Mining Spatio-Spectro-Temporal Cortical Dynamics: A Guideline for Offline and Online Electrocorticographic Analyses

Recent advances in the technology of electrocorticography (ECoG) allow accessing neural activity from most of the cortex, which poses the challenge of extracting relevant information from an overwhelming amount of data. In this chapter, we will present useful routines for identifying statistically significant features in high-dimensional ECoG signals (offline analysis) and for establishing decoding models that can translate ECoG signals to specific behavioral measures in real time (online analysis). We will use our data, which are freely available online, in a step-by-step demonstration and will highlight useful MATLAB toolboxes for trouble-free implementation.

[1]  Scott Makeig,et al.  High-frequency Broadband Modulations of Electroencephalographic Spectra , 2009, Front. Hum. Neurosci..

[2]  Hualou Liang,et al.  Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment , 2000, Biological Cybernetics.

[3]  Partha P. Mitra,et al.  Chronux: A platform for analyzing neural signals , 2010, Journal of Neuroscience Methods.

[4]  D. Brillinger Time series - data analysis and theory , 1981, Classics in applied mathematics.

[5]  Andrew B. Schwartz,et al.  Brain-Controlled Interfaces: Movement Restoration with Neural Prosthetics , 2006, Neuron.

[6]  M. Eichler,et al.  Assessing the strength of directed influences among neural signals using renormalized partial directed coherence , 2009, Journal of Neuroscience Methods.

[7]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces to restore motor function and probe neural circuits , 2003, Nature Reviews Neuroscience.

[8]  Helmut Ltkepohl,et al.  New Introduction to Multiple Time Series Analysis , 2007 .

[9]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[10]  Brian Litt,et al.  Mining terabytes of submillimeter-resolution ECoG datasets for neurophysiologic biomarkers , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[11]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  C. Granger Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .

[13]  Naotaka Fujii,et al.  Long-Term Asynchronous Decoding of Arm Motion Using Electrocorticographic Signals in Monkeys , 2009, Front. Neuroeng..

[14]  Alois Schlögl,et al.  Analyzing event-related EEG data with multivariate autoregressive parameters. , 2006, Progress in brain research.

[15]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[16]  A. Mognon,et al.  ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. , 2011, Psychophysiology.

[17]  Joachim Gross,et al.  Reliability of multivariate causality measures for neural data , 2011, Journal of Neuroscience Methods.

[18]  Steven L. Bressler,et al.  Wiener–Granger Causality: A well established methodology , 2011, NeuroImage.

[19]  D. Szarowski,et al.  Brain responses to micro-machined silicon devices , 2003, Brain Research.

[20]  Terrence J. Sejnowski,et al.  AUTOMATIC ARTIFACT REJECTION FOR EEG DATA USING HIGH-ORDER STATISTICS AND INDEPENDENT COMPONENT ANALYSIS , 2001 .

[21]  K. Miller Broadband Spectral Change: Evidence for a Macroscale Correlate of Population Firing Rate? , 2010, The Journal of Neuroscience.

[22]  J. Geweke,et al.  Measurement of Linear Dependence and Feedback between Multiple Time Series , 1982 .

[23]  Kaustubh Supekar,et al.  Sparse logistic regression for whole-brain classification of fMRI data , 2010, NeuroImage.

[24]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[25]  Joseph D. Bronzino,et al.  The Biomedical Engineering Handbook , 1995 .

[26]  Masa-aki Sato,et al.  Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns , 2008, NeuroImage.

[27]  Andreas Ziehe,et al.  TDSEP { an e(cid:14)cient algorithm for blind separation using time structure , 1998 .

[28]  Mingzhou Ding,et al.  Cortical functional network organization from autoregressive modeling of local field potential oscillations , 2007, Statistics in medicine.

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

[30]  M. Kaminski,et al.  Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method , 2003, Journal of Neuroscience Methods.

[31]  Katarzyna J. Blinowska,et al.  Review of the methods of determination of directed connectivity from multichannel data , 2011, Medical & Biological Engineering & Computing.

[32]  M. H. Quenouille Approximate Tests of Correlation in Time‐Series , 1949 .

[33]  John P. Donoghue,et al.  Connecting cortex to machines: recent advances in brain interfaces , 2002, Nature Neuroscience.

