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- W4381302915 abstract "Brain connectivity network (BCN) represents an advanced approach for brain functionality analysis. In BCN, the human brain is modeled as a graph, containing vertices and connectivity links. The electroencephalogram (EEG) signals recorded from the diseased as well as healthy subjects are used to construct a BCN. The graph matrices are a quantitative approach for comparing the network of diseased and healthy subjects. The selection of graph matrices to be extracted from the network depends on whether all the networks have the same or a different number of nodes. In this paper, analysis of matrices dependent to numbers of nodes (DNN) and matrices independent to numbers of nodes (INN) has been performed. These matrices are calculated as features from schizophrenia disease EEG datasets having 16 and 19 channels. Analysis of these matrices has been done based on distance and similarity measures. From the results, it is observed that the INN features show less variability as compared to DNN." @default.
- W4381302915 created "2023-06-21" @default.
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- W4381302915 date "2023-01-01" @default.
- W4381302915 modified "2023-09-23" @default.
- W4381302915 title "Comparative Analysis of Node-Dependent and Node-Independent Graph Matrices for Brain Connectivity Network" @default.
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- W4381302915 doi "https://doi.org/10.1007/978-981-99-0085-5_2" @default.
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