Independent Component Analysis of Sparse-transformed EEG Signals for ADHD/Normal Adults' Classification

The present study investigates the EEG source differences between adults with ADHD and aged match controls. The processing method is based on sparse representation of electrode signals and complex-valued independent component analysis with a robust measure of sparseness. Combination of scalp topography, estimated dipole source location and spectral patterns of resulted ICs were used to k-means clustering and identification of near-equivalent ICs across subjects. Several frequency features were extracted from clustered ICs and individually submitted to k-nn classifier. The best resulted accuracy was 86.36% using (mean feature at R-parietal cluster. Eight pairs of features resulted in such accuracy. The method used in this study not only improves the participant's classification accuracy compared to reference analysis, but also better identifies the dynamic of brain source signals than time domain ICA algorithms.