Elimination of EOG signals from raw EEG signals using step size based recursive least squares- least mean fourth adaptive algorithm

Abstract A novel Step Size based Recursive least squares- Least mean fourth(SSRL) adaptive algorithm is proposed as a noise canceller, in this paper, to eliminate the Ocular artifacts(OAs) from the recorded raw EEG signals. Here, both second and fourth-order power optimization algorithms have been used to eliminate the OAs from raw EEG signals. The Reference signals, such as vertical Electrooculogram and horizontal Electrooculogram signals, have been recorded separately.FIR filter has been used to process the reference signals by updating filter coefficients using the proposed SSRL algorithm. Thereby, true EEG signals are obtained by subtracting the processed signals from the raw signals. Moreover, a mathematical model for the mean square deviation analysis is proposed for the SSRL algorithm, which provides better results compared to the conventional methods.

[1]  J. G. Harris,et al.  Combined LMS/F algorithm , 1997 .

[2]  Jacob Benesty,et al.  A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification , 2008, IEEE Signal Processing Letters.

[3]  José Carlos M. Bermudez,et al.  An improved statistical analysis of the least mean fourth (LMF) adaptive algorithm , 2003, IEEE Trans. Signal Process..

[4]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[5]  Tareq Y. Al-Naffouri,et al.  Convergence and tracking analysis of a variable normalised LMF (XE-NLMF) algorithm , 2009, Signal Process..

[6]  Rohit Gupta,et al.  Single channel EMG-based continuous terrain identification with simple classifier for lower limb prosthesis , 2019, Biocybernetics and Biomedical Engineering.

[7]  Rachid Latif,et al.  An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform , 2016 .

[8]  Eweda Eweda,et al.  Stochastic Analysis of the LMS and NLMS Algorithms for Cyclostationary White Gaussian Inputs , 2014, IEEE Transactions on Signal Processing.

[9]  Herbert Bauer,et al.  Using ICA for removal of ocular artifacts in EEG recorded from blind subjects , 2005, Neural Networks.

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

[11]  Panagiotis D. Bamidis,et al.  REG-ICA: A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts , 2011, Biomed. Signal Process. Control..

[12]  R N Vigário,et al.  Extraction of ocular artefacts from EEG using independent component analysis. , 1997, Electroencephalography and clinical neurophysiology.

[13]  Saeed Vaseghi.  Advanced Signal Processing and Digital Noise Reduction , 1996 .

[14]  E. C. Ifeachor,et al.  New online method for removing ocular artefacts from EEG signals , 2006, Medical and Biological Engineering and Computing.

[15]  M. Moghavvemi,et al.  Real time ocular and facial muscle artifacts removal from EEG signals using LMS adaptive algorithm , 2007, 2007 International Conference on Intelligent and Advanced Systems.

[16]  H. Moldofsky,et al.  A spectral method for removing eye movement artifacts from the EEG. , 1978, Electroencephalography and clinical neurophysiology.

[17]  Abolfazl Mehbodniya,et al.  Sparse LMS/F algorithms with application to adaptive system identification , 2015, Wirel. Commun. Mob. Comput..

[18]  Wei Xing Zheng,et al.  A new combined-step-size normalized least mean square algorithm for cyclostationary inputs , 2017, Signal Process..

[19]  P. Berg,et al.  The correction of ocular artifacts: a topographic perspective , 2000, Clinical Neurophysiology.

[20]  C. Joyce,et al.  Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. , 2004, Psychophysiology.

[21]  G. Wilson,et al.  Removal of ocular artifacts from electro-encephalogram by adaptive filtering , 2004, Medical and Biological Engineering and Computing.

[22]  Amit Kumar Kohli,et al.  Excision of Ocular Artifacts from EEG Using NVFF-RLS Adaptive Algorithm , 2017, Circuits Syst. Signal Process..

[23]  R. Homan,et al.  Cerebral location of international 10-20 system electrode placement. , 1987, Electroencephalography and clinical neurophysiology.

[24]  Zhenyu Wang,et al.  Robust removal of ocular artifacts by combining Independent Component Analysis and system identification , 2014, Biomed. Signal Process. Control..

[25]  J. C. Woestenburg,et al.  The removal of the eye-movement artifact from the EEG by regression analysis in the frequency domain , 1983, Biological Psychology.

[26]  Bernard Widrow,et al.  The least mean fourth (LMF) adaptive algorithm and its family , 1984, IEEE Trans. Inf. Theory.

[27]  Azzedine Zerguine.  Convergence and steady-state analysis of the normalized least mean fourth algorithm , 2007, Digit. Signal Process..