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A Tutorial on Graph Theory for Brain Signal Analysis

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Document pages: 19 pages

Abstract: This tutorial paper refers to the use of graph-theoretic concepts foranalyzing brain signals. For didactic purposes it splits into two parts: theoryand application. In the first part, we commence by introducing some basicelements from graph theory and stemming algorithmic tools, which can beemployed for data-analytic purposes. Next, we describe how these concepts areadapted for handling evolving connectivity and gaining insights into networkreorganization. Finally, the notion of signals residing on a given graph isintroduced and elements from the emerging field of graph signal processing(GSP) are provided. The second part serves as a pragmatic demonstration of thetools and techniques described earlier. It is based on analyzing a multi-trialdataset containing single-trial responses from a visual ERP paradigm. The paperends with a brief outline of the most recent trends in graph theory that areabout to shape brain signal processing in the near future and a more generaldiscussion on the relevance of graph-theoretic methodologies for analyzingcontinuous-mode neural recordings.

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