A Review Technique for Graph Theoretical Approach Applied for EEG Features Identification
Journal of Basic and Applied Engineering Research Print
In recent decades it has been a flow of analysis of brain model in terms of network Technologies. This review is basically concerned with the analyzing the functional Connectivity network of brain with the help of various images like EEG or MEG etc. Graph Theoretical approaches are in current trends to find out principle of brain network. It is a mathematical representation of a network by establishing a relationship among Vertices (electrodes) and edges (relation between electrodes). We have
... ctrodes). We have implemented algorithms of graph theory on MATLAB and done comparative analysis. We have compared algorithms for efficiency for shortest path computation, clustering coefficient, motifs, sub graphs, path length and efficiency. These properties are used to identify information processing, propagation of information over paths in network, occurrence and existence of neural diseases and cognitive abilities on different brain connectivity levels. This approach is applicable for ten to hundreds of nodes because the graph parameter estimates differ as the network size changes. Also accordingly association matrix is created by assigning pair wise associations between nodes and a threshold value is applied to each element of the matrix to generate an adjacency matrix. According to the relationship between nodes, matrix can be weighted or un-weighted. Weighted graphs have more information than un-weighted. Hence the ultimate goal is to demonstrate the possible applications of graph theoretical approaches in the analyses of brain functional connectivity networks from Electroencephalography (EEG) signals.