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- W4322731630 abstract "In the latest years, several approaches for cognitive analysis were developed. One of those approaches addresses the perspective regarding the brain as a network. This paper tries to prove the utility of brain analysis using functional networks. For this experiment, we considered signals provided by DEAP dataset, on which we applied frequency filters and statistical methods for obtaining a graph-based model of the brain, for every subject and trial. A real challenge was the choice of the threshold that differentiates the relevance of the functional links between groups of neurons. Furthermore, in the attempt to prove the effectiveness of the considered method, the graph metrics were computed (average degree, betweenness centrality, global efficiency, cluster coefficients, etc.) and used for emotional state detection and classification. For the same classification problem, the classic features extracted from the brain waves (power spectrum, spectral entropy, Hjorth parameters, etc.) were considered. A comparison between the accuracy obtained from both classifiers is performed for the sole purpose of enforcing the utility of functional network driven methods in further analyses. Finally, by adding the graph metrics to the standard ones, better accuracy of the classification was obtained." @default.
- W4322731630 created "2023-03-03" @default.
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- W4322731630 creator A5043812873 @default.
- W4322731630 date "2022-09-22" @default.
- W4322731630 modified "2023-09-25" @default.
- W4322731630 title "Analysis of Brain Activity Using Functional Networks" @default.
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- W4322731630 doi "https://doi.org/10.1109/iccp56966.2022.10053966" @default.
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