Directional and Causal Information Flow in EEG for Assessing Perceived Audio Quality

In this paper, electroencephalography (EEG) measurements are used to infer change in cortical functional connectivity in response to change in audio stimulus. Experiments are conducted wherein the EEG activity of human subjects is recorded as they listen to audio sequences whose quality varies with time. A causal information theoretic framework is then proposed to measure the information flow between EEG sensors appropriately grouped into different regions of interest (ROI) over the cortex. A new causal bidirectional information (CBI) measure is defined as an improvement over standard directed information measures for the purposes of identifying connectivity between ROIs in a generalized cortical network setting. CBI can be intuitively interpreted as a causal bidirectional modification of directed information, and inherently calculates the divergence of the observed data from a multiple access channel with feedback. Further, we determine the analytical relationship between the different causal measures and compare how well they are able to distinguish between the perceived audio quality. The connectivity results inferred indicate a significant change in the rate of information flow between ROIs as the subjects listen to different audio qualities, with CBI being the best in discriminating between the perceived audio quality, compared to using standard directed information measures.

[1]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[2]  A. Seth,et al.  Granger causality and transfer entropy are equivalent for Gaussian variables. , 2009, Physical review letters.

[3]  Olivier J. J. Michel,et al.  On directed information theory and Granger causality graphs , 2010, Journal of Computational Neuroscience.

[4]  Ifije E. Ohiorhenuan,et al.  Sparse coding and high-order correlations in fine-scale cortical networks , 2010, Nature.

[5]  Jinghua Xu,et al.  Information transmission in human cerebral cortex , 1997 .

[6]  H. Marko,et al.  The Bidirectional Communication Theory - A Generalization of Information Theory , 1973, IEEE Transactions on Communications.

[7]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[8]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[9]  Seung-Hyun Jin,et al.  EEG in schizophrenic patients: mutual information analysis , 2002, Clinical Neurophysiology.

[10]  Sergio Martinoia,et al.  Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks , 2009, PloS one.

[11]  B. Shinn-Cunningham,et al.  Task-modulated “what” and “where” pathways in human auditory cortex , 2006, Proceedings of the National Academy of Sciences.

[12]  C. Granger.  Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[13]  Jörg Kliewer,et al.  An Information Theoretic Approach Toward Assessing Perceptual Audio Quality Using EEG , 2015, IEEE Transactions on Molecular, Biological and Multi-Scale Communications.

[14]  Liam Paninski,et al.  Estimation of Entropy and Mutual Information , 2003, Neural Computation.

[15]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[16]  Lutz Jäncke,et al.  The Problem of Thresholding in Small-World Network Analysis , 2013, PloS one.

[17]  Mehryar Mohri,et al.  Confidence Intervals for the Area Under the ROC Curve , 2004, NIPS.

[18]  Liang Li,et al.  The dual-pathway model of auditory signal processing , 2008, Neuroscience Bulletin.

[19]  Thomas Wiegand,et al.  Brain-Computer Interfacing for multimedia quality assessment , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[20]  Vasily A. Vakorin,et al.  Confounding effects of indirect connections on causality estimation , 2009, Journal of Neuroscience Methods.

[21]  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.

[22]  Charles D. Creusere,et al.  Assessment of subjective audio quality from EEG brain responses using time-space-frequency analysis , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[23]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[24]  Pulkit Grover,et al.  Is the direction of greater Granger causal influence the same as the direction of information flow? , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[25]  Victor Solo,et al.  On causality and mutual information , 2008, 2008 47th IEEE Conference on Decision and Control.

[26]  Deniz Gençaga,et al.  Transfer Entropy , 2018, Entropy.

[27]  Karl J. Friston.  Functional and effective connectivity in neuroimaging: A synthesis , 1994 .

[28]  Sebastian Bosse,et al.  Toward a Direct Measure of Video Quality Perception Using EEG , 2012, IEEE Transactions on Image Processing.

[29]  Todd P. Coleman,et al.  Estimating the directed information to infer causal relationships in ensemble neural spike train recordings , 2010, Journal of Computational Neuroscience.

[30]  Albert Y. Zomaya,et al.  Local information transfer as a spatiotemporal filter for complex systems. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Liang Wu,et al.  Classifying n-back EEG data using entropy and mutual information features , 2007, ESANN.

[32]  Todd P. Coleman,et al.  Directed Information Graphs , 2012, IEEE Transactions on Information Theory.

[33]  J. Fermaglich.  Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[34]  J. Massey.  CAUSALITY, FEEDBACK AND DIRECTED INFORMATION , 1990 .

[35]  Stephen G. West,et al.  Structural equation models with non-normal variables: Problems and remedies , 1995 .

[36]  Gerhard Kramer,et al.  Directed information for channels with feedback , 1998 .

[37]  Ying Liu,et al.  The relationship between transfer entropy and directed information , 2012, 2012 IEEE Statistical Signal Processing Workshop (SSP).

[38]  Olivier J. J. Michel,et al.  Causal conditioning and instantaneous coupling in causality graphs , 2012, 2012 IEEE Statistical Signal Processing Workshop (SSP).

