| Analyzing consistency of independent components: An fMRI illustration J Ylipaavalniemi, R Vigário NeuroImage 39 (1), 169-180, 2008 | 70 | 2008 |
| Dependencies between stimuli and spatially independent fMRI sources: Towards brain correlates of natural stimuli J Ylipaavalniemi, E Savia, S Malinen, R Hari, R Vigário, S Kaski NeuroImage 48 (1), 176-185, 2009 | 47 | 2009 |
| Machine-learning system for optimising the performance of a biometric system J Ylipaavalniemi, TJGM Moretti, AR Partington US Patent 10,380,499, 2019 | 27 | 2019 |
| Consistency and asymptotic normality of FastICA and bootstrap FastICA N Reyhani, J Ylipaavalniemi, R Vigário, E Oja Signal processing 92 (8), 1767-1778, 2012 | 27 | 2012 |
| Analysis of auditory fMRI recordings via ICA: A study on consistency J Ylipaavalniemi, R Vigário Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference …, 2004 | 18 | 2004 |
| Finding dependent and independent components from related data sets: A generalized canonical correlation analysis based method J Karhunen, T Hao, J Ylipaavalniemi Neurocomputing 113, 153-167, 2013 | 11 | 2013 |
| Arabica: Robust ICA in a Pipeline J Ylipaavalniemi, J Soppela Independent Component Analysis and Signal Separation, 379-386, 2009 | 11 | 2009 |
| A generalized canonical correlation analysis based method for blind source separation from related data sets J Karhunen, T Hao, J Ylipaavalniemi The 2012 International Joint Conference on Neural Networks (IJCNN), 1-9, 2012 | 10 | 2012 |
| Functional elements and networks in fMRI J Ylipaavalniemi, E Savia, R Vigário, S Kaski | 10 | 2007 |
| Subspaces of spatially varying independent components in fMRI J Ylipaavalniemi, R Vigário Independent Component Analysis and Signal Separation, 665-672, 2007 | 9 | 2007 |
| Brains and phantoms: an ICA study of fMRI J Ylipaavalniemi, S Mattila, A Tarkiainen, R Vigário Independent Component Analysis and Blind Signal Separation, 503-510, 2006 | 7 | 2006 |
| Variability of independent components in functional magnetic resonance imaging J Ylipaavalniemi MSc, Department of Computer Science and Engineering, Helsinki University of …, 2005 | 5 | 2005 |
| Lainvalmistelu tiedonhallinnan haasteena–tekoäly ratkaisuna? H Lonka, A Keinänen, E Ovaska, K Kiiski, V Jääskinen, J Ylipaavalniemi, ... Edita Publishing, 2020 | 3 | 2020 |
| A canonical correlation analysis based method for improving BSS of two related data sets J Karhunen, T Hao, J Ylipaavalniemi International Conference on Latent Variable Analysis and Signal Separation …, 2012 | 3 | 2012 |
| A generalized canonical correlation analysis based method for blind source separation from related data sets K Juha, T Hao, J Ylipaavalniemi the 2012 International Joint Conference on Neural Networks (IJCNN). IEEE, 2012 | 2 | 2012 |
| Data-driven analysis for natural studies in functional brain imaging J Ylipaavalniemi Aalto University, 2013 | 1 | 2013 |
| Matching complex activation patterns with features of natural stimuli J Ylipaavalniemi, R Vigário Image 15 (16_Figure), 2, 2008 | 1 | 2008 |
| ICA decomposition of an auditory functional MRI reveals thalamic activation J Ylipaavalniemi, R Vigário Submitted to a conference, 0 | 1 | |
| Distributional convergence of subspace estimates in FastICA: a bootstrap study J Ylipaavalniemi, N Reyhani, R Vigário International Conference on Latent Variable Analysis and Signal Separation …, 2012 | | 2012 |
| Analysis of independent components in biomedical signals R Vigário, J Särelä, E Karp, J Ylipaavalniemi | | |
Ricardo VigárioUniversidade Nova de LisboaVerified email at fct.unl.pt
