Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data
Abstract
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.
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Univ. of California, San Francisco, CA (United States)
- Univ. of California, Davis, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- OSTI Identifier:
- 1377525
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Additional Journal Information:
- Conference: BCB '16 Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Seattle, WA (United States), 2-5 Oct 2016; Journal ID: ISSN 1545-5963
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 60 APPLIED LIFE SCIENCES; Linked Views; Neuroinformatics; Brain Imaging; Electrocorticography; Graph Visualization
Citation Formats
Murugesan, Sugeerth, Bouchard, Kristofer, Chang, Edward, Dougherty, Max, Hamann, Bernd, and Weber, Gunther H. Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data. United States: N. p., 2016.
Web. doi:10.1145/2975167.2985688.
Murugesan, Sugeerth, Bouchard, Kristofer, Chang, Edward, Dougherty, Max, Hamann, Bernd, & Weber, Gunther H. Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data. United States. https://doi.org/10.1145/2975167.2985688
Murugesan, Sugeerth, Bouchard, Kristofer, Chang, Edward, Dougherty, Max, Hamann, Bernd, and Weber, Gunther H. Sun .
"Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data". United States. https://doi.org/10.1145/2975167.2985688. https://www.osti.gov/servlets/purl/1377525.
@article{osti_1377525,
title = {Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data},
author = {Murugesan, Sugeerth and Bouchard, Kristofer and Chang, Edward and Dougherty, Max and Hamann, Bernd and Weber, Gunther H.},
abstractNote = {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.},
doi = {10.1145/2975167.2985688},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
number = ,
volume = ,
place = {United States},
year = {Sun Oct 02 00:00:00 EDT 2016},
month = {Sun Oct 02 00:00:00 EDT 2016}
}
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