EEG ocular artifact removal through ARMAX model system identification using extended least squares

The removal of ocular artifact from scalp electroencephalograms (EEGs) is of con- siderable importance for both the automated and visual analysis of underlying brainwave activity. Traditionally, subtraction techniques use linear regression to estimate the influence of eye movements on the electrodes of interest. These methods are based on the assumption that the underlying brain- wave activity is uncorrelated when, in general, it is not. Furthermore, regression methods assume that the ocular artifact propagation is frequency independent, i.e. all waveforms of the ocular artifact propagate similarly. In this paper, we examine relaxing these assumptions by using a more general autoregressive (AR) moving average (MA) exogenous (X) model and the extended least squares (ELS) algorithm to remove ocular artifact. We demonstrate that in some cases this general ARMAX model can decrease ocular artifact not removable by standard regression techniques. We also show that the incorporation of a forgetting factor to exponentially weight past data can improve ocular artifact removal even for the traditional subtraction method.

[1]  Richard F. Gunst,et al.  Applied Regression Analysis , 1999, Technometrics.

[2]  Han-Fu Chen,et al.  Convergence rate of least-squares identification and adaptive control for stochastic systems† , 1986 .

[3]  E. J. Hannan,et al.  Linear estimation of ARMA processes , 1983, Autom..

[4]  T. Gasser,et al.  Correction of EOG artifacts in event-related potentials of the EEG: aspects of reliability and validity. , 1982, Psychophysiology.

[5]  E. Hannan,et al.  Recursive estimation of mixed autoregressive-moving average order , 1982 .

[6]  P K Sadasivan,et al.  ANC schemes for the enhancement of EEG signals in the presence of EOG artifacts. , 1996, Computers and biomedical research, an international journal.

[7]  Lei Guo.  Self-convergence of weighted least-squares with applications to stochastic adaptive control , 1996, IEEE Trans. Autom. Control..

[8]  Han-Fu Chen,et al.  Identification and Stochastic Adaptive Control , 1991 .

[9]  Shane M. Haas.  Decision Weighted Adaptive Algorithms with Applications to Wireless Channel Estimation by , 1999 .

[10]  Joachim Mocks,et al.  Correcting ocular artifacts in the EEG: A comparison of several methods , 1989 .

[11]  David Q. Mayne,et al.  Linear identification of ARMA processes , 1982, Autom..

[12]  John H. Busser,et al.  Principles of Applied Biomedical Instrumentation , 1968 .

[13]  Petr Mandl,et al.  On the consistency of a least squares identification procedure , 1992, Kybernetika.

[14]  Steven A. Hillyard,et al.  Chapter 8 – Methodological Issues in CNV Research , 1974 .

[15]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[16]  Emmanuel Chigozie Ifeachor.  INVESTIGATION OF OCULAR ARTEFACTS IN THE HUMAN EEG AND THEIR REMOVAL BY A MICROPROCESSOR-BASED INSTRUMENT , 1984 .

[17]  R. Hartley.  Stochastic Modelling and Control , 1985 .

[18]  Pravin Varaiya,et al.  Stochastic Systems: Estimation, Identification, and Adaptive Control , 1986 .

[19]  B. Bercu.  Weighted estimation and tracking for ARMAX models , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[20]  E Donchin,et al.  A new method for off-line removal of ocular artifact. , 1983, Electroencephalography and clinical neurophysiology.

[21]  Emmanuel Ifeachor,et al.  Digital Signal Processing: A Practical Approach , 1993 .

[22]  Lopes Da Silva Fh,et al.  Analysis of EEG non-stationarities. , 1978 .

[23]  B. Rockstroh,et al.  Removal of ocular artifacts from the EEG--a biophysical approach to the EOG. , 1985, Electroencephalography and clinical neurophysiology.

[24]  M. J. Nichols,et al.  The assessment of two methods for removing eye movement artefact from the EEG. , 1985, Electroencephalography and clinical neurophysiology.

[25]  Han-fu Chʿen.  Recursive estimation and control for stochastic systems , 1985 .

[26]  Xuan Kong,et al.  Adaptive Signal Processing Algorithms: Stability and Performance , 1994 .

[27]  B. Widrow,et al.  The complex LMS algorithm , 1975, Proceedings of the IEEE.

[28]  Emanuel Donchin.  Methodological issues in CNV research , 1973 .