Poster E96, Monday, March 27, 2:30 – 4:30 pm, Pacific Concourse
Localizing Event-Related Potentials using New Approaches to Multi-source Minimum Variance Beamforming
Anthony Herdman1, Alexander Moiseev2, Urs Ribary2; 1University of British Columbia, Canada, 2Simon Fraser University, Canada
Adaptive and non-adaptive beamformers have become a prominent neuroimaging tool for localizing neural sources of electroencephalographic (EEG) and magnetoencephalographic (MEG) data. We investigated single-source and multi-source scalar beamformers with respect to their performances in localizing and reconstructing source activity for simulated EEG data. We compared a new multi-source search approach (multi-step iterative approach; MIA) to our previous multi-source search approach (single-step iterative approach; SIA) and a single-source search approach (single-step peak approach; SPA). In order to compare performances across these beamformer approaches, we manipulated various simulated source parameters, such as the amount of signal-to-noise ratio (0.1 to 0.9), inter-source correlations (0.3 to 0.9), number of simultaneously active sources (2 to 8), and source locations. Results showed that localization performances followed the order of MIA>SIA>SPA regardless of the number of sources, source correlations, and single-to-noise ratios. In addition, SIA and MIA were significantly better than SPA at localizing four or more sources. Moreover, MIA was better than SIA and SPA at identifying the true source locations when signal characteristics were at their poorest. Source waveform reconstructions were similar between MIA and SIA but were significantly better than those for SPA. Based on our findings, we conclude that multi-source scalar beamformers (MIA and SIA) are an improvement over scalar beamformers for localizing EEG. Importantly, our new search method, MIA, had a better localization performance, localization precision, and source waveform reconstruction as compared to SIA or SPA. We therefore recommend its use for improved source localization and waveform reconstruction of ERPs.
Topic Area: METHODS: Neuroimaging