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Speaker: Katharina Glomb Description: It is an intuitively understandable idea to view brain activity as a signal that spreads through a network of interconnected brain regions. Mathematically, such a network is also known as a graph, and apart from the brain, many things outside of neuroscience can be considered graphs: traffic networks, social networks, the irregular surfaces of 3-dimensional objects, and many more. Importantly, in this context, there is a distinction to be made between the signal - in the brain, this is neural activity - and the graph on top of which this signal plays out. This point of view makes it possible to apply tools from a well-established framework to neural data, i.e., graph signal processing (GSP). In this lecture, we will take a look at how GSP can be used to decompose, interpret and statistically analyze neural signals on a graph.