Study of Kalman filter based Noise Estimation in Artifactual EEG and their Quantification

This paper presents an approach to estimate the noise in electroencephalographic (EEG) recordings. The approach is based on the Kalman filter technique. The parameters of noise signals are estimated using the Kalman filter. The idea behind this approach is to denoise the EEG signals and to preserve the stationarity by considering different window sized EEG time series. The results are validated in different window sizes of 300 milliseconds, 1 minute, 2 minutes, and 3 minutes up to 4 minutes data in different EEG. The entropy of the original signal and the estimated signal is also calculated, which shows the content of the signal. The Mean Square Error (MSE) and Signal to Noise ratio (SNR) in different channels are calculated for different window size EEG data. From the obtained results, we observe that with an increase in window size data, the MSE decreases and SNR increases and the entropy of the estimated signal is almost same as that of the original signal. Depending on the requirement, the window size is chosen. The smaller window gives better visibility, and the larger window gives less error. Estimation of noise quantifies the different types of inband artifacts contaminated the EEG signal.

[1]  Lourens J. Waldorp,et al.  Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model , 2002, IEEE Transactions on Biomedical Engineering.

[2]  Stergios I. Roumeliotis,et al.  Bayesian estimation and Kalman filtering: a unified framework for mobile robot localization , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[3]  Stephen J. Roberts,et al.  ARTEFACT DETECTION IN SLEEP EEG BY THE USE OF KALMAN FILTERING , 2007 .

[4]  Qiang Li,et al.  Kalman Filter and Its Application , 2015, 2015 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS).

[5]  Joris De Schutter,et al.  Adaptive Kalman filter for noise identification , 2000 .

[6]  Aurobinda Routray,et al.  A robust motifs based artifacts removal technique from EEG , 2017 .

[7]  Sarp Ertürk,et al.  Real-Time Digital Image Stabilization Using Kalman Filters , 2002, Real Time Imaging.

[8]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[9]  Uwe Kiencke,et al.  Automotive Control Systems: For Engine, Driveline, and Vehicle , 2000 .

[10]  Ali H. Shoeb,et al.  Application of machine learning to epileptic seizure onset detection and treatment , 2009 .

[11]  Aurobinda Routray,et al.  Generalised Orthogonal Partial Directed Coherence as a Measure of Neural Information Flow During Meditation , 2015 .

[12]  Sophie Keller.  Automotive Control Systems For Engine Driveline And Vehicle , 2016 .

[13]  Ehud Weinstein,et al.  Iterative and sequential Kalman filter-based speech enhancement algorithms , 1998, IEEE Trans. Speech Audio Process..

[14]  D. Salmond,et al.  Target tracking: introduction and Kalman tracking filters , 2001 .

[15]  J Gotman.  Computer analysis during intensive monitoring of epileptic patients. , 1987, Advances in neurology.

[16]  G. Strang,et al.  Linear Algebra, Geodesy, and GPS , 1997 .

[17]  John B. Pearson,et al.  Kalman Filter Applications in Airborne Radar Tracking , 1974, IEEE Transactions on Aerospace and Electronic Systems.

[18]  P. Hazemann,et al.  Handbook of Electroencephalography and Clinical Neurophysiology , 1975 .

[19]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..