Fengyu Cong

发表

Advances in Neural Networks - ISNN 2017

Fengyu Cong, Andrew Leung, Qinglai Wei, 2017, Lecture Notes in Computer Science.

Identifying Task-Based Dynamic Functional Connectivity Using Tensor Decomposition

Tapani Ristaniemi, Fengyu Cong, Wenya Liu, 2020, ICONIP.

Generalization of Linked Canonical Polyadic Tensor Decomposition for Group Analysis

Tapani Ristaniemi, Fengyu Cong, Xiulin Wang, 2019, ISNN.

ICA of full complex-valued fMRI data using phase information of spatial maps

Vince D. Calhoun, Fengyu Cong, Qiu-Hua Lin, 2015, Journal of Neuroscience Methods.

End-to-end sleep staging using convolutional neural network in raw single-channel EEG

Fengyu Cong, Zhiqiang Wang, Fan Li, 2021, Biomed. Signal Process. Control..

Multi-domain collaborative spectrum sensing and power control in presence of multiple primary users

Yang Liu, Fengyu Cong, Zhenyu Zhou, 2014, 2014 IEEE International Conference on Communication Systems.

Fast and effective model order selection method to determine the number of sources in a linear transformation model

Tapani Ristaniemi, Fengyu Cong, Zhaoshui He, 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

Data model conversion for independent component analysis to extract brain signals

Tapani Ristaniemi, Fengyu Cong, F. Cong, 2011, 2011 3rd International Conference on Awareness Science and Technology (iCAST).

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

Guoqiang Hu, Fengyu Cong, Serdar Aslan, 2020, Frontiers in Neuroscience.

Sequential nonnegative tucker decomposition on multi-way array of time-frequency transformed event-related potentials

Tapani Ristaniemi, Fengyu Cong, Andrzej Cichocki, 2012, 2012 IEEE International Workshop on Machine Learning for Signal Processing.

A quantitative analysis of noncircularity for complex-valued fMRI based on semi-blind ICA

Vince D. Calhoun, Fengyu Cong, Qiu-Hua Lin, 2011, 2011 3rd International Conference on Awareness Science and Technology (iCAST).

Evaluation and extraction of mismatch negativity through exploiting temporal, spectral, time-frequency, and spatial features

Fengyu Cong, F. Cong, 2010 .

How to validate similarity in linear transform models of event-related potentials between experimental conditions?

Tapani Ristaniemi, Fengyu Cong, Qiu-Hua Lin, 2014, Journal of Neuroscience Methods.

Feature Extraction by Nonnegative Tucker Decomposition from EEG Data Including Testing and Training Observations

Tapani Ristaniemi, Fengyu Cong, Andrzej Cichocki, 2012, ICONIP.

Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition

Tapani Ristaniemi, Fengyu Cong, Deqing Wang, 2018, ISNN.

Benefits of Multi-Domain Feature of mismatch Negativity Extracted by Non-Negative Tensor Factorization from EEG Collected by Low-Density Array

Heikki Lyytinen, Tapani Ristaniemi, Fengyu Cong, 2012, Int. J. Neural Syst..

Applying Wavelet Packet Decomposition and One-Class Support Vector Machine on Vehicle Acceleration Traces for Road Anomaly Detection

Jukka Riekki, Tapani Ristaniemi, Fengyu Cong, 2013, ISNN.

Classifying Healthy Children and Children with Attention Deficit through Features Derived from Sparse and Nonnegative Tensor Factorization Using Event-Related Potential

Heikki Lyytinen, Tapani Ristaniemi, Fengyu Cong, 2010, LVA/ICA.

Spatiotemporal Dynamical Analysis of Brain Activity During Mental Fatigue Process

Fengyu Cong, Tapani Ristaniemi, Chi Zhang, 2021, IEEE Transactions on Cognitive and Developmental Systems.

Answering six questions in extracting children’s mismatch negativity through combining wavelet decomposition and independent component analysis

Tapani Ristaniemi, Fengyu Cong, Yixiang Huang, 2011, Cognitive Neurodynamics.

Sparse Nonnegative CANDECOMP/PARAFAC Decomposition in Block Coordinate Descent Framework: A Comparison Study pdf

Tapani Ristaniemi, Fengyu Cong, Deqing Wang, 2018, ArXiv.

Deriving Electrophysiological Brain Network Connectivity via Tensor Component Analysis During Freely Listening to Music

Tapani Ristaniemi, Fengyu Cong, Jia Liu, 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis

Guoqiang Hu, Fengyu Cong, Jing Xu, 2020, BioMedical Engineering OnLine.

