Signal Fraction Analysis and Artifact Removal in Eeg

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 mixing of brain and artifact source signals. The signal separation methods are compared on a series of tests constructed from artificially generated data. A novel method of comparison based on the classification of mental task data for a brain-computer interface (BCI) is also pursued. The results show that the MSF transformation is an effective technique for removing artifacts from EEG recordings. The performance of the MSF approach is comparable with ICA, the current state of the art, and is faster to compute. It is also demonstrated that certain artifacts can be removed from EEG data without negatively impacting the classification of mental tasks. James N. Knight Department of Computer Science Colorado State University Fort Collins, Colorado 80523 Fall 2003