Showing items with similar title or keywords.

• On the usage of joint diagonalization in multivariate statistics 

  Nordhausen, Klaus; Ruiz-Gazen, Anne (Elsevier, 2022)

  Scatter matrices generalize the covariance matrix and are useful in many multivariate data analysis methods, including well-known principal component analysis (PCA), which is based on the diagonalization of the covariance ...

• Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi‐site MRI data 

  Hao, Yuxing; Xu, Huashuai; Xia, Mingrui; Yan, Chenwei; Zhang, Yunge; Zhou, Dongyue; Kärkkäinen, Tommi; Nickerson, Lisa D.; Li, Huanjie; Cong, Fengyu (Wiley-Blackwell, 2023)

  Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the ...

• Generation of stimulus features for analysis of FMRI during natural auditory experiences 

  Tsatsishvili, Valeri; Cong, Fengyu; Ristaniemi, Tapani; Toiviainen, Petri; Alluri, Vinoo; Brattico, Elvira; Nandi, Asoke (IEEE, 2014)

  In contrast to block and event-related designs for fMRI experiments, it becomes much more difficult to extract events of interest in the complex continuous stimulus for finding corresponding blood-oxygen-level ...

• Nonlinear blind source separation exploiting spatial nonstationarity 

  Sipilä, Mika; Nordhausen, Klaus; Taskinen, Sara (Elsevier, 2024)

  In spatial blind source separation the observed multivariate random fields are assumed to be mixtures of latent spatially dependent random fields. The objective is to recover latent random fields by estimating the unmixing ...

• Snowball ICA : A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data 

  Hu, Guoqiang; Waters, Abigail B.; Aslan, Serdar; Frederick, Blaise; Cong, Fengyu; Nickerson, Lisa D. (Frontiers Media, 2020)

  In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order ...