Abstract
Blind source separation (BSS) using independent component based analysis (e.g., probabilistic ICA and infomax ICA) have been studied in-depth to extract common hemodynamic sources for a group of functional magnetic resonance images (fMRI). The inherent assumption here is that the sources must be non-Gaussian. For most of the real world data, the decomposition is non-unique. Furthermore, there is no quantitative way to determine the component(s) of interest common for the group. This paper shows that using a novel constrained Parallel Factor Analysis (PARAFAC)-based tensor decomposition, one can extract the common task signals and spatial maps from a group of noisy fMRI as rank-1 tensors. The extracted hemodynamic signals have very high correlation with ideal hemodynamic response. A quantitative algorithm to extract common components for a group of subjects is also presented. The modified decomposition preserves the uniqueness under mild conditions which is the most attractive feature for any PARAFAC-based tensor decomposition approach.
Original language | English (US) |
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Title of host publication | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1113-1117 |
Number of pages | 5 |
ISBN (Electronic) | 9781509041176 |
DOIs | |
State | Published - Jun 16 2017 |
Event | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States Duration: Mar 5 2017 → Mar 9 2017 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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ISSN (Print) | 1520-6149 |
Other
Other | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 |
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Country/Territory | United States |
City | New Orleans |
Period | 3/5/17 → 3/9/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- PARAFAC
- fMRI
- spatial map
- task signal
- tensor decomposition