Sparse EEG Source Localization Using LAPPS: Least Absolute l -P (0 < p < 1) Penalized Solution
Electroencephalographic (EEG) is commonly used to study the brain activity with high temporal resolution, but it is usually inevitably contaminated by strong outliers. Here, we propose a novel EEG source localization algorithm, LAPPS, which employs the l 1-loss for the residual error to alleviate the effect of outliers and another l p-penalty norm (p=0.5) to obtain sparse sources while suppressing Gaussian noise in EEG recordings. The simulation results in various dipoles configurations under various SNRs prove the superiority of LAPPS. In a real visual oddball experiment, LAPPS also obtained sparse activations consistent with previous findings revealed by EEG and fMRI.