Functional magnetic resonance imaging has been recently the most common tool for examining the neural activity in human and animals. The goal of a typical data-mining challenge is the localisation of brain areas activated during a cognitive task which is usually performed using a linear model or correlation methods. For this purpose several authors have proposed the use of methods evaluating signal complexity which could possibly overcome some of the shortcomings of the standards methods due to their independence on a priori knowledge of data characteristics. This work explains possibilities of using such methods including aspects of their configuration and it proposes an evaluation of performance of the methods applied on simulated data following expected biological characteristics. The results of the evaluation of performance showed little advantage of these methods over the standard ones in cases when the standard methods were possible to apply. However, some of the methods evaluating signal complexity were found useful for determining the regularity of signals which is a feature that cannot be assessed by the standard methods. Optimal parameters of the methods evaluating signal regularity were determined on simulated data and finally the methods were applied on the data examining emotional processing of...
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:304114 |
| Date | January 2012 |
| Creators | Vyhnánek, Jan |
| Contributors | Boldyš, Jiří, Dvořák, Jiří |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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