Performance of beamformers on EEG source reconstruction

Yaqub Jon Mohamadi, Govinda Poudel, Carrie R Innes, Richard Deneice Jones

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


Recently a number of new beamformers have been introduced for reconstruction and localization of neural sources from EEG and MEG. However, little is known about the relative performance of these beamformers. In this study, 8 scalar beamformers were examined with respect to several parameters to determine how effective they are at reconstruction of a dipole time course from EEG. A simulated EEG signal was produced by means of forward head modelling for projection of an artificial dipole on scalp electrodes then superimposed on background signal. Both real EEG and white noise were applied as background activity. Although the eigenspace beamformer can perform slightly better than other beamformers for small dipoles, and even more so for large dipoles, it is not a contender for real-time beamforming of EEG as it cannot be completely automated. Overall, in terms of performance, robustness to variations in parameters, and ease of application, the minimum variance and Borgiotti-Kaplan beamformers were found to be the best performers.
Original languageEnglish
Title of host publicationEngineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
EditorsGert Cauwenberghs, James D Weiland
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2517 - 2521
Number of pages5
ISBN (Print)9781424441198
Publication statusPublished - 2012
EventInternational Conference of the IEEE Engineering in Medicine and Biology Society 2012 - Hilton San Diego Bayfront, San Diego, United States of America
Duration: 28 Aug 20121 Sep 2012
Conference number: 34th


ConferenceInternational Conference of the IEEE Engineering in Medicine and Biology Society 2012
Abbreviated titleEMBC 2012
CountryUnited States of America
CitySan Diego

Cite this