Spectral estimation of nonstationary EEG using particle filtering with application to event-related desynchronization (ERD)IEEE Transactions on Biomedical Engineering (2010)
This paper proposes non-Gaussian models for parametric spectral estimation with application to event-related desynchronization (ERD) estimation of nonstationary EEG. Existing approaches for time-varying spectral estimation use time-varying autoregressive (TVAR) state-space models with Gaussian state noise. The parameter estimation is solved by a conventional Kalman filtering. This study uses non-Gaussian state noise to model autoregressive (AR) parameter variation with estimation by a Monte Carlo particle filter (PF). Use of non-Gaussian noise such as heavy-tailed distribution is motivated by its ability to track abrupt and smooth AR parameter changes, which are inadequately modeled by Gaussian models. Thus, more accurate spectral estimates and better ERD tracking can be obtained. This study further proposes a non-Gaussian state space formulation of time-varying autoregressive moving average (TVARMA) models to improve the spectral estimation. Simulation on TVAR process with abrupt parameter variation shows superior tracking performance of non-Gaussian models. Evaluation on motor-imagery EEG data shows that the non-Gaussian models provide more accurate detection of abrupt changes in alpha rhythm ERD. Among the proposed non-Gaussian models, TVARMA shows better spectral representations while maintaining reasonable good ERD tracking performance.
- Time-varying autoregressive models,
- particle filters,
- event-related desynchronization
Publication DateOctober 21, 2010
Citation InformationChee-Ming Ting, Sh-Hussain Salleh, Z. M. Zainuddin and Arifah Bahar. "Spectral estimation of nonstationary EEG using particle filtering with application to event-related desynchronization (ERD)" IEEE Transactions on Biomedical Engineering Vol. 58 Iss. 2 (2010)
Available at: http://works.bepress.com/chee-ming_ting/4/