Open Access. Powered by Scholars. Published by Universities.®
Biomedical Engineering and Bioengineering Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Biomedical Engineering and Bioengineering
Spatio-Temporal Progression Of Cortical Activity Related To Continuous Overt And Covert Speech Production In A Reading Task, Jonathan S. Brumberg, Dean J. Krusienski, Shreya Chakrabarti, Aysegul Gunduz, Peter Brunner, Anthony L. Ritaccio, Gerwin Schalk
Spatio-Temporal Progression Of Cortical Activity Related To Continuous Overt And Covert Speech Production In A Reading Task, Jonathan S. Brumberg, Dean J. Krusienski, Shreya Chakrabarti, Aysegul Gunduz, Peter Brunner, Anthony L. Ritaccio, Gerwin Schalk
Electrical & Computer Engineering Faculty Publications
How the human brain plans, executes, and monitors continuous and fluent speech has remained largely elusive. For example, previous research has defined the cortical locations most important for different aspects of speech function, but has not yet yielded a definition of the temporal progression of involvement of those locations as speech progresses either overtly or covertly. In this paper, we uncovered the spatio-temporal evolution of neuronal population-level activity related to continuous overt speech, and identified those locations that shared activity characteristics across overt and covert speech. Specifically, we asked subjects to repeat continuous sentences aloud or silently while we recorded …
A Comparative Study Of Two Prediction Models For Brain Tumor Progression, Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)
A Comparative Study Of Two Prediction Models For Brain Tumor Progression, Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)
Electrical & Computer Engineering Faculty Publications
MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images.
We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. …