On application of kernel PCA for generating stimulus features for fMRI during continuous music listening

Autor: Tsatsishvili, Valeri, Burunat, Iballa, Cong, Fengyu, Toiviainen, Petri, Alluri, Vinoo, Ristaniemi, Tapani

Publikováno v: In Journal of Neuroscience Methods 1 June 2018 303:1-6

Electrodermal activity in couple therapy for intimate partner violence

Autor: Paananen, Katja, Vaununmaa, Riikka, Holma, Juha, Karvonen, Anu, Kykyri, Virpi-Liisa, Tsatsishvili, Valeri, Kaartinen, Jukka, Penttonen, Markku, Seikkula, Jaakko

The aim of this study was to examine the extent to which intimate partner violence (IPV) is discussed in couple therapy, what the participants say about it and how, and how the participants’ electrodermal activity (EDA) is activated during these di

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1222::b8f70fccbb295777a4480cdd2bb8eab6
http://urn.fi/URN:NBN:fi:jyu-201805032445

A moment within the psychoanalytic psychotherapy of an adult female patient – The meanings of nonverbal and bodily expressions.

Autor: Aho-Mustonen, Päivi, Enckell, Henrik, Seikkula, Jaakko, Tsatsishvili, Valeri, Kykyri, Virpi-Liisa

Publikováno v: Scandinavian Psychoanalytic Review; Jun2020, Vol. 43 Issue 1, p13-26, 14p

Dimension reduction for individual ICA to decompose FMRI during real-world experiences:Principal component analysis vs. canonical correlation analysis

Autor: Tsatsishvili, Valeri, Cong, Fengyu, Puoliväli, Tuomas, Alluri, Vinoo, Toiviainen, Petri, Nandi, Asoke K., Brattico, Elvira, Ristaniemi, Tapani

Publikováno v: Tsatsishvili, V, Cong, F, Puoliväli, T, Alluri, V, Toiviainen, P, Nandi, A K, Brattico, E & Ristaniemi, T 2013, Dimension reduction for individual ICA to decompose FMRI during real-world experiences : Principal component analysis vs. canonical correlation analysis . in ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning . pp. 137-142, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013, Bruges, Belgium, 24/04/2013 .

Group independent component analysis (ICA) with special assumptions is often used for analyzing functional magnetic resonance imaging (fMRI) data. Before ICA, dimension reduction is applied to separate signal and noise subspaces. For analyzing noisy

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pure_au_____::04c24d8844c239f554ef5eab98ee1614
https://pure.au.dk/portal/da/publications/dimension-reduction-for-individual-ica-to-decompose-fmri-during-realworld-experiences(0939d8e4-48a7-4f8b-8b44-4451416b1c49).html

Combining PCA and multiset CCA for dimension reduction when group ICA is applied to decompose naturalistic fMRI data.

Autor: Tsatsishvili, Valeri, Cong, Fengyu, Toiviainen, Petri, Ristaniemi, Tapani

Publikováno v: 2015 25th International Conference on Field Programmable Logic & Applications (FPL); 2015, p1-6, 6p