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Structural analysis and application to brain imaging
Corresponding Author(s) : M. Bernard
Cellular and Molecular Biology,
Vol. 53 No. 2: Biomedical signal and image processing - Volume 2
Abstract
Our goal is to organize the ElectroEncephaloGram (EEG) signal so as to describe and image various brain activities. Our work is based on a data structure, a graph, which sums up the brain activity in the spatial, temporal and frequency domains. From the information contained in the time-frequency map of EEG signals, a graph is constructed. In order to analyze the complexity of the signal, our method is based on a multi-scale approach with several levels of information extraction. To compare different EEG signals, we use techniques of graph-matching with our data structure. The developed algorithm is based on the A* algorithm that allows us to compare variations of the recorded EEG in term o f latency, frequency, energy and activated areas. The results of this project show first, that the graph is an appropriate tool to reduce the cortical activity complexity, and second, that graph-matching offers some interesting perspectives in order to describe functional brain activity.
Keywords
EEG
Wavelet
Graph-matching
ERD/ERS.
Bernard, M., Richard, N., & Paquereau, J. (2007). Structural analysis and application to brain imaging. Cellular and Molecular Biology, 53(2), 36–43. Retrieved from http://cellmolbiol.org/index.php/CMB/article/view/1115
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