Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
A fast fixed-point algorithm for independent component analysis
Neural Computation
Analyzing and visualizing single-trial event-related potentials
Proceedings of the 1998 conference on Advances in neural information processing systems II
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Source localization using recursively applied and projected (RAP)MUSIC
IEEE Transactions on Signal Processing
Overlearning in marginal distribution-based ICA: analysis and solutions
The Journal of Machine Learning Research
Overlearning in marginal distribution-based ICA: analysis and solutions
The Journal of Machine Learning Research
Stabilized linear model for neuromagnetic source localization
BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Journal of VLSI Signal Processing Systems
Comparison of BSS methods for the detection of α-activity components in EEG
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Brains and phantoms: an ICA study of fMRI
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
CUDAICA: GPU optimization of infomax-ICA EEG analysis
Computational Intelligence and Neuroscience - Special issue on Advanced Computational Techniques and Tools for Neuroscience
A Bayesian inverse solution using independent component analysis
Neural Networks
Hi-index | 0.00 |
We applied second-order blind identification (SOBI), an independent component analysis method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identification and localization. We compare localization of the SOBI-separated components to localization from unprocessed sensor signals, using an equivalent current dipole modeling method. For visual and somatosensory modalities, SOBI preprocessing resulted in components that can be localized to physiologically and anatomically meaningful locations. Furthermore, this preprocessing allowed the detection of neuronal source activations that were otherwise undetectable. This increased probability of neuronal source detection and localization can be particularly beneficial for MEG studies of higher-level cognitive functions, which often have greater signal variability and degraded signal-to-noise ratios than sensory activation tasks.