DSL and MUSIC under model misspecification and noise-conditions

Sources of synchronized induced Gamma-Band responses during a simple object recognition task: a replication study in human MEG

Natural stimuli are compiled of numerous features, which are cortically represented in dispersed structures. Synchronized oscillations in the Gamma-Band (>30 Hz; induced Gamma-Band Responses, iGBRs), are regarded as a plausible mechanism to re-integrate these regions into a meaningful cortical object representation. Using electroencephalography (EEG) it was demonstrated that the generators of iGBRs can be localized to temporal, parietal, posterior, and frontal areas. The present magnetoencephalogram (MEG) study intended to replicate these findings in order contribute to the ongoing debate regarding the possible functional difference of high-frequency signals as measured by both techniques. During a standard object recognition task we found an augmentation of the iGBR after the presentation of meaningful as opposed to meaningless stimuli at approximately 160-440 ms after stimulus onset. This peak was localized to inferior temporal gyri, superior parietal lobules and the right middle frontal gyrus. Importantly, most of these brain structures were significantly phase-locked to each other. The implications of these results are twofold: (1) they present further evidence for the view that iGBRs signify neuronal activity in a broadly distributed network during object recognition. (2) MEG is well suited to detect induced high-frequency oscillations with a very similar morphology as revealed by EEG recordings, thereby eliminating known problems with electroencephalographical methods (e.g. reference confounds). In contrast to the iGBR, the localization of event-related fields (ERFs) and evoked Gamma-Band Response (eGBRs) revealed generators in focal visual areas, and thus, seem to mirror early sensory processing.

An alternative subspace approach to EEG dipole source localization

In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist.

Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

A spatial filtering method for localizing sources of brain electrical activity from surface recordings is described and analyzed. The spatial filters are implemented as a weighted sum of the data recorded at different sites. The weights are chosen to minimize the filter output power subject to a linear constraint. The linear constraint forces the filter to pass brain electrical activity from a specified location, while the power minimization attenuates activity originating at other locations. The estimated output power as a function of location is normalized by the estimated noise power as a function of location to obtain a neural activity index map. Locations of source activity correspond to maxima in the neural activity index map. The method does not require any prior assumptions about the number of active sources of their geometry because it exploits the spatial covariance of the source electrical activity. This paper presents a development and analysis of the method and explores its sensitivity to deviations between actual and assumed data models. The effect on the algorithm of covariance matrix estimation, correlation between sources, and choice of reference is discussed. Simulated and measured data is used to illustrate the efficacy of the approach.

The usefulness of the Laplacian in principal component analysis and dipole source localization

Evoked potentials are difficult to analyze because multiple sources are active simultaneously. Principal component analysis and dipole localization are two techniques that have been used to disentangle overlapping sources. Both of these techniques have problems. Principal component analysis suffers from a rotation ambiguity. Dipole localization suffers from biases when the model used to derive the sources from the scalp potentials is misspecified. Using computer simulations we demonstrate that by applying both of these techniques to the Laplacian of the voltages rather than to the raw voltages the problems associated with the two techniques are reduced. Computer programs for the analyses are presented in an Appendix.