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

Tensor factorization has exciting advantages to analyze EEG for simultaneously exploiting its information in the time, frequency and spatial domains as well as for sufficiently visualizing data in different domains concurrently. Event-related potentials (ERPs) are usually investigated by the group-level analysis, for which tensor factorization can be used. However, sizes of a tensor including time-frequency representation of ERPs of multiple channels of multiple participants can be immense. It is time-consuming to decompose such a tensor. The low-rank approximation based sequential nonnegative Tucker decomposition (LraSNTD) has been recently developed and shown to be computationally efficient with respect to some benchmark datasets. Here, LraSNTD is applied to decompose a fourth-order tensor representation of ERPs. We find that the decomposed results from LraSNTD and a benchmark nonnegative Tucker decomposition algorithm are very similar. So, LraSNTD is promising for ERP studies.

[1]  R. Näätänen.  Attention and brain function , 1992 .

[2]  Liqing Zhang,et al.  Noninvasive BCIs: Multiway Signal-Processing Array Decompositions , 2008, Computer.

[3]  Tapani Ristaniemi,et al.  Answering six questions in extracting children’s mismatch negativity through combining wavelet decomposition and independent component analysis , 2011, Cognitive Neurodynamics.

[4]  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.

[5]  Andrzej Cichocki,et al.  Nonnegative Matrix and Tensor Factorization T , 2007 .

[6]  Karl J. Friston,et al.  Mechanisms of evoked and induced responses in MEG/EEG , 2006, NeuroImage.

[7]  Andrzej Cichocki,et al.  Extended HALS algorithm for nonnegative Tucker decomposition and its applications for multiway analysis and classification , 2011, Neurocomputing.

[8]  J. Joutsensalo,et al.  Optimal Digital Filtering versus Difference Waves on the Mismatch Negativity in an Uninterrupted Sound Paradigm , 2007, Developmental neuropsychology.

[9]  Durk Talsma,et al.  Auto-adaptive averaging: detecting artifacts in event-related potential data using a fully automated procedure. , 2008, Psychophysiology.

[10]  H. Lyytinen,et al.  Frequency-response-based Wavelet Decomposition for Extracting Children's Mismatch Negativity Elicited by Uninterrupted Sound , 2011 .

[11]  Heikki Lyytinen,et al.  Identical fits of nonnegative matrix/tensor factorization may correspond to different extracted event-related potentials , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[12]  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.

[13]  Bülent Yener,et al.  Unsupervised Multiway Data Analysis: A Literature Survey , 2009, IEEE Transactions on Knowledge and Data Engineering.

[14]  Rasmus Bro,et al.  MULTI-WAY ANALYSIS IN THE FOOD INDUSTRY Models, Algorithms & Applications , 1998 .

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

[16]  C. C. Duncan,et al.  Event-related potentials in clinical research: Guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400 , 2009, Clinical Neurophysiology.

[17]  H. Lyytinen,et al.  Does Mismatch Negativity Show Differences in Reading-Disabled Children Compared to Normal Children and Children with Attention Deficit? , 2007, Developmental neuropsychology.

[18]  Saeid Sanei,et al.  EEG signal processing , 2000, Clinical Neurophysiology.

[19]  Lars Kai Hansen,et al.  ERPWAVELAB A toolbox for multi-channel analysis of time–frequency transformed event related potentials , 2007, Journal of Neuroscience Methods.

[20]  Tapani Ristaniemi,et al.  Can back-projection fully resolve polarity indeterminacy of independent component analysis in study of event-related potential? , 2011, Biomed. Signal Process. Control..

[21]  Tapani Ristaniemi,et al.  Multi-domain Feature of Event-Related Potential Extracted by Nonnegative Tensor Factorization: 5 vs 14 Electrodes EEG Data , 2012, LVA/ICA.

[22]  Tapani Ristaniemi,et al.  Analysis on subtracting projection of extracted independent components from EEG recordings , 2011, Biomedizinische Technik. Biomedical engineering.

[23]  Wim Van Paesschen,et al.  Canonical Decomposition of Ictal Scalp EEG and Accurate Source Localisation: Principles and Simulation Study , 2007, Comput. Intell. Neurosci..

[24]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[25]  Tapani Ristaniemi,et al.  Single-Trial Based Independent Component Analysis on mismatch Negativity in Children , 2010, Int. J. Neural Syst..

[26]  Margot J. Taylor,et al.  Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. , 2000, Psychophysiology.

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

[28]  S Makeig,et al.  Blind separation of auditory event-related brain responses into independent components. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Patrick Dupont,et al.  Canonical decomposition of ictal scalp EEG reliably detects the seizure onset zone , 2007, NeuroImage.

[30]  A. Cichocki,et al.  Tensor decompositions for feature extraction and classification of high dimensional datasets , 2010 .

[31]  H. Kiers,et al.  Three-mode principal components analysis: choosing the numbers of components and sensitivity to local optima. , 2000, The British journal of mathematical and statistical psychology.

[32]  L. K. Hansen,et al.  Sparse Coding and Automatic Relevance Determination for Multiway models , 2009 .

[33]  Andrzej Cichocki,et al.  Nonnegative Tensor Factorization for Continuous EEG Classification , 2007, Int. J. Neural Syst..

[34]  Heikki Lyytinen,et al.  Classifying Healthy Children and Children with Attention Deficit through Features Derived from Sparse and Nonnegative Tensor Factorization Using Event-Related Potential , 2010, LVA/ICA.

[35]  Bülent Yener,et al.  Computational analysis of epileptic focus localization , 2006 .

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

[37]  T. Sejnowski,et al.  Functionally Independent Components of the Late Positive Event-Related Potential during Visual Spatial Attention , 1999, The Journal of Neuroscience.

[38]  R. Näätänen,et al.  The mismatch negativity: an index of cognitive decline in neuropsychiatric and neurological diseases and in ageing. , 2011, Brain : a journal of neurology.

[39]  E. Oja,et al.  BSS and ICA in Neuroinformatics: From Current Practices to Open Challenges , 2008, IEEE Reviews in Biomedical Engineering.

[40]  J. Pernier,et al.  Stimulus Specificity of Phase-Locked and Non-Phase-Locked 40 Hz Visual Responses in Human , 1996, The Journal of Neuroscience.