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Παρασκευή 1 Δεκεμβρίου 2017

Recovering Wood and McCarthy’s ERP-prototypes by means of ERP-specific procrustes-rotation

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Publication date: 1 February 2018
Source:Journal of Neuroscience Methods, Volume 295
Author(s): André Beauducel
BackgroundThe misallocation of treatment-variance on the wrong component has been discussed in the context of temporal principal component analysis of event-related potentials. There is, until now, no rotation-method that can perfectly recover Wood and McCarthy's prototypes without making use of additional information on treatment-effects.New methodIn order to close this gap, two new methods: for component rotation were proposed. After Varimax-prerotation, the first method identifies very small slopes of successive loadings. The corresponding loadings are set to zero in a target-matrix for event-related orthogonal partial Procrustes- (EPP-) rotation. The second method generates Gaussian normal distributions around the peaks of the Varimax-loadings and performs orthogonal Procrustes-rotation towards these Gaussian distributions. Oblique versions of this Gaussian event-related Procrustes- (GEP) rotation and of EPP-rotation are based on Promax-rotation.ResultsA simulation study revealed that the new orthogonal rotations recover Wood and McCarthy's prototypes and eliminate misallocation of treatment-variance. In an additional simulation study with a more pronounced overlap of the prototypes GEP Promax-rotation reduced the variance misallocation slightly more than EPP Promax-rotation.Comparison with Existing Method(s): Varimax- and conventional Promax-rotations resulted in substantial misallocations of variance in simulation studies when components had temporal overlap. A substantially reduced misallocation of variance occurred with the EPP-, EPP Promax-, GEP-, and GEP Promax-rotations.ConclusionsMisallocation of variance can be minimized by means of the new rotation methods: Making use of information on the temporal order of the loadings may allow for improvements of the rotation of temporal PCA components.



from #ORL-AlexandrosSfakianakis via ola Kala on Inoreader http://ift.tt/2zWGpvw

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