Title :
Comparison of independent component analysis algorithms for EEG-fMRI data fusion
Author :
Youssofzadeh, Vahab ; Faye, Ibrahima ; Malik, Aamir Saeed ; Reza, Faruque ; Kamel, Nidal ; Abdullah, Jafri Malin
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. Teknol. PETRONAS, Tronoh, Malaysia
Abstract :
Fusion of EEG and fMRI data helps researchers to provide a more comprehensive understanding of neural basis for the functional behavior in human brain. EEG and fMRI Joint analysis for cognitive tasks indicates plausible results to obtain a better spatiotemporal resolution of event related responses in the brain. Joint-ICA as a multivariate data analysis method, assumes more than two features type (modalities) have common mixing data and it tries to maximizes independency among joint components. Here, we study the performance of five ICA algorithms when applied to joint analysis of EEG/fMRI data. We use the visualization and computational tools to quantitatively analyze the performance of different ICA algorithms for EEG/fMRI fusion and discuss the results for the simulation and real data.
Keywords :
biomedical MRI; data analysis; electroencephalography; image fusion; independent component analysis; medical image processing; neurophysiology; EEG-fMRI data fusion; EEG-fMRI joint analysis; ICA algorithms; cognitive tasks; common mixing data; event related responses; human brain; independent component analysis algorithms; joint components; multivariate data analysis method; spatiotemporal resolution; Algorithm design and analysis; Brain modeling; Data models; Electroencephalography; Feature extraction; Independent component analysis; Joints;
Conference_Titel :
Intelligent and Advanced Systems (ICIAS), 2012 4th International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-4577-1968-4
DOI :
10.1109/ICIAS.2012.6306099