Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm

IEEE Workshop on Statistical Signal Processing Proceedings

Volumn , Issue , 2014, Pages 181-184

  Samdin, S Balqis ^a   Ting, Chee Ming ^a   Salleh, Sh Hussain ^a   Hamedi, Mahyar ^a   Noor, A B Mohd ^a  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

dynamic cortical connectivity; EEG; EM algorithm; Multivariate autoregressive model; state space model

Indexed keywords

ELECTROENCEPHALOGRAPHY; SIGNAL PROCESSING;

DIRECTED TRANSFER FUNCTIONS; EM ALGORITHMS; EXPECTATION-MAXIMIZATION ALGORITHMS; MULTIVARIATE AUTOREGRESSIVE; MULTIVARIATE AUTOREGRESSIVE MODELS; PARTIAL DIRECTED COHERENCE; STATE-SPACE FRAMEWORK; STATE-SPACE MODELING;

ALGORITHMS;

EID: 84907388577     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSP.2014.6884605     Document Type: Conference Paper

References (15)

1. S. M. Smith et. al., "Network modelling methods for FMRI," Neuroimage, vol. 54, pp. 875-891, 2011.
2. B. L. Cheung, B. A. Riedner, G. Tanoni, B. D. V. Veen, "Estimation of cortical connectivity from EEG using state-space models," IEEE Trans. Biomed. Eng., vol. 57, no. 9, pp. 2122-2133, 2010.
3. M. Billinger, C. Brunner and G. Muller-Putz, "Single-trial connectivity estimation for classification of motor-imagery data," J. Neural Eng., vol. 10, pp. 1-8, 2013.
4. R. M. Hutchison et. al., "Dynamic functional connectivity: Promise, issues, and interpretations," Neuroimage, vol. 80, pp. 360-378, 2013.
5. C.-M. Ting et. al., "Spectral estimation of non-stationary EEG using particle filtering with application to event-related desynchronization (ERD)," IEEE Trans. Biomed. Eng., vol. 58, no. 9, pp. 321-331, 2011.
6. W. Hesse, E. Moller, M. Arnold and B. Schack, "The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies," J. of Neuroscience Methods, vol. 124, pp. 27-44, 2003.
7. L. Astolfi, et. al., "Tracking the time-varying cortical connectiivty patterns by adaptive multivariate estimators," IEEE Trans. Biomed. Eng., vol. 55, no. 3, pp. 902-913, 2008.
8. A. Omidvarnia, M. Mesbah, J. M. O'Toole, P. Colditz and B. Boashash, "Analysis of the time-varying cortical neural connectivity in the newborn EEG: A timefrequency approach," presented at 7th Int. Workshop on Systems, Signal Process. and their Applications (WOSSPA), Tipaza, 2011.
9. A. Omidvarnia, G. Azemi, B. Boashash, J. M. O'Toole, P. Colditz and S. Vanhatalo, "Measuring time-varying information flow for scalp EEG signals: orthogonalized partial directed coherence," IEEE Trans. Biomed. Eng., vol. 61, no. 3, pp. 680-693, 2013.
10. L. Faes and G. Nollo, "Extended causal modeling to asseses partial directed coherence in multiple time series with significant instantaneous interactions," Biological Cybern., vol. 103, pp. 387-400, 2010.
11. G. Nolte, O. Bai, L. Wheaton, Z. Mari, S. Vorbach and M. Hallett, "Identifying true brain interaction from EEG data using the imagery part of coherency," Clinical Neurophysiology, vol. 115, pp. 2292-2307, 2004.
12. M. Arnold, W. H. R. Miltner, H. Witte, R. Bauer, and C. Braun, "Adaptive AR modeling of nonstationary time series by means of Kalman filtering," IEEE Trans. Biomed. Eng., vol. 45, no. 5, pp. 553-562, May 1998.
13. th Annu. Int. Conf. IEEE Eng. Med. Bio. Soc, San Diego, 2012.
14. R. H. Shumway and D. S. Stoffer, "An approach to time-series smoothing and forecasting using the EM algorithm," J. Time Series Analysis, vol. 3, no. 4, 1982.
15. B.-N. Vo B.-T. Vo and R. P. S. Mahler "Closed-form solutions to forward-backward smoothing," IEEE Trans. Signal Process., vol. 60, no. 1, pp. 2-17, 2012.