Deep Neural Network (DNN)

     
http://bspl.korea.ac.kr/image/bspl/dnn.png        
Deep Neural Network (DNN) is an artificial neural network (ANN) with multiple layers between the input and output layers.
The DNN finds the correct mathematical manipulation to turn the input into the output, whether it be a linear relationship or a non-linear relationship.
Deep neural network (DNN) with weight sparsity control (i.e., L1-norm regularization) improved the classification performance using whole-brain resting-state functional connectivity patterns of schizophrenia patient and healthy groups.
Initializing DNN's weights through stacked auto-encoder enhanced the classification performance as well. (Kim et al., NI, 2016). 
Here, we provide MATLAB and Python based codes in terms of the DNN with the weight sparsity control

Reference: Kim et al., Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. 
NeuroImage. 2016 Jan.; 124(Pt A):127-46. PubMed / Google Scholar ]




Iterative Dual-Regression (IDR)

http://bspl.korea.ac.kr/Software/IDR/iDR2.png
Iterative Dual-Regression (iDR) with sparse prior is aimed to better estimate an individual's neuronal activation using the results of an independent component analysis (ICA) method applied to a temporally concatenated group of fMRI data 
(i.e., Tc-GICA method) 




Recursive approach of EEG-segment-based principal component analysis (rsPCA)

https://sites.google.com/site/bsplkoreauniversity/software/rspca/main_rspca.png?attredirects=0&height=232&width=800
Recursive approach of EEG-segment-based principal component analysis (rsPCA) toolbox to eliminate helium-pump artifact in EEG data that were simultaneously acquired with fMRI data.
Reference: Kim et al., Recursive approach of EEG segment based principal component analysis substantially reduces helium-pump artifacts of EEG data simultaneously acquired with fMRI, NeuroImage 2015, 104: 437-51 PubMed / Google Scholar ] 



Eye-tracking based Naturalistic Viewing Paradigm (ENV)























A naturalistic viewing paradigm using 360° panoramic video clips and real-time field-of-view changes with eye-gaze tracking, NeuroImage, Accepted [PubMed / Google Scholar]