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.
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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
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DOI: https://doi.org/10.1007/978-3-642-34713-9_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34712-2
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