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Summary: THESIS
SIGNAL FRACTION ANALYSIS AND ARTIFACT REMOVAL IN EEG
Submitted by
James N. Knight
Department of Computer Science
In partial fulfillment of the requirements
for the Degree of Master of Science
Colorado State University
Fort Collins, Colorado
Fall 2003
ABSTRACT OF THESIS
SIGNAL FRACTION ANALYSIS AND ARTIFACT REMOVAL IN EEG
The presence of artifacts, such as eye blinks, in electroencephalographic (EEG) recordings obscures
the underlying processes and makes analysis difficult. Large amounts of data must often be discarded
because of contamination by eye blinks, muscle activity, line noise, and pulse signals. To overcome
this difficulty, signal separation techniques are used to separate artifacts from the EEG data of
interest. The maximum signal fraction (MSF) transformation is introduced as an alternative to the
two most common techniques: principal component analysis (PCA) and independent component
analysis (ICA). A signal separation method based on canonical correlation analysis (CCA) is also
considered. The method of delays is introduced as a technique for dealing with non-instantaneous
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