Thesis supervisor: Zoltán Juhász
co-supervisor: György Kozmann
Location of studies (in Hungarian): University of Pannonia, H-8200 Veszprém, Egyetem u. 10. Hungary Abbreviation of location of studies: PE
Description of the research topic:
Electroencephalography (EEG) measures the electrical activity of our brain using scalp electrodes. Current EEG technology uses electrode counts in the range of 19 (clinical practice) to 256 (research labs). Increasing the sampling frequency and the electrode count does not result in automatic improvement in temporal and spatial resolution. The goal of this research is to investigate advanced signal processing methods for improving the imaging capabilities of EEG technology and to understand the characteristics of resolution limiting constraints. Methods such as Independent Component Analysis, Empirical Mode Decomposition, Continuous Wavelet Transform, Surface Laplacian, Karhunen Loeve transformation will be used as starting points but the aim of the research is to improve existing methods or create novel ones for high-resolution EEG imaging and feature extraction.
Papers related to the topic:
1. Michel, C.M. and Murray, M.M., 2012. Towards the utilization of EEG as a brain imaging tool. Neuroimage, 61(2), pp.371-385.
2. Nunez, P. L., R. B. Silberstein, P. J. Cadusch, R. S. Wijesinghe, A. F. Westdorp, and R. Srinivasan. "A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging." Electroencephalography and clinical neurophysiology 90, no. 1 (1994): 40-57.
Required language skills: english Further requirements: Besides a solid foundation in computer science, the applicant should be fluent in English, and have a strong knowledge of advanced mathematics, physics, signal processing methods as well as an interest in biology and neuroscience.
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