a combination of independent component analysis (ICA) and recursively applied and projected multiple signal classification (RAP-MUSIC) in real epileptiform EEG discharges.
Methods: EEG data including an array of spikes from 3 patients were decomposed by ICA, and source estimation was performed by applying RAP-MUSIC to the spatial information defined by the set of ICA components that showed epileptiform activity in their waveform. Sources were also estimated from the same data using RAP-MUSIC based on eigen-decomposition of the covariance matrix of averaged spikes, and common spatial pattern decomposition for comparison.
Results: RAP-MUSIC based on ICA could estimate generally correct epileptic sources in the 3 patients, and its results were better than those of the other methods, when compared to intracerebral data. The present analysis proceeded without introduction of subjective decision after data selection. The separation of epileptiform discharges from the background is essential for this analysis, and was successfully performed in the real EEG data.
Conclusions: RAP-MUSIC based on ICA appears promising for estimation of epileptic sources with minimal dependence on subjective decisions in the process of analysis. In particular, it was not necessary to select the number of sources.