In this thesis, we consider a stochastic SIRS model of EEG data. The model is built over three different network structures: a random network, a scale-free network, and a small-world network. These models are then fit to an EEG signal from a control individual and an EEG signal from an individual experiencing an epileptic seizure. We are interested in determining whether these models can distinguish between the two data sets, and whether any of the network structures offer a significantly better fit to the data than others; there is also a broader interest in the effects of different network structures on the time series characteristics of an SIRS system. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/22173 |
| Date | 08 1900 |
| Creators | Mitchell, Evan |
| Contributors | Bolker, Benjamin, Mathematics and Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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