[39]  M. Mishkin,et al.  Dual streams of auditory afferents target multiple domains in the primate prefrontal cortex , 1999, Nature Neuroscience.

[40]  Gabriel Curio,et al.  Using ERPs for assessing the (sub) conscious perception of noise , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[41]  Yoichi Saito,et al.  Multidimensional Directed Information and Its Application , 2002 .

[42]  John M. Beggs,et al.  Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model , 2011, PloS one.

[43]  K. Shiraishi,et al.  Coherence Analysis of EEG Changes during Olfactory Stimulation , 1998, Clinical EEG.

[44]  P. Hall,et al.  On blocking rules for the bootstrap with dependent data , 1995 .

[45]  Rogers F. Silva,et al.  Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source Separation Algorithms on Real and Realistic Simulated Data , 2016, Brain Topography.

[46]  Gordon Pipa,et al.  Transfer entropy—a model-free measure of effective connectivity for the neurosciences , 2010, Journal of Computational Neuroscience.

[47]  Mahmood Al-khassaweneh,et al.  The Relationship Between Two Directed Information Measures , 2008, IEEE Signal Processing Letters.

[48]  Jörg Kliewer,et al.  Directed information measures for assessing perceived audio quality using EEG , 2015, 2015 49th Asilomar Conference on Signals, Systems and Computers.

[49]  Gregory W. Corder,et al.  Nonparametric Statistics : A Step-by-Step Approach , 2014 .

[50]  Jonathon Shlens,et al.  The Structure of Large-Scale Synchronized Firing in Primate Retina , 2009, The Journal of Neuroscience.

[51]  Charles D. Creusere,et al.  Assessing the Quality of Audio Containing Temporally Varying Distortions , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[52]  Andrea J. Goldsmith,et al.  Inferring neural connectivity via measured delay in directed information estimates , 2013, 2013 IEEE International Symposium on Information Theory.

[53]  Joseph T. Lizier,et al.  Directed Information Measures in Neuroscience , 2014 .

[54]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[55]  Sosuke Ito,et al.  Backward transfer entropy: Informational measure for detecting hidden Markov models and its interpretations in thermodynamics, gambling and causality , 2016, Scientific Reports.

[56]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[57]  G. Kramer,et al.  Causal conditioning, directed information and the multiple-access channel with feedback , 1998, Proceedings. 1998 IEEE International Symposium on Information Theory (Cat. No.98CH36252).

[58]  H. Harashima,et al.  A time‐series analysis method based on the directed transinformation , 1984 .

[59]  Olivier J. J. Michel,et al.  The relation between Granger causality and directed information theory: a review , 2012, Entropy.

[60]  B. Efron.  Nonparametric standard errors and confidence intervals , 1981 .

[61]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[62]  J. Gore,et al.  Mutual information analysis of the EEG in patients with Alzheimer's disease , 2001, Clinical Neurophysiology.

[63]  Jörg Kliewer,et al.  A new EEG-based causal information measure for identifying brain connectivity in response to perceived audio quality , 2017, 2017 IEEE International Conference on Communications (ICC).

[64]  Sebastian Bosse,et al.  Psychophysiology-Based QoE Assessment: A Survey , 2017, IEEE Journal of Selected Topics in Signal Processing.

[65]  Walter Krämer.  Wojtek J. Krzanowski and David J. Hand: ROC curves for continuous data , 2011 .

[66]  Hae-Young Kim.  Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis , 2013, Restorative dentistry & endodontics.

[67]  Aurel A. Lazar,et al.  Information theory in neuroscience , 2011, Journal of Computational Neuroscience.

[68]  J. Rauschecker,et al.  Maps and streams in the auditory cortex: nonhuman primates illuminate human speech processing , 2009, Nature Neuroscience.

[69]  Mikail Rubinov,et al.  Inferring effective computational connectivity using incrementally conditioned multivariate transfer entropy , 2013, BMC Neuroscience.

[70]  Albert Y. Zomaya,et al.  Information modification and particle collisions in distributed computation. , 2010, Chaos.

[71]  Marcus A. Magnor,et al.  Single-trial EEG classification of artifacts in videos , 2012, TAP.

[72]  Gang Li,et al.  Testing Statistical Significance of the Area under a Receiving Operating Characteristics Curve for Repeated Measures Design with Bootstrapping , 2005, Journal of Data Science.

[73]  David J. Hand,et al.  ROC Curves for Continuous Data , 2009 .

[74]  Andreas Daffertshofer,et al.  Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory , 2010, PloS one.

[75]  Michael J. Berry,et al.  Weak pairwise correlations imply strongly correlated network states in a neural population , 2005, Nature.

[76]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[77]  Barry Horwitz,et al.  The elusive concept of brain connectivity , 2003, NeuroImage.

[78]  Stefano Panzeri,et al.  How Well Can We Estimate the Information Carried in Neuronal Responses from Limited Samples? , 1997, Neural Computation.

[79]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .