Removing Ocular Artifacts from EEG Signals using Adaptive Filtering and ARMAX Modeling
EEG signal is one of the oldest measures of brain
activity that has been used vastly for clinical diagnoses and
biomedical researches. However, EEG signals are highly
contaminated with various artifacts, both from the subject and from
equipment interferences. Among these various kinds of artifacts,
ocular noise is the most important one. Since many applications such
as BCI require online and real-time processing of EEG signal, it is
ideal if the removal of artifacts is performed in an online fashion.
Recently, some methods for online ocular artifact removing have
been proposed. One of these methods is ARMAX modeling of EEG
signal. This method assumes that the recorded EEG signal is a
combination of EOG artifacts and the background EEG. Then the
background EEG is estimated via estimation of ARMAX parameters.
The other recently proposed method is based on adaptive filtering.
This method uses EOG signal as the reference input and subtracts
EOG artifacts from recorded EEG signals. In this paper we
investigate the efficiency of each method for removing of EOG
artifacts. A comparison is made between these two methods. Our
undertaken conclusion from this comparison is that adaptive filtering
method has better results compared with the results achieved by
ARMAX modeling.
[1] P. He, G. Wilson, & C. Russell, "Removal of ocular artifacts from electroencephalogram
by adaptive filtering", Medical and Biological
Engineering and Computing, vol. 42, pp 407-412, 2004.
[2] Shane M. Haas, Mark G. Frei, Ivan Osorio, Bozenna Pasik- Duncan, &
Jeff Radel, "EEG ocular artifact removal through ARMAX model system
identification using extended least squares", Communications in
Information and Systems, 3, (1), pp 19-40, 2003.
[3] E. Nezhadarya, & M.B. Shamsollahi, "EOG artifact removal from EEG
using ICA and ARMAX modeling", ICBME 2005, Singapore, 2005.
[4] A. Cohen , Biomedical Signal Processing, Volume I and II, Boca Raton,
USA, CRC Press, 1986
[5] S. V. Vaseghi, Advanced signal processing and digital noise reduction,
New York, USA, John Wiley & Sons and B. G. Teubner, 1996
[6] H. Chen & L. Guo, Identification and stochastic adaptive control,
Birkhauser, 1991.
[7] T. P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne, & T.
J. Sejnowski, "Removal of eye activity artifacts from visual event-related
potential in normal and clinical subjects", Clin. Neorophysiol., vol. 11, pp
1745-1758, 2000.
[8] R. Verleger, T. Gasser, & J. Möcks, "Correction of EOG artifacts in eventrelated
potentials of the EEG: Aspects of reliability and validity",
Psychophysiology, vol. 19, pp 472-480, 1982.
[9] J. L. Kenemans, P. C. M. Molenaar, M. N. Verbaten, & J. L. Slangen,
"Removal of the ocular artifacts from the EEG: A comparison of time and
frequency domain methods with simulated and real data",
Psychophysiology,vol. 28, , pp 115-121, 1991.
[10] J. L. Whitton, F. Lue, & H. Moldofsky, "A spectral method for removing
eye movement artifacts from the EEG", Electroenceph. Clin.
Neurophysiol. vol. 44, pp. 735-741, 1978.
[1] P. He, G. Wilson, & C. Russell, "Removal of ocular artifacts from electroencephalogram
by adaptive filtering", Medical and Biological
Engineering and Computing, vol. 42, pp 407-412, 2004.
[2] Shane M. Haas, Mark G. Frei, Ivan Osorio, Bozenna Pasik- Duncan, &
Jeff Radel, "EEG ocular artifact removal through ARMAX model system
identification using extended least squares", Communications in
Information and Systems, 3, (1), pp 19-40, 2003.
[3] E. Nezhadarya, & M.B. Shamsollahi, "EOG artifact removal from EEG
using ICA and ARMAX modeling", ICBME 2005, Singapore, 2005.
[4] A. Cohen , Biomedical Signal Processing, Volume I and II, Boca Raton,
USA, CRC Press, 1986
[5] S. V. Vaseghi, Advanced signal processing and digital noise reduction,
New York, USA, John Wiley & Sons and B. G. Teubner, 1996
[6] H. Chen & L. Guo, Identification and stochastic adaptive control,
Birkhauser, 1991.
[7] T. P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne, & T.
J. Sejnowski, "Removal of eye activity artifacts from visual event-related
potential in normal and clinical subjects", Clin. Neorophysiol., vol. 11, pp
1745-1758, 2000.
[8] R. Verleger, T. Gasser, & J. Möcks, "Correction of EOG artifacts in eventrelated
potentials of the EEG: Aspects of reliability and validity",
Psychophysiology, vol. 19, pp 472-480, 1982.
[9] J. L. Kenemans, P. C. M. Molenaar, M. N. Verbaten, & J. L. Slangen,
"Removal of the ocular artifacts from the EEG: A comparison of time and
frequency domain methods with simulated and real data",
Psychophysiology,vol. 28, , pp 115-121, 1991.
[10] J. L. Whitton, F. Lue, & H. Moldofsky, "A spectral method for removing
eye movement artifacts from the EEG", Electroenceph. Clin.
Neurophysiol. vol. 44, pp. 735-741, 1978.
@article{"International Journal of Medical, Medicine and Health Sciences:61561", author = "Parisa Shooshtari and Gelareh Mohamadi and Behnam Molaee Ardekani and Mohammad Bagher Shamsollahi", title = "Removing Ocular Artifacts from EEG Signals using Adaptive Filtering and ARMAX Modeling", abstract = "EEG signal is one of the oldest measures of brain
activity that has been used vastly for clinical diagnoses and
biomedical researches. However, EEG signals are highly
contaminated with various artifacts, both from the subject and from
equipment interferences. Among these various kinds of artifacts,
ocular noise is the most important one. Since many applications such
as BCI require online and real-time processing of EEG signal, it is
ideal if the removal of artifacts is performed in an online fashion.
Recently, some methods for online ocular artifact removing have
been proposed. One of these methods is ARMAX modeling of EEG
signal. This method assumes that the recorded EEG signal is a
combination of EOG artifacts and the background EEG. Then the
background EEG is estimated via estimation of ARMAX parameters.
The other recently proposed method is based on adaptive filtering.
This method uses EOG signal as the reference input and subtracts
EOG artifacts from recorded EEG signals. In this paper we
investigate the efficiency of each method for removing of EOG
artifacts. A comparison is made between these two methods. Our
undertaken conclusion from this comparison is that adaptive filtering
method has better results compared with the results achieved by
ARMAX modeling.", keywords = "Ocular Artifacts, EEG, Adaptive Filtering,
ARMAX", volume = "1", number = "11", pages = "603-4", }