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Localizing Event-Related Potentials Using Multi-source Minimum Variance Beamformers: A Validation Study

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Abstract

Adaptive and non-adaptive beamformers have become a prominent neuroimaging tool for localizing neural sources of electroencephalographic (EEG) and magnetoencephalographic (MEG) data. In this study, we investigated single-source and multi-source scalar beamformers with respect to their performances in localizing and reconstructing source activity for simulated and real 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 (0.1–0.9), inter-source correlations (0.3–0.9), number of simultaneously active sources (2–8), and source locations. Results showed that localization performance 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 that for SPA. A similar trend was also found when applying these beamformer approaches to a real EEG dataset. Based on our findings, we conclude that multi-source beamformers (MIA and SIA) are an improvement over single-source beamformers for localizing EEG. Importantly, our new search method, MIA, had 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 event-related potentials.

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Fig. 1

Source reconstruction results regarding “observed” locations and waveforms were compared to the “true” source locations and waveforms. See main text for more detailed descriptions of the steps for the SPA, SIA, and MIA pathways and of the comparisons between “true” and “observed” source waveforms and locations

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Funding

Funding was provided by Natural Sciences and Engineering Research Council of Canada and University of British Columbia.

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Authors and Affiliations

  1. Faculty of Medicine, School of Audiology and Speech Sciences, University of British Columbia, 2177 Wesbrook Mall, Vancouver, V6T 1Z3, Canada

    Anthony T. Herdman

  2. Behavioral and Cognitive Neuroscience Institute (BCNI), Simon Fraser University, Burnaby, Canada

    Anthony T. Herdman, Alexander Moiseev & Urs Ribary

  3. Department of Psychology, Simon Fraser University, Burnaby, Canada

    Urs Ribary

Authors
  1. Anthony T. Herdman
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  2. Alexander Moiseev
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  3. Urs Ribary
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Correspondence to Anthony T. Herdman.

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Handling Editor: Seppo P. Ahlfors.

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Herdman, A.T., Moiseev, A. & Ribary, U. Localizing Event-Related Potentials Using Multi-source Minimum Variance Beamformers: A Validation Study. Brain Topogr 31, 546–565 (2018). https://doi.org/10.1007/s10548-018-0627-x

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