Source estimation of spikes by a combination of independent component analysis and RAP-MUSIC

Abstract

We designed a combination of independent component analysis (ICA) and recursively applied and projected multiple signal classification (RAP-MUSIC) as a new approach to dipole source estimation of epileptiform discharges. The estimation is minimally influenced by subjective decisions in this analysis. A simulation study was performed by generating 10 EEG data matrices by means of a computer: each matrix included real background activity from a normal subject to which an array of simulated unaveraged spikes was added. Each discharge was a summation of two transients originating from slightly different “original dipole sources”. The simulated spikes in the unaveraged EEG data were extracted from the background by ICA as spatiotemporal components, each component having fixed potential field distribution and maximally independent waveform. The complete separation between spikes and background was objectively proven by reconstruction of the EEG from the decomposed components. RAP-MUSIC was performed based on the spatial information included in the epileptic ICA components. RAP-MUSIC does not involve subjective decisions such as selection of multiple local score peaks, determination of the number of dipoles or initial guesses of dipole parameters. In every simulated EEG data matrix, two dipoles close to the original sources were estimated. Their activities were also similar to those of the original sources. For comparison, the same simulated EEG data were averaged and subjected to RAP-MUSIC using eigen-decomposition of the covariance matrices. Only one dipole was estimated in every simulation by this conventional method and therefore the result of the present method was better. RAP-MUSIC based on ICA, thus, proved promising for source estimation of unaveraged epileptiform discharges.