Investeşte în oameni! FONDUL SOCIAL EUROPEAN Proiect cofinanţat din Fondul Social European prin Programul Operaţional Sectorial pentru Dezvoltarea Resurselor Umane 2007-2013 Axa prioritară 1 : Educaţia şi formarea profesională în sprijinul creşterii economice şi dezvoltării societăţii bazate pe cunoaştere Domeniul major de intervenţie 1.5 Programe doctorale şi post-doctorale în sprijinul cercetării Titlul proiectului : Q-DOC- Creşterea calităţii studiilor doctorale în ştiinţe inginereşti pentru sprijinirea dezvoltării societăţii bazate pe cunoaştere Contract : POSDRU/107/1.5/S/78534 Beneficiar : Universitatea Tehnică din Cluj-Napoca FACULTY OF ELECTRONICS, TELECOMMUNICATIONS AND INFORMATION TECHNOLOGY Ing. Nicolae-Cristian PAMPU PhD. THESIS NEURONAL SIGNAL ANALYSIS INVOLVED IN SENSORY PROCESSING SUMMARY Scientific Coordinator : Prof.dr.ing Corneliu RUSU 2013
Contents Introduction 1 1.1 Motivation 1 1.2 Signal processing in neuroscience 2 1.3 Overview of the thesis 3 Neuroscience Background 5 2.1 The Human Brain 5 2.1.1 Structure and functional role of the brain........................ 5 2.1.2 The cortex........................................ 7 2.2 Neurons and neuronal activity 11 2.2.1 Neuron structure.................................... 11 2.2.2 Synapses........................................ 12 2.2.3 Neuronal action potential................................ 13 2.3 Measuring neuronal activity 15 2.3.1 EEG/MEG measurement of neuronal activity..................... 16 2.4 The problem of volume conduction 19 2.5 Summary 20 Methods for assessing oscillatory activity and detecting connectivity 21 3.1 Spectrum 23 3.1.1 Fourier Transform.................................... 24 3.1.2 Energy and Power Spectral density........................... 24 3.1.3 Estimating Spectrum.................................. 25 3.1.4 Causality........................................ 27 3.2 Autocorrelation and Cross-correlation 28 3.3 Autocovariance and Cross-covariance 30 3.4 Cross spectral density 31 3.5 Coherence 31 3.6 Phase Locking Value 32 3.7 Granger causality methods 34 3.7.1 Autoregressive modeling and Granger causality.................... 35 3.7.2 Directed transfer function................................ 41 3.7.3 Partial directed coherence................................ 43 3.7.4 Neuroscience applications............................... 45 3.8 Mutual Information 46 3.8.1 Entropy and Entropy estimation............................ 46 3.8.2 Mutual Information................................... 48 ii
3.9 Experiments 50 3.10Summary 55 A new method for measuring directed interactions 57 4.1 Transfer Entropy 57 4.2 Calculating Transfer entropy 59 4.3 Experiments 64 4.3.1 Analysis of unidirectional interaction.......................... 64 4.3.2 Analysis for multiple unidirectional interactions.................... 67 4.3.3 Analysis of bidirectional interaction.......................... 69 4.3.4 Analysis of self-feedback interactions......................... 71 4.3.5 Ring of multiple node system.............................. 74 4.3.6 Noise variation analysis................................. 75 4.3.7 Following the system s interaction dynamics...................... 76 4.4 Summary and discussions 79 Advanced connectivity analysis for Electroencephalography and Magnetoencephalography 81 5.1 Source Analysis 81 5.1.1 Forward problem.................................... 82 5.1.2 The Lead Field..................................... 89 5.1.3 Inverse problem..................................... 90 5.1.4 Source statistics..................................... 92 5.2 Electroencephalographic source reconstruction 94 5.2.1 Experiment description and data acquisition...................... 96 5.2.2 Source reconstruction.................................. 98 5.2.3 Source time course reconstruction........................... 108 5.2.4 Transfer entropy interaction delay estimation on reconstructed source space..... 111 5.3 Magnetoencephalographic source reconstruction 114 5.3.1 Experiment description and data acquisition...................... 114 5.3.2 Source reconstruction.................................. 116 5.3.3 Source time course reconstruction........................... 120 5.3.4 Transfer entropy interaction delay estimation on reconstructed source space..... 121 5.4 Summary and discussions 122 Conclusions 125 6.1 Personal contributions and conclusions 125 6.2 Future work 130 Bibliography 131 iii
Key Words Transfer Entropy, interaction lag, source imaging, T E SP O estimator, neuronal signal processing. Introduction and motivation of the thesis Human kind would have not evolved so much if it had not been the "spark" that made us unique among all the living creatures of earth. What made us unique is the ability of judgment, to have a perception about the world we are living in and to be able to self-teach. All this is due to the evolution over time of the brain, a very complex and important anatomical part that helped us, as a species, reach the amount of knowledge we have today. Although we have a high amount of knowledge about the anatomy of the brain, only a small part of its function can be explained today. In order to understand better the brain, we need to study how its functional processes give rise to awareness and judgment-making, the role of brain regions and how these regions interact with each other. It is not enough to know the very basic functions of brain cells, how they are chemically connected and how they are chemically interacting. We need to understand the brain at a macroscopic level, to understand how rather small regions can influence other larger functional regions. We can describe the brain as one complex system, where different functions of different areas emerge from low-level physical mechanisms. This allows the brain to produce more outcome (ie. high level processes, intelligence, behavior) than the sum of individual neural activities. If we can measure the neuronal activity and their effects we will obtain