Online removal of eye movement and blink artifacts from EEG signals without EOG

In this thesis, two novel methods are presented for online removal of ocular artifacts (OA) from EEG without the need for EOG electrodes attached to the face. Both methods are fully automated and can remove the effects of both eye movements and blinks. The first method employs a high speed eye tracker and three frontal EEG electrodes as a reference to any nonlinear adaptive filter to remove OAs without any calibration. For the filters considered, at some frontal electrodes, using the eye tracker-based reference was shown to significantly (p < .05) improve the ability to remove OAs over using either EOG or only frontal EEG as a reference. Using an eye tracker provides the means for recording point-of-gaze and blink dynamics simultaneously with EEG, which is often desired or required in clinical studies and a variety of human computer interface applications. The second method uses a biophysical model of the head and movement of the eyes to remove OAs. It only requires a short once-per-subject calibration and does not require subject-specific MRI. It was compared to four existing methods, and was shown to perform consistently over a variety of tasks. In removing both saccades and blinks, it removed more than 4 times as much OA as the other methods. In terms of distortion, it was the only method that never removed more power than was present in the original EEG. To carry out the above studies, several related original investigations and developments were needed. These included a novel algorithm to extract the blink time course from eye tracker images, a new measure of OA removal distortion, a high speed eye tracker recording system, a study to determine whether frontal EEG could be used to replace EOG for OA removal and studies of the frequency content of blinks, the effects of an electromagnetic sensor on EEG, and the effects of varying mental states on OA removal methods. In summary this thesis has helped pave the way towards a real-time EEG-based human interface that is free of OAs and does not require EOG electrodes in its operation.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat