In this paper we apply common spatial pattern
(CSP) to the classification of electrooculography (EOG) signals
with four distinct classes, namely, eye blinks (EB), eye rotation
clockwise (ERC), vertical eye movement (VEM) and horizontal
eye movement (HEM). We first describe the CSP and Linear
Discriminant Analysis (LDA) algorithms with two classes. We
apply the classification method to a database with 9 subjects
to evaluate the system performance for long trials (15 s) and
short trials (3 s). In both cases the performance was above 86%.
We then generalize the method to the multiclass situation (four
classes). It is shown that 100% accuracy is obtained (in the scope
of the used data set) when the classifier is trained with 8 subjects
and tested with another. Even in the extreme situation, when only
one subject is used to train the classifier, the system can perform
with an accuracy of 84.3%.