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

Tapani Ristaniemi, Fengyu Cong, Vinoo Alluri, 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

Post-ICA phase de-noising for resting-state complex-valued FMRI data

Vince D. Calhoun, Fengyu Cong, Qiu-Hua Lin, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Extracting multi-mode ERP features using fifth-order nonnegative tensor decomposition

Tapani Ristaniemi, Fengyu Cong, Deqing Wang, 2018, Journal of Neuroscience Methods.

Blind signal separation and reverberation cancelling with active sonar data

Fengyu Cong, Yifan Hu, Chi Hau Chen, 2005, Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005..

Individual-Level ICA to Extract the ERP Components from the Averaged EEG Data

Tapani Ristaniemi, Fengyu Cong, Heikki Lyytinen, 2015 .

Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition

Tapani Ristaniemi, Fengyu Cong, Xiulin Wang, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

Tapani Ristaniemi, Fengyu Cong, Petri Toiviainen, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Multi-domain feature analysis for depression: A study of N170 in time, frequency and spatial domains simultaneously

Fengyu Cong, Shan Chen, Shanbao Tong, 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Multi-subject fMRI data analysis: Shift-invariant tensor factorization vs. group independent component analysis

Vince D. Calhoun, Fengyu Cong, Qiu-Hua Lin, 2013, 2013 IEEE China Summit and International Conference on Signal and Information Processing.

Classification of Heart Sounds Using Convolutional Neural Network

Fengyu Cong, Fan Li, Klaus Mathiak, 2020, Applied Sciences.

Optimal imaging of multi-channel EEG features based on a novel clustering technique for driver fatigue detection

Tapani Ristaniemi, Fengyu Cong, Lina Sun, 2020, Biomed. Signal Process. Control..

Network Entropy for the Sequence Analysis of Functional Connectivity Graphs of the Brain

Tapani Ristaniemi, Fengyu Cong, Jia Liu, 2018, Entropy.

Can back-projection fully resolve polarity indeterminacy of independent component analysis in study of event-related potential?

Tapani Ristaniemi, Fengyu Cong, Igor Kalyakin, 2011, Biomed. Signal Process. Control..

Nonnegative Tensor Train Decompositions for Multi-domain Feature Extraction and Clustering

Fengyu Cong, Andrzej Cichocki, Anh Huy Phan, 2016, ICONIP.

Multistage Blind Source Separation and Deconvolution for Convolutive Mixture of Speech Signals

Fengyu Cong, Ichiro Hagiwara, Yanxue Liang, 2007 .

Stability-driven non-negative matrix factorization-based approach for extracting dynamic network from resting-state EEG

Fengyu Cong, Tianyi Zhou, Xiaoli Li, 2020, Neurocomputing.

2011 Ieee International Workshop on Machine Learning for Signal Processing Second Order Impropriety Based Complex-valued Algorithm for Frequency-domain Blind Separation of Convolutive Speech Mixtures

Tapani Ristaniemi, Fengyu Cong, Qiu-Hua Lin, 2022 .

Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis

Tapani Ristaniemi, Fengyu Cong, Vinoo Alluri, 2014, Journal of Neuroscience Methods.

Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening

Chi Zhang, Fengyu Cong, Petri Toiviainen, 2019, bioRxiv.

Optimal Number of Clusters by Measuring Similarity Among Topographies for Spatio-Temporal ERP Analysis

Fengyu Cong, Reza Mahini, Peng Xu, 2019, Brain Topography.

Model order effects on independent vector analysis applied to complex-valued fMRI data

Vince D. Calhoun, Fengyu Cong, Qiu-Hua Lin, 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia

Vince D. Calhoun, Fengyu Cong, Qiu-Hua Lin, 2018, Journal of Neuroscience Methods.

Automatic sleep scoring: A deep learning architecture for multi-modality time series

Fengyu Cong, Fan Li, Rui Yan, 2020, Journal of Neuroscience Methods.

Non-negative matrix factorization Vs. FastICA on mismatch negativity of children

Heikki Lyytinen, Tapani Ristaniemi, Zhilin Zhang, 2009, 2009 International Joint Conference on Neural Networks.

A Systematic Independent Component Analysis Approach to Extract Mismatch Negativity

Tapani Ristaniemi, Fengyu Cong, Aleksandr Aleksandrov, 2012, ISNN.

Automatic Sleep Scoring Toolbox and Its Application in Sleep Apnea

Tapani Ristaniemi, Fengyu Cong, Xiaoyu Wang, 2019, ICETE.

ERP qualification exploiting waveform, spectral and time-frequency infomax

Fengyu Cong, T. Ristaniemi, H. Lyytinen, 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

Wood Surface Quality Detection and Classification Using Gray Level and Texture Features

Fengyu Cong, Deqing Wang, Zengwu Liu, 2015, ISNN.