[34]  D. Thomson,et al.  Spectrum estimation and harmonic analysis , 1982, Proceedings of the IEEE.

[35]  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.

[36]  Cédric Févotte,et al.  Two contributions to blind source separation using time-frequency distributions , 2004, IEEE Signal Processing Letters.

[37]  Jie Cui,et al.  2008 Special Issue: BSMART: A Matlab/C toolbox for analysis of multichannel neural time series , 2008 .

[38]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[39]  Yasuo Nagasaka,et al.  Multidimensional Recording (MDR) and Data Sharing: An Ecological Open Research and Educational Platform for Neuroscience , 2011, PloS one.

[40]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[41]  J. Wolpaw,et al.  Decoding two-dimensional movement trajectories using electrocorticographic signals in humans , 2007, Journal of neural engineering.

[42]  Karen L. Smith,et al.  Effects of insertion conditions on tissue strain and vascular damage during neuroprosthetic device insertion , 2006, Journal of neural engineering.

[43]  Karl J. Friston,et al.  Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets , 1993, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[44]  Christopher J. James,et al.  A Comparison of Time Structure and Statistically Based BSS Methods in the Context of Long-Term Epileptiform EEG Recordings , 2004, ICA.

[45]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[46]  S. Scott,et al.  Reaching movements with similar hand paths but different arm orientations. I. Activity of individual cells in motor cortex. , 1997, Journal of neurophysiology.

[47]  Christopher J James,et al.  Independent component analysis for biomedical signals , 2005, Physiological measurement.

[48]  Jens Timmer,et al.  Handbook of time series analysis : recent theoretical developments and applications , 2006 .

[49]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[50]  Nitish V. Thakor,et al.  Wavelet (time-scale) analysis in biomedical signal processing , 2006 .

[51]  Arnaud Delorme,et al.  EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing , 2011, Comput. Intell. Neurosci..

[52]  Lei Ding,et al.  A cortical potential imaging study from simultaneous extra- and intracranial electrical recordings by means of the finite element method , 2006, NeuroImage.

[53]  S. Bressler,et al.  Granger Causality: Basic Theory and Application to Neuroscience , 2006, q-bio/0608035.

[54]  Michael A. DiSano,et al.  Variability of the Relationship between Electrophysiology and BOLD-fMRI across Cortical Regions in Humans , 2011, The Journal of Neuroscience.

[55]  Anil K. Seth,et al.  A MATLAB toolbox for Granger causal connectivity analysis , 2010, Journal of Neuroscience Methods.

[56]  Aapo Hyvärinen,et al.  Validating the independent components of neuroimaging time series via clustering and visualization , 2004, NeuroImage.

[57]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[58]  Barak A. Pearlmutter,et al.  Blind Source Separation by Sparse Decomposition in a Signal Dictionary , 2001, Neural Computation.

[59]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[60]  Ahmed H. Tewfik,et al.  ECoG Based Brain Computer Interface with Subset Selection , 2008, BIOSTEC.

[61]  W. Freeman,et al.  Spatial spectra of scalp EEG and EMG from awake humans , 2003, Clinical Neurophysiology.

[62]  F. Mussa-Ivaldi,et al.  Brain–machine interfaces: computational demands and clinical needs meet basic neuroscience , 2003, Trends in Neurosciences.

[63]  Justin C. Williams,et al.  Chronic neural recording using silicon-substrate microelectrode arrays implanted in cerebral cortex , 2004, IEEE Transactions on Biomedical Engineering.

[64]  Stefan Schaal,et al.  Variational Bayesian least squares: An application to brain-machine interface data , 2008, Neural Networks.

[65]  Motoaki Kawanabe,et al.  A resampling approach to estimate the stability of one-dimensional or multidimensional independent components , 2002, IEEE Transactions on Biomedical Engineering.

[66]  Katarzyna J. Blinowska,et al.  Determination of EEG activity propagation: pair-wise versus multichannel estimate , 2004, IEEE Transactions on Biomedical Engineering.

[67]  H. Akaike A new look at the statistical model identification , 1974 .

[68]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[69]  Dennis A. Turner,et al.  The development of brain-machine interface neuroprosthetic devices , 2011, Neurotherapeutics.

[70]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[71]  Rainer Goebel,et al.  Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns , 2008, NeuroImage.