Abstract
A novel Weighted Unscented Kalman Filtering method is introduced for neural activity estimation from electroencephalographic signals. The introduction of a weighting stage improves the solution by extracting relevant information directly from the measured data. Besides, a discrete nonlinear state space model representing the brain neural activity is used as a physiological constraint in order to improve the estimation. Moreover, time-varying parameters are considered which allow describing adequately healthy and pathological activity even for localized epilepsy events. Performance of the new method is evaluated in terms of introduced error measurements by application to simulated EEG data over several noise conditions. As a result, a considerable improvement over linear estimation approaches is found.
This is a preview of subscription content, log in via an institution to check access.
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
Giraldo, E., den Dekker, A.J., Castellanos-Dominguez, G.: Estimation of dynamic neural activity using a Kalman filter approach based on physiological models. In: 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, Argentina, September 1-5 (2010)
Giraldo, E., Peluffo-Ordoñez, D., Castellanos, G.: Weighted time series analysis for electroencephalographic source localization. Journal of the Faculty of Mines Dyna Universidad Nacional de Colombia - Sede Medellın 79(176), 64–70 (2012)
Kim, J.W., Shin, H.B., Robinson, P.A.: Compact continuum brain model for human electroencephalogram. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 6802 (2007), doi:10.1117/12.759005
Giraldo, E., Castaño-Candamil, J.S., Castellanos-Dominguez, C.G.: A Weighted Dynamic Inverse Problem for Electroencephalographic Current Density Reconstruction. In: 6th International IEEE EMBS Conference on Neural Engineering, San Diego, California, November 6-8 (2013)
Hallez, H., Vanrumste, B., Grech, R., Muscat, J., Clercq, W., Velgut, A., D’Asseler, Y., Camilleri, K., Fabri, S., Van Huffel, S., Lemahieu, I.: Review on solving the forward problem in eeg source analysis. Journal of NeuroEngineering and Rehabilitation 4(46), 101–113 (2007)
Connors, W., Trappenberg, T.: Improved path integration using a modified weight combination method. Cognitive Computation 5(3), 295–306 (2013), doi:10.1007/s12559-013-9209-0
Barton, M., Robinson, P., Kumar, S., Galka, A., Durrant-White, H., Guivant, J., Ozaki, T.: Evaluating the performance of kalman-filter-based eeg source localization. IEEE Transactions on Biomedical Engineering 56(1), 435–453 (2009)
Grech, R., Tracey, C., Muscat, J., Camilleri, K., Fabri, S., Zervakis, M., Xanthoupoulos, P., Sakkalis, V., Vanrumste, B.: Review on solving the inverse problem in eeg source analysis. Journal of NeuroEngineering and Rehabilitation 5(25), 792–800 (2008)
Haykin, S.: Kalman Filtering and Neural Networks. Wiley (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Padilla-Buritica, J.I., Giraldo-Suárez, E., Castellanos-Dominguez, G. (2015). Weighted Filtering for Neural Activity Reconstruction Under Time Varying Constraints. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-18914-7_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18913-0
Online ISBN: 978-3-319-18914-7
eBook Packages: Computer ScienceComputer Science (R0)