Maximum likelihood dipole fitting in spatially colored noise.

Abstract

We evaluated a maximum likelihood dipole-fitting algorithm for somatosensory evoked field (SEF) MEG data in the presence of spatially colored noise. The method exploits the temporal multiepoch structure of the evoked response data to estimate the spatial noise covariance matrix from the section of data being fit, which eliminates the stationarity assumption implicit in prestimulus based whitening approaches. The performance of the method, including its effectiveness in comparison to other localization techniques (dipole fitting, LCMV and MUSIC) was evaluated using the bootstrap technique. Synthetic data results demonstrated robustness of the algorithm in the presence of relatively high levels of noise when traditional dipole fitting algorithms fail. Application of the algorithm to adult somatosensory MEG data showed that while it is not advantageous for high SNR data, it definitely provides improved performance (measured by the spread of localizations) as the data sample size decreases.