Objectives: We propose 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 method is minimally dependent on subjective decisions.
Methods: Ten electroencephalographic (EEG) data matrices were generated by computer, each matrix including real background activity from a normal subject and an array of added simulated spikes. Each spike was a summation of two transients originating from slightly different 'original dipole sources'. The unaveraged EEG matrices were decomposed by ICA, and source estimation was performed by applying RAP-MUSIC to the spatial information defined by the ICA components showing epileptiform activity in their waveform. For comparison, dipoles were also estimated from the same matrices using two existing methods: RAP-MUSIC based on eigen-decomposition of the covariance matrices of averaged spikes and common spatial pattern decomposition.
Results: In every simulated EEG data matrix, two dipoles close to the original sources were estimated by the present method. Their unaveraged activities were also similar to those of the original sources. The two existing methods gave less precise results than the proposed method.
Conclusions: RAP-MUSIC based on ICA thus proved promising for source estimation of unaveraged epileptiform discharges.