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A0557
Title: Spectral topological data analysis for EEG brain signals Authors:  Anass El Yaagoubi Bourakna - King Abdullah University of Science and Technology (Saudi Arabia)
Moo K Chung - University of Wisconsin-Madison (United States)
Shuhao Jiao - City University of Hong Kong (Hong Kong)
Hernando Ombao - KAUST (Saudi Arabia) [presenting]
Abstract: Topological data analysis has become a powerful approach over the last twenty years, mainly because it captures the shape and geometry inherent in the data. Specifically, the use of persistence homology for analyzing functional brain connectivity has witnessed considerable success in the literature. It solves the problem of connectivity matrix thresholding at arbitrary levels by considering a filtration of the weighted network across all possible threshold values. Such approaches for analyzing the topological structure of functional brain connectivity rely on simple connectivity measures such as Pearson correlation. To overcome this limitation, a frequency-specific approach that leverages coherence is proposed to assess the brain's functional connectivity, leading to a novel topological summary, the spectral landscape, which is an extension of the persistence landscape. Using this novel approach to analyze the EEG brain connectivity of ADHD subjects, frequency-specific differences in the topology of brain connectivity between healthy controls and ADHD subjects are shed light.