Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions

• Published in: BMC bioinformatics Vol. 18; no. Suppl 6; p. 236
• Main Authors: Murugesan, Sugeerth, Bouchard, Kristofer, Chang, Edward, Dougherty, Max, Hamann, Bernd, Weber, Gunther H
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 06.06.2017; BioMed Central; BMC
• Subjects: 60 APPLIED LIFE SCIENCES; Algorithms; Brain - diagnostic imaging; Brain - physiopathology; Brain imaging; Cluster Analysis; Clustering; Computational biology; Computational Biology - methods; Electrocorticography; Electrocorticography - methods; Epilepsy; Epilepsy - physiopathology; Graph visualization; Humans; Innovations; Medical research; Mutli-scale analysis; Neuroimaging; Neuroinformatics; Software; Spatio-temporal graphs; Unsupervised learning; Visual analysis; Brain imaging; Mutli-scale analysis; Epilepsy; Neuroinformatics; Visual analysis; Graph visualization; Spatio-temporal graphs; Clustering; Unsupervised learning; Electrocorticography

There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from...