Robust EEG Source Localization Using Subspace Principal Vector Projection Technique

ElectroEncephaloGram (EEG) signals based Brain Source Localization (BSL) has been an active area of research. The performance of BSL algorithms is severely degraded in the presence of background interferences. Pre-Whitening (PW) based approach to deal with such interference assumes temporal stationarity of the data which does not hold good for EEG based processing. Null Projection (NP) based approach relaxes the temporal stationarity. However, the strict spatial stationarity of the number of interfering sources is maintained between control state and activity state measurement. In practical scenarios where an interference source that exists only in the control state, and does not appear in activity state, NP based approach removes a higher dimension space from the activity data leading to its poor performance. The proposed Subspace Principal Vector Projection (SPVP) based approach utilizes subspace correlation based common interference statistics and thus relaxing the strict spatial stationarity condition. In particular, SPVP based MUltiple SIgnal Classification (MUSIC) and Linearly Constrained Minimum Variance (LCMV) algorithms are presented for BSL. Simulation and experiment with real EEG data from Physionet dataset involving motor imagery task illustrate the effectiveness of the proposed algorithms in robust BSL with interference suppression.