Abstract
Beamforming is one of the most commonly used methods for estimating the active neural sources from the MEG or EEG sensor readings. The basic assumption in beamforming is that the sources are uncorrelated, which allows for estimating each source independent of the others. In this paper, we incorporate the independence assumption of the standard beamformer in a linear dynamical system, thereby introducing the dynamic beamformer. Using empirical data, we show that the dynamic beamformer outperforms the standard beamformer in predicting the condition of interest which strongly suggests that it also outperforms the standard method in localizing the active neural generators.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Antelis, J., Minguez, J.: Dynamic solution to the EEG source localization problem using Kalman filters and particle filters. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 77–80 (2009)
Antelis, J., Minguez, J.: DYNAMO: Dynamic multi-model source localization method for EEG and/or MEG. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5141–5144 (2010)
Bahramisharif, A., van Gerven, M., Heskes, T., Jensen, O.: Covert attention allows for continuous control of brain-computer interfaces. Eur. J. Neurosci. 31(8), 1501–1508 (2010)
Beauchamp, M., Petit, L., Ellmore, T., Ingeholm, J., Haxby, J.: A parametric fMRI study of overt and covert shifts of visuospatial attention. NeuroImage 14(2), 310–321 (2001)
Bishop, C.: Pattern Recognition and Machine Learning. Springer (2006)
Dale, A., Liu, A., Fischl, B., Buckner, R., Belliveau, J., Lewine, J., Halgren, E.: Dynamic statistical parametric mapping: Combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron 26(1), 55–67 (2000)
Dale, A., Sereno, M.: Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cognitive Neurosci. 5(2), 162–176 (1993)
Gross, J., Kujala, J., Hämäläinen, M., Timmermann, L., Schnitzler, A., Salmelin, R.: Dynamic imaging of coherent sources: studying neural interactions in the human brain. P. Natl. Acad. Sci. USA 98(2), 694–699 (2001)
Hämäläinen, M., Ilmoniemi, R.: Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput. 32(1), 35–42 (1994)
Haufe, S., Tomioka, R., Nolte, G., Müller, K., Kawanabe, M.: Modeling sparse connectivity between underlying brain sources for EEG/MEG. IEEE T. Bio-Med. Eng. 57(8), 1954–1963 (2010)
Jensen, O., Bahramisharif, A., Oostenveld, R., Klanke, S., Hadjipapas, A., Okazaki, Y., van Gerven, M.: Using brain–computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience. Front. Psychol. 2(100) (2011)
Mattout, J., Phillips, C., Penny, W., Rugg, M., Friston, K.: MEG source localization under multiple constraints: an extended Bayesian framework. NeuroImage 30(3), 753–767 (2006)
Nolte, G.: The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors. Phys. Med. Biol. 48, 3637–3652 (2003)
Oostenveld, R., Fries, P., Maris, E., Schoffelen, J.: FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computat. Intell. Neurosc. 2011, 1 (2011)
Oppenheim, A., Willsky, A., Nawab, S.: Signals and Systems. Pearson education (1998)
Penny, W., Kiebel, S., Friston, K.: Variational Bayesian inference for fMRI time series. NeuroImage 19(3), 727–741 (2003)
Roweis, S., Ghahramani, Z.: A unifying review of linear Gaussian models. Neural Comput. 11(2), 305–345 (1999)
Van Veen, B., van Drongelen, W., Yuchtman, M., Suzuki, A.: Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE T. Bio-Med. Eng. 44(9), 867–880 (1997)
Wipf, D., Nagarajan, S.: A unified Bayesian framework for MEG/EEG source imaging. NeuroImage 44(3), 947–966 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bahramisharif, A., van Gerven, M.A.J., Schoffelen, JM., Ghahramani, Z., Heskes, T. (2012). The Dynamic Beamformer. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-34713-9_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34712-2
Online ISBN: 978-3-642-34713-9
eBook Packages: Computer ScienceComputer Science (R0)