Discovering dynamic task-modulated functional networks with specific spectral modes using MEG

Tapani Ristaniemi, Fengyu Cong, Jia Liu, 2020, NeuroImage.

Analysis on subtracting projection of extracted independent components from EEG recordings

Tapani Ristaniemi, Fengyu Cong, Zheng Chang, 2011, Biomedizinische Technik. Biomedical engineering.

Blind speech signal separation based on non-stationary and colored characteristics

Fengyu Cong, Yifan Hu, Yanxue Liang, 2005, Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005..

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

Tapani Ristaniemi, Fengyu Cong, Vinoo Alluri, 2018, Journal of Neuroscience Methods.

Low-rank Approximation Based non-Negative Multi-Way Array Decomposition on Event-Related potentials

Tapani Ristaniemi, Fengyu Cong, Andrzej Cichocki, 2014, Int. J. Neural Syst..

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

Tapani Ristaniemi, Fengyu Cong, Vinoo Alluri, 2013, ESANN.

Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint

Fengyu Cong, Qiu-Hua Lin, Xiao-Feng Gong, 2020, IEEE Transactions on Medical Imaging.

An Automatic Sleep Scoring Toolbox: Multi-modality of Polysomnography Signals' Processing

Tapani Ristaniemi, Fengyu Cong, Rui Yan, 2019, ICETE.

Analysis of ongoing EEG elicited by natural music stimuli using nonnegative tensor factorization

Tapani Ristaniemi, Fengyu Cong, Andrzej Cichocki, 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

Exploiting ongoing EEG with multilinear partial least squares during free-listening to music

Tapani Ristaniemi, Fengyu Cong, Deqing Wang, 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

Distributed Road Surface Condition Monitoring Using Mobile Phones

Jukka Riekki, Teemu Leppänen, Tapani Ristaniemi, 2011, UIC.

Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition

Fengyu Cong, Wenya Liu, Xiulin Wang, 2019, Journal of Neuroscience Methods.

Extract Mismatch Negativity and P3a through Two-Dimensional Nonnegative Decomposition on Time-Frequency Represented Event-Related Potentials

Heikki Lyytinen, Tapani Ristaniemi, Fengyu Cong, 2010, ISNN.

Diffusion map for clustering fMRI spatial maps extracted by independent component analysis

Tapani Ristaniemi, Fengyu Cong, Vinoo Alluri, 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening

Tapani Ristaniemi, Fengyu Cong, Petri Toiviainen, 2019, Brain Topography.

Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering

Tapani Ristaniemi, Fengyu Cong, Guoliang Chen, 2020, Frontiers in Neuroscience.

Classification of Schizophrenia Patients and Healthy Controls Using ICA of Complex-Valued fMRI Data and Convolutional Neural Networks

Vince D. Calhoun, Fengyu Cong, Qiu-Hua Lin, 2019, ISNN.

Cluster Aggregation for Analyzing Event-Related Potentials

Peng Li, Fengyu Cong, Asoke K. Nandi, 2017, ISNN.

Multi-modality of polysomnography signals' fusion for automatic sleep scoring

Tapani Ristaniemi, Fengyu Cong, Rui Yan, 2019, Biomed. Signal Process. Control..

Semi-blind independent component analysis of functional MRI elicited by continuous listening to music

Tapani Ristaniemi, Fengyu Cong, Qiu-Hua Lin, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

Determining the number of sources in high-density EEG recordings of event-related potentials by model order selection

Tapani Ristaniemi, Fengyu Cong, Zhaoshui He, 2011, 2011 IEEE International Workshop on Machine Learning for Signal Processing.

Concatenated trial based Hilbert-Huang transformation on event-related potentials

Heikki Lyytinen, Tapani Ristaniemi, Fengyu Cong, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Consistency of independent component analysis for FMRI

Fengyu Cong, Huanjie Li, Wei Zhao, 2020, Journal of Neuroscience Methods.

Linking Brain Responses to Naturalistic Music Through Analysis of Ongoing EEG and Stimulus Features

Tapani Ristaniemi, Fengyu Cong, Vinoo Alluri, 2013, IEEE Transactions on Multimedia.

Adaptive independent vector analysis for multi-subject complex-valued fMRI data

Fengyu Cong, Li-Dan Kuang, Xiao-Feng Gong, 2017, Journal of Neuroscience Methods.

Comparison of Functional Network Connectivity and Granger Causality for Resting State fMRI Data

Vince D. Calhoun, Fengyu Cong, Qiu-Hua Lin, 2017, ISNN.

