PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Automatic removal of ocular artifacts using adaptive filtering and Independent Component Analysis for EEG data
C. Guerrero-Mosquera and Angel Navia-Vazquez
IET Signal Processing (accepted, pending publication) 2012.

Abstract

A new method for eye movement artifacts removal based on Independent Component Analysis (ICA) and Recursive Least Squares (RLS) is presented. The proposed algorithm combines the effective ICA capacity of separating artifacts from brain waves, together with the online interference cancellation achieved by adaptive filtering. Eye blink, saccades, eyes opening and closing produce changes of potentials at frontal areas. For this reason, the method uses as a reference electrodes closest to the eyes Fp1, Fp2, F7 and F8, which register vertical and horizontal eye movements in the electroencephalogram (EEG) caused by these activities as an alternative of using extra dedicated EOG electrodes, that could not always be available and could be subject to larger variability. Both reference signals and EEG components are first projected into ICA domain and then the interference is estimated using the RLS algorithm. The component related to EOG artifact is automatically eliminated using channel localizations. Results from experimental data demonstrate that this approach is suitable for eliminating artifacts caused by eye movements, and the principles of this method can be extended to certain other artifacts as well, whenever a correlated reference signal is available.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:9080
Deposited By:Angel Navia-Vazquez
Deposited On:21 February 2012