EEG ANALYSIS USING TIME-FREQUENCY SIGNAL PROCESSING TECHNIQUE
Abstract
Analyzing the interaction among electroencephalogram (EEG) data is an important step to understand the brain process. There have been many studies of directional effective influence between regions of the brain using EEG. However, most of the previous studies are based on the assumption of time-invariant connectivity structure, which is insufficient to understand the change of the connectivity during cognitive tasks. In this paper, we introduce the time-frequency method to analyzing the eeg data using autoregressive model. The method allows the examination of the dynamic interactions among brain regions during cognitive tasks using Kalman filter and Partial Directed Coherence. The result of the simulation study and the real data indicates that this approach is effective to estimate the time-varying connectivity among EEG data.
References
Rieder, M. K., Rahm, B., Williams, J. D., and Kaiser, J. (2011). Human -band activity and behavior. International Journal of Psychophysiology, 79:39-48, 2011.
K.J. Friston, L. Harisson, and W.D. Penny, “Dynamic causal modelling,” Neuroimage, Vol. 19, pp. 1273-1302, 2003
Granger, C. W. J., “Investigating causal relations by econometric models and crossspectral methods”. Econometrica, 37(3):424-438, 1969
Roebroeck, A., Formisano, E., and Goebel, R., “Mapping directed in uence over the brain using granger causality and fmri”. NeuroImage, 25(1):230-242, 2005.
Goebel R, Roebroeck A, Kim DS, Formisano E, “Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping”. Magn Reson Imaging 21:1251–1261, 2003.
L. Baccala, and K. Sameshima, “Partial directed coherence: a new concept in neural structure determination,” Biol. Cybern., Vol. 84, pp. 463-474,2001.
Akaike, H. Information theory and an extension of the maximum likelihood principle. 2nd International Symposium on Information Theory, B. N. Petrov and F.Csaki (eds.), Akademiai Kiado, Budapest, 267 – 281, 1973.
Schwarz, G. “Estimating the dimension of a model”. Annals of Statistics, 6, 461–464, 1978.
Mingzhou Ding, Steven L. Bressler, Weiming Yang, Hualou Liang, “Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment”, Biological Cybernetics, Springer-Verlag, vol. 83, pp 35-45, 2000.
S.S. Haykin, “Kalman Filtering and Neural Networks,” Wiley Chichester, 2001.