An adaptive fixed-point IVA algorithm applied to multi-subject complex-valued FMRI data

Vince D. Calhoun, Fengyu Cong, Qiu-Hua Lin, 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Driver fatigue analysis based on binary brain networks

Fengyu Cong, Chi Zhang, Hong Wang, 2017, 2017 Seventh International Conference on Information Science and Technology (ICIST).

Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition

Chi Zhang, Fengyu Cong, Shuo Cao, 2019, Brain Topography.

Multi-Domain Feature Extraction for Small Event-Related potentials through Nonnegative Multi-Way Array Decomposition from Low Dense Array EEG

Tapani Ristaniemi, Fengyu Cong, Andrzej Cichocki, 2013, Int. J. Neural Syst..

Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition

Tapani Ristaniemi, Fengyu Cong, Yongjie Zhu, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Double coupled canonical polyadic decomposition of third-order tensors: Algebraic algorithm and relaxed uniqueness conditions

Fengyu Cong, Lieven De Lathauwer, Qiu-Hua Lin, 2019, Signal Process. Image Commun..

Wavelet Filter Design Based on Frequency Responses for Filtering ERP Data With Duration of One Epoch

Tapani Ristaniemi, Fengyu Cong, Heikki Lyytinen, 2015 .

Multi-domain Feature of Event-Related Potential Extracted by Nonnegative Tensor Factorization: 5 vs 14 Electrodes EEG Data

Tapani Ristaniemi, Fengyu Cong, Andrzej Cichocki, 2012, LVA/ICA.

Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network

Fengyu Cong, Ling Long, Sijie Lin, 2019, Applied Sciences.

Spatial source phase: A new feature for identifying spatial differences based on complex‐valued resting‐state fMRI data

Fengyu Cong, Qiu-Hua Lin, Yu-Ping Wang, 2019, Human brain mapping.

Single-Trial Based Independent Component Analysis on mismatch Negativity in Children

Tapani Ristaniemi, Fengyu Cong, Hong Li, 2010, Int. J. Neural Syst..

Individual Independent Component Analysis on EEG: Event-Related Responses Vs. Difference Wave of Deviant and Standard Responses

Tapani Ristaniemi, Fengyu Cong, Zheng Chang, 2016, ISNN.

Decoding Analysis of Alpha Oscillation Networks on Maintaining Driver Alertness

Ying Li, Pengbo Liu, Fengyu Cong, 2020, Entropy.

Multi-resolution Statistical Shape Models for Multi-organ Shape Modelling

Tapani Ristaniemi, Fengyu Cong, Zhonghua Chen, 2020, ISNN.

Identical fits of nonnegative matrix/tensor factorization may correspond to different extracted event-related potentials

Heikki Lyytinen, Tapani Ristaniemi, Fengyu Cong, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Multi-Domain Feature of the ERP Extracted by NTF: New Approach for Group-Level Analysis of ERPs

Tapani Ristaniemi, Fengyu Cong, Heikki Lyytinen, 2015 .

Biological image analysis using deep learning-based methods: Literature review

Fengyu Cong, Shaoxiang Zhang, Ruxue Hu, 2018 .

Drawback of ICA Procedure on EEG: Polarity Indeterminacy at Local Optimization

Tapani Ristaniemi, Fengyu Cong, Igor Kalyakin, 2008 .

Alpha Band Dysconnectivity Networks in Major Depression during Resting State

Fengyu Cong, Wenya Liu, Xiulin Wang, 2021, 2021 29th European Signal Processing Conference (EUSIPCO).

Tucker Decomposition for Extracting Shared and Individual Spatial Maps from Multi-Subject Resting-State fMRI Data

Vince D. Calhoun, Fengyu Cong, Qiu-Hua Lin, 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression

Tapani Ristaniemi, Fengyu Cong, Petri Toiviainen, 2020, Int. J. Neural Syst..

Sparse nonnegative tensor decomposition using proximal algorithm and inexact block coordinate descent scheme

Fengyu Cong, Deqing Wang, Zheng Chang, 2021, Neural Computing and Applications.

The Effect of Alpha Oscillation Network Decoding on Driver Alertness

Fengyu Cong, Ruosong Chang, Chi Zhang, 2021, Newest Updates in Physical Science Research Vol. 9.

One dimensional convolutional neural networks for seizure onset detection using long-term scalp and intracranial EEG

Fengyu Cong, Tommi Kärkkäinen, Wenya Liu, 2021, Neurocomputing.

Fengyu Cong, Zhong Yao, F. Cong, 2015, Cognitive Neurodynamics.

Second-order improperness in frequency-domain colored signal model

Fengyu Cong, T. Ristaniemi, 2008, 2008 IEEE Workshop on Machine Learning for Signal Processing.

Tapani Ristaniemi, Fengyu Cong, Piia Astikainen, 2013 .