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 ...

• 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 ...

• 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 ...

• Event-related potentials to unattended changes in facial expressions: detection of regularity violations or encoding of emotions? 

  Astikainen, Piia; Cong, Fengyu; Ristaniemi, Tapani; Hietanen, Jari K. (Frontiers, 2013)

  Visual mismatch negativity (vMMN), a component in event-related potentials (ERPs), can be elicited when rarely presented “deviant” facial expressions violate regularity formed by repeated “standard” faces. vMMN is observed ...

• KernelICA : Kernel Independent Component Analysis 

  Koesner, Christoph L.; Nordhausen, Klaus (CRAN - The Comprehensive R Archive Network, 2021)

  The kernel independent component analysis (kernel ICA) method introduced by Bach and Jordan (2003) . The incomplete Cholesky decomposition used in kernel ICA is provided as separate function.