Date of Award

2013

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical Engineering

First Advisor

Walter G. Besio

Abstract

The electroencephalogram (EEG) is broadly used for research of brain activities and diagnosis of brain diseases and disorders. Although the EEG provides good temporal resolution, millisecond or less, it does not provide very good spatial resolution. There are two main reasons for the poor spatial resolution, (1) the blurring effects of the head volume conductor, and (2) poor signal to noise ratio. The surface Laplacian of the potential distribution was found to increase the spatial resolution. Several potential interpolation based methods were previously developed to estimate the surface Laplacian. However, these methods are generally complicated in terms of computation, which limits their real-time applications. Previously a special electrode, the tripolar concentric ring electrode (TCRE), was developed and proven to be a much simpler approach to estimate the surface Laplacian while achieving significantly better signal to noise ratio and approximation to the surface Laplacian. In the first part of the dissertation work, computer simulations comparing spatial resolution between conventional EEG disc electrode sensors and TCRE Laplacian sensors were performed. For verification of the computer simulations visual evoked stimulus experiments were performed to acquire visual evoked potentials (VEPs) from healthy human subjects. Analysis of the computer simulation results shows that the TCRE Laplacian sensors can provide approximately a ten-fold improvement in spatial resolution and pass signals from specific volumes. Placing TCRE sensors near the brain region of interest should allow passage of the wanted signals and reject distant interference signals. It was also shown that the TCRE VEPs appeared to separate sources better than disc electrode VEPs. In the second part, a tripolar EEG based automatic seizure detection algorithm was developed for rats, the paramters of the detector was optimized based on the recorded data. According to this algorithm, a Matlab based real-time detector was implimented and tested. In the last part of the dissertation, a prototype of FPGA based automatic seizure detector was Described, which has the ability to detect signal from much more channels real-timely. An multi-channel EEG monitor system was also described.

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