The electrical dipoles of eyes change by eye movements and blinks, producin
g a signal known as an electrooculogram (EOG). A fraction of EOGs contamina
te the electrical activity of the blain (electroencephalogram, EEG). Ocular
artefact (OA) is a collective term used to represent EEG contaminating pot
entials caused by eye movements and blinks. A procedure for quantifying the
effectiveness of an algorithm for removing OA from the EEG was devised. Th
is enabled the similarity between the EEG waveforms before contamination by
OA and the contaminated EEG waveforms following their processing by an OA
removal method to be measured. Four methods for OA removal were included in
the study: extended independent component analysis (ICA)? joint approximat
ion diagonalisation of eigenmatrices (JADE), principal component analysis (
PCA) and EOG subtraction. The operation of JADE and ICA is subject to ampli
tude scaring and channel permutation. Procedures were incorporated to estim
ate the amplitude of the recovered EEG waveforms and to allocate them to th
e correct channels. It was demonstrated that the signal separation techniqu
es of JADE and extended ICA were more effective than EOG subtraction and PC
A for removing OA from the EEG. EOG subtraction was shown to cause attenuat
ion of the recovered EEG waveforms. The effect of additive Gaussian noise o
n the performance of the four OA removal methods was also investigated This
indicated that the performance of the methods was unaffected by an additiv
e Gaussian noise source, as long as the signal-to-noise ratio remained abov
e 50.