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

BACKGROUND Ongoing EEG data are recorded as mixtures of stimulus-elicited EEG, spontaneous EEG and noises, which require advanced signal processing techniques for separation and analysis. Existing methods cannot simultaneously consider common and individual characteristics among/within subjects when extracting stimulus-elicited brain activities from ongoing EEG elicited by 512-s long modern tango music. NEW METHOD Aiming to discover the commonly music-elicited brain activities among subjects, we provide a comprehensive framework based on fast double-coupled nonnegative tensor decomposition (FDC-NTD) algorithm. The proposed algorithm with a generalized model is capable of simultaneously decomposing EEG tensors into common and individual components. RESULTS With the proposed framework, the brain activities can be effectively extracted and sorted into the clusters of interest. The proposed algorithm based on the generalized model achieved higher fittings and stronger robustness. In addition to the distribution of centro-parietal and occipito-parietal regions with theta and alpha oscillations, the music-elicited brain activities were also located in the frontal region and distributed in the 4-11 Hz band. COMPARISON WITH EXISTING METHOD(S) The present study, by providing a solution of how to separate common stimulus-elicited brain activities using coupled tensor decomposition, has shed new light on the processing and analysis of ongoing EEG data in multi-subject level. It can also reveal more links between brain responses and the continuous musical stimulus. CONCLUSIONS The proposed framework based on coupled tensor decomposition can be successfully applied to group analysis of ongoing EEG data, as it can be reliably inferred that those brain activities we obtained are associated with musical stimulus.

[1]  A. Maratos,et al.  Music therapy for depression. , 2008, The Cochrane database of systematic reviews.

[2]  R. Schaefer,et al.  Music perception and imagery in EEG: alpha band effects of task and stimulus. , 2011, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[3]  Stefan Koelsch,et al.  Brain and Music , 2012 .

[4]  Yukihiko Yamashita,et al.  Linked PARAFAC/CP Tensor Decomposition and Its Fast Implementation for Multi-block Tensor Analysis , 2012, ICONIP.

[5]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[6]  Barbara L. Wheeler,et al.  Music, Health, and Wellbeing , 2016 .

[7]  M. Grigutsch,et al.  Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music. , 2007, Psychophysiology.

[8]  Yanchun Zhang,et al.  EEG Signal Analysis and Classification , 2016, Health Information Science.

[9]  Andrzej Cichocki,et al.  Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical Data , 2015, Proceedings of the IEEE.

[10]  Patrick Dupont,et al.  Tensor decompositions and data fusion in epileptic electroencephalography and functional magnetic resonance imaging data , 2017, WIREs Data Mining Knowl. Discov..

[11]  R. VanRullen,et al.  The Phase of Ongoing EEG Oscillations Predicts Visual Perception , 2009, The Journal of Neuroscience.

[12]  Nitis Mukhopadhyay,et al.  Correlation Coefficient , 2011, International Encyclopedia of Statistical Science.

[13]  Vince D. Calhoun,et al.  EEGIFT: Group Independent Component Analysis for Event-Related EEG Data , 2011, Comput. Intell. Neurosci..

[14]  Qiu-Hua Lin,et al.  Generalized Non-Orthogonal Joint Diagonalization With LU Decomposition and Successive Rotations , 2015, IEEE Transactions on Signal Processing.

[15]  P. Vuust,et al.  Music for insomnia in adults. , 2015, The Cochrane database of systematic reviews.

[16]  Yuan-Pin Lin,et al.  EEG-Based Emotion Recognition in Music Listening , 2010, IEEE Transactions on Biomedical Engineering.

[17]  L. Trainor,et al.  Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions , 2001 .

[18]  Tapani Ristaniemi,et al.  Exploiting ongoing EEG with multilinear partial least squares during free-listening to music , 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

[19]  Jaeseung Jeong.  EEG dynamics in patients with Alzheimer's disease , 2004, Clinical Neurophysiology.

[20]  Lieven De Lathauwer,et al.  Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank- (Lr, n, Lr, n, 1) Terms - Part II: Algorithms , 2015, SIAM J. Matrix Anal. Appl..

[21]  C. Herrmann.  Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena , 2001, Experimental Brain Research.

[22]  H. Adeli,et al.  Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology , 2010 .

[23]  Seungjin Choi,et al.  Group Nonnegative Matrix Factorization for EEG Classification , 2009, AISTATS.

[24]  G. Tononi,et al.  Local sleep and learning , 2004, Nature.

[25]  Florian Roemer,et al.  Multi-dimensional model order selection , 2011, EURASIP J. Adv. Signal Process..

[26]  Tapani Ristaniemi,et al.  Extracting multi-mode ERP features using fifth-order nonnegative tensor decomposition , 2018, Journal of Neuroscience Methods.

[27]  Yuan-Pin Lin,et al.  Support vector machine for EEG signal classification during listening to emotional music , 2008, 2008 IEEE 10th Workshop on Multimedia Signal Processing.

[28]  Qiu-Hua Lin,et al.  Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation , 2016, IEEE Transactions on Signal Processing.

[29]  Yanchun Zhang,et al.  EEG Signal Analysis and Classification: Techniques and Applications , 2017 .

[30]  Andrzej Cichocki,et al.  Tensor Decompositions: A New Concept in Brain Data Analysis? , 2013, ArXiv.

[31]  Ali Taylan Cemgil,et al.  Link prediction in heterogeneous data via generalized coupled tensor factorization , 2013, Data Mining and Knowledge Discovery.

[32]  Lars Kai Hansen,et al.  Decomposing the time-frequency representation of EEG using non-negative matrix and multi-way factorization , 2006 .

[33]  J. Ket,et al.  Music therapy for depression. , 2017, The Cochrane database of systematic reviews.

[34]  Rufin VanRullen,et al.  The phase of ongoing EEG oscillations predicts the amplitude of peri-saccadic mislocalization , 2016, Scientific Reports.

[35]  Tapani Ristaniemi,et al.  Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening , 2019, Brain Topography.

[36]  Jie Li,et al.  Explore the Brain Response to Naturalistic and Continuous Music Using EEG Phase Characteristics , 2016, ICIC.

[37]  Pierre Morel,et al.  Gramm: grammar of graphics plotting in Matlab , 2018, J. Open Source Softw..

[38]  David M. Groppe,et al.  Mass univariate analysis of event-related brain potentials/fields I: a critical tutorial review. , 2011, Psychophysiology.

[39]  Lieven De Lathauwer,et al.  Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank-(Lr, n, Lr, n, 1) Terms - Part I: Uniqueness , 2015, SIAM J. Matrix Anal. Appl..

[40]  Marina Schmid,et al.  An Introduction To The Event Related Potential Technique , 2016 .

[41]  Rasmus Bro,et al.  Multiway analysis of epilepsy tensors , 2007, ISMB/ECCB.

[42]  S. Luck.  An Introduction to the Event-Related Potential Technique , 2005 .

[43]  Tapani Ristaniemi,et al.  Linking Brain Responses to Naturalistic Music Through Analysis of Ongoing EEG and Stimulus Features , 2013, IEEE Transactions on Multimedia.

[44]  Andrzej Cichocki,et al.  Hierarchical ALS Algorithms for Nonnegative Matrix and 3D Tensor Factorization , 2007, ICA.

[45]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[46]  Xun Chen,et al.  Joint Blind Source Separation for Neurophysiological Data Analysis: Multiset and multimodal methods , 2016, IEEE Signal Processing Magazine.

[47]  Mikko Sams,et al.  Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm , 2012, NeuroImage.

[48]  Xi-Jing Chen,et al.  Music therapy for people with schizophrenia and schizophrenia-like disorders. , 2017, The Cochrane database of systematic reviews.

[49]  H. Berger.  Über das Elektrenkephalogramm des Menschen , 1938, Archiv für Psychiatrie und Nervenkrankheiten.

[50]  A. Cichocki,et al.  alidating rationale of group-level component analysis based on estimating umber of sources in EEG through model order selection , 2012 .

[51]  L. Trainor,et al.  Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions , 2001 .

[52]  Xiaofeng Gong,et al.  Tensor decomposition of EEG signals: A brief review , 2015, Journal of Neuroscience Methods.

[53]  Heikki Lyytinen,et al.  Benefits of Multi-Domain Feature of mismatch Negativity Extracted by Non-Negative Tensor Factorization from EEG Collected by Low-Density Array , 2012, Int. J. Neural Syst..

[54]  Tapani Ristaniemi,et al.  Analysis of ongoing EEG elicited by natural music stimuli using nonnegative tensor factorization , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[55]  Andrzej Cichocki,et al.  Fast Nonnegative Matrix/Tensor Factorization Based on Low-Rank Approximation , 2012, IEEE Transactions on Signal Processing.

[56]  Morten Mørup,et al.  Applications of tensor (multiway array) factorizations and decompositions in data mining , 2011, WIREs Data Mining Knowl. Discov..

[57]  Tapani Ristaniemi,et al.  Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[58]  Vince D. Calhoun,et al.  A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data , 2009, NeuroImage.

[59]  Xi-Jing Chen,et al.  Music therapy for people with schizophrenia and schizophrenia-like disorders. , 2011, The Cochrane database of systematic reviews.

[60]  William Stafford Noble,et al.  Support vector machine , 2013 .

[61]  A. G. Asuero,et al.  The Correlation Coefficient: An Overview , 2006 .

[62]  Andrzej Cichocki,et al.  Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations , 2009, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[63]  Antoine J. Shahin,et al.  Development of auditory phase-locked activity for music sounds. , 2010, Journal of neurophysiology.

[64]  Tapani Ristaniemi,et al.  Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition , 2018, ISNN.