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FastBRaIn_at_NECST

Presentation describing the State of the Art of FastBRaIn

Engineering
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Valentina Corbetta: valentina.corbetta@mail.polimi.it Giada Casagrande: giada.casagrande@mail.polimi.it Filippo Carloni: filippo.carloni@mail.polimi.it 16th May 2018 XOHW18 Meeting @NECSTLab State of Art FastBRaIn
ICA The Independent Component Analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. There are different approaches to implement ICA: Infomax maximum likelihood FastICA maximizes nongaussianity JADE joint diagonalization EVD second order correlation 2
Execution time: related to a Pentium 4, 1.6-GHz, 512 GB, Windows XP computer using MATLAB version 7.0. Nicolle Correa, Tu ̈lay Adali, and Vince D Calhoun. Performance of blind source separation algorithms for fMRI analysis using a group ICA method. Magnetic resonance imaging, 25(5):684–94, jun 2007. Algorithms Spatial Maps Accuracy Time Courses Accuracy Parameters Settings Execution Time [s] Infomax 70 FastICA 38 JADE 62 EVD 3 ICA Algorithms 3
4
5 Related Works Execution Time User Friendliness
Related Works 6 User Friendliness Execution Time FSL[1] Execution Time: 20 min [1]https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT
Related Works User Friendliness Execution Time Gift: Execution Time: 8 min 7 FSL[1] Execution Time: 20 min [1]https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT
Related Works User Friendliness Execution Time Gift: Execution Time: 8 min 8 FSL[1] Execution Time: 20 min [1]https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT FastBRaIn
FastBRaIn Thank you for your attention FastBrainatNECST FastBRaIn_NECST Valentina Corbetta: valentina.corbetta@mail.polimi.it Giada Casagrande: giada.casagrande@mail.polimi.it Filippo Carloni: filippo.carloni@mail.polimi.it

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SoA_project

  • 1. Valentina Corbetta: valentina.corbetta@mail.polimi.it Giada Casagrande: giada.casagrande@mail.polimi.it Filippo Carloni: filippo.carloni@mail.polimi.it 16th May 2018 XOHW18 Meeting @NECSTLab State of Art FastBRaIn
  • 2. ICA The Independent Component Analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. There are different approaches to implement ICA: Infomax maximum likelihood FastICA maximizes nongaussianity JADE joint diagonalization EVD second order correlation 2
  • 3. Execution time: related to a Pentium 4, 1.6-GHz, 512 GB, Windows XP computer using MATLAB version 7.0. Nicolle Correa, Tu ̈lay Adali, and Vince D Calhoun. Performance of blind source separation algorithms for fMRI analysis using a group ICA method. Magnetic resonance imaging, 25(5):684–94, jun 2007. Algorithms Spatial Maps Accuracy Time Courses Accuracy Parameters Settings Execution Time [s] Infomax 70 FastICA 38 JADE 62 EVD 3 ICA Algorithms 3
  • 4. 4
  • 6. Related Works 6 User Friendliness Execution Time FSL[1] Execution Time: 20 min [1]https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT
  • 7. Related Works User Friendliness Execution Time Gift: Execution Time: 8 min 7 FSL[1] Execution Time: 20 min [1]https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT
  • 8. Related Works User Friendliness Execution Time Gift: Execution Time: 8 min 8 FSL[1] Execution Time: 20 min [1]https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT FastBRaIn
  • 9. FastBRaIn Thank you for your attention FastBrainatNECST FastBRaIn_NECST Valentina Corbetta: valentina.corbetta@mail.polimi.it Giada Casagrande: giada.casagrande@mail.polimi.it Filippo Carloni: filippo.carloni@mail.polimi.it

Editor's Notes

  1. FastICA  the best overall performance Iterative algorithm Maximize non-Gaussianity of estimated sources Easy to use: requires initialization and setting of a limited number of parameters Independent components estimated individually
  2. FSL is a comprehensive library of analysis tools for fMRI, MRI and DTI. It runs on Apple and PCs Gift is a MATLAB toolbox. It implements different algorithms for ICA applied to the analysis of fMRI