information which will permit a more detailed view of what is happening in the brain. This is not possible without methods from signal processing, an evolving domain that can help us understand of the neuronal processes at macroscopic level. Techniques from this domain can be used to detect and measure synchronized activities or temporal correlation of different brain areas. One problem of interest is to identify of the causal relations between several areas. These relations can provide information about the communication mechanisms from a complex neuronal network. It is desired that signal processing techniques are adapted to detect directional interactions, information transfer and system lag for neuronal data. The ability to estimate these parameters is crucial in the study of brain functions. Thesis Objectives First objective of this thesis is to show an overview about the interaction measuring methods both linear and non-linear. More, it must present an overview about causal measuring methods. These includes Information Theory methods for measuring transfer of information between random systems. The second objective of the thesis is to present and test a new estimator for Transfer Entropy (T E SP O ), estimator which represent a measurement technique for information transfer, directivity and lag between random processes. The testing of the estimator T E SP O has a role of identifying of the behavior in some situations, like non-linear systems with unidirectional, bidirectional and self feedback interactions. The third objective is the testing of the T E SP O estimator on real Electroencephalografic and Magnetoencephalografic signals. This also includes the reconstruction of neuronal source activity in corresponding brain areas and estimate the interaction using the T E SP O estimator. The last objective of this thesis is to analyze and interpret the resulted interactions, to find the correctness of these results compared to with previous neuronal studies. Thesis structure The thesis is divided in three parts, with a total of 5 chapters as : iv
First part (chapter 1,2 and 3) contains general information about neuronal activity, how it can be measured and the methods that can be used to detect interactions and connectivity. The second part (chapter 4) contains description of the new estimator T E SP O and its testing on non-linear systems. The third part (chapter 5) treats the analysis and reconstruction of source activity from real experimental data using EEG and MEG from two paradigms. The reconstructed source time courses were used to estimate the interactions directivity and delays with T E SP O method. Personal Contributions The work that has been done for this thesis permits the statement of the following contributions in the neuronal signal processing : 1. The study and improvement for transfer entropy method (T E SP O estimator), a new method based on the information theory which can measure interactions in non-linear in time series : the implementation of the algorithm ; non-linear system generated based on Autoregressive and Lorenz processes ; testing the algorithm using the simulated systems ; algorithm improvement and parallelization 2. EEG and MEG data recording using the experimental paradigms "Dots" and "Mooney" 3. Reconstruct of the corresponding neuronal source activation locations for both EEG and MEG data 4. Source state space reconstruction from the significant source locations in both experiments 5. Lag interaction estimation for the above source locations using T E SP O estimator. 6. Analyzing the source locations and estimated interactions, comparing them with other neuronal studies. 7. Bibliographic study for the synthesis of the techniques used in the domain of neuronal signal processing, more exact techniques for interaction estimation. Publications List Articles Published Articles Wibral, M., Pampu, N., Priesemann, V., Siebenhuhner, F., Seiwert, H., Lindner, M.,Lizier, J. T., Vincente, R., (2013). Measuring Information-Transfer Delays, PLoS ONE, 8(2), e55809. Pampu, N., (2011) Study of Effects of The Short Time Fourier Transform Configuration On EEG Spectral Estimates, Acta Technica Napocensis Electronica-Telecomunicatii, 54(4) :7-12. Work in progress Articles Pampu, N., Mureşan, R. C., Moca, V. V., Tincas, I., Wibral, M. Transfer Entropy as a way to benchmark volume conduction methods, Frontiers in Neuroscience (Neuroinformatics). Proceedings conferences Pampu, N. C., Vicente, R., Mureşan, R. C., Priesemann, V.,Siebenhuhner, F., and Wibral, M., (2013). Transfer Entropy as a tool for reconstructing interaction delays in neural signals, in proceedings of International Symposium on Signals, Circuits and Systems - ISSCS 2013. v
Wibral, M., Wollstadt, P., Meyer, U., Pampu, N., Priesemann, V., and Vicente, R. (2012).Revisiting Wiener s principle of causality - interaction-delay reconstruction using transfer entropy and multivariate analysis on delay weighted graphs, in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (p. 3676-3679)(EMBC), IEEE. Other scientific activity publications Bob, F. I., Pampu, N. C., Chira, L. T. (2011). Improving analog-to-digital converter s resolution using the oversampling technique, proceedings of Signal Processing and Applied Mathematics for Electronics and Communications-SPAMEC 2011, Cluj-Napoca, Romania. Pampu N. (2011) Mental Stress Level Indicator Based on Physiological Measurement, Novice Insights in Electronics, Communications and Information Technology Magazine, 1842-6085. Pampu N., Priesemann, V., Siebenhuhner, F., Vicente, R., Wibral, M, (2012) Reconstructing neural interaction delays with information theoretic methods, the Rhine-Main Neuroscience Network, 25.06.2012, Obervesel, Germania. Wibral, M., Siebenhuhner, F., Priesemann, V., Pampu, N. Lindner, M., Vicente, R. (2012). Estimating neural interaction delays using Transfer entropy, 18th International Conference on Biomagnetism, BIOMAG2012 26.08.2012, Paris Pampu, N., Munteanu, M., Rusