Adaptive Spectral Estimation of Non-stationary Biomedical Signals Based on Autoregressive Modeling and Kalman Filtering
[1]
Abdelhaq Ouelli, Laboratoire de Développement Durable, University Sultan Moulay Slimane, Beni Mellal, Morocco.
[2]
Benachir Elhadadi, Laboratoire de Développement Durable, University Sultan Moulay Slimane, Beni Mellal, Morocco.
[3]
Hicham Aissaoui, Laboratoire de Développement Durable, University Sultan Moulay Slimane, Beni Mellal, Morocco.
[4]
Belaid Bouikhalene, Laboratoire de Développement Durable, University Sultan Moulay Slimane, Beni Mellal, Morocco.
In this paper, we present an adaptive spectrum estimation method for non-stationary Biomedical Signals. The algorithm is based on time-varying autoregressive (TVAR) modeling where the time varying parameters are estimated by Kalman filtering. The algorithm generates adaptively an estimate of the power spectral density (PSD) at each time instant. A comparison was made with the recursive least squares (RLS) method, the main feature of the proposed approach is the capability of the Kalman filter that enables tracking smooth and sharp changes in the time varying process parameters. Furthermore, it provides better time-frequency resolution and gives a good spectral peak matching. Simulation studies and applications on real EEG data show that the proposed algorithm can provide important transient information on the inherent dynamics of non-stationary biomedical processes.
Brain-Computer Interface (BCI), Motor Imagery, Kalman Filtering, Autoregressive Model, Event Related Desynchronization (ERD)
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