Structural analysis and application to brain imaging.
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.
Richard
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N
Laboratoire Signal-Image-Communications, Futuroscope Cedex, France. bernard@sic.univ-poitiers.fr
Bernard
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Paquereau
J
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D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D003071:Cognition / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D005625:Frontal Lobe / Q000502:physiology*; D006801:Humans; D008875:Middle Aged; D017711:Nonlinear Dynamics