Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data

• Published in: IEEE/ACM transactions on computational biology and bioinformatics
• Main Authors: Murugesan, Sugeerth, Bouchard, Kristofer, Chang, Edward, Dougherty, Max, Hamann, Bernd, Weber, Gunther H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 02.10.2016
• Subjects: 60 APPLIED LIFE SCIENCES; Brain Imaging; Electrocorticography; Graph Visualization; Linked Views; MATHEMATICS AND COMPUTING; Neuroinformatics
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1145/2975167.2985688

Here, we present ECoG ClusterFlow, a novel interactive visual analysis tool for the exploration of high-resolution Electrocorticography (ECoG) data. Our system detects and visualizes dynamic high-level structures, such as communities, using the time-varying spatial connectivity network derived from the high-resolution ECoG data. ECoG ClusterFlow provides a multi-scale visualization of the spatio-temporal patterns underlying the time-varying communities using two views: 1) an overview summarizing the evolution of clusters over time and 2) a hierarchical glyph-based technique that uses data aggregation and small multiples techniques to visualize the propagation of clusters in their spatial domain. ECoG ClusterFlow makes it possible 1) to compare the spatio-temporal evolution patterns across various time intervals, 2) to compare the temporal information at varying levels of granularity, and 3) to investigate the evolution of spatial patterns without occluding the spatial context information. Lastly, we present case studies done in collaboration with neuroscientists on our team for both simulated and real epileptic seizure data aimed at evaluating the effectiveness of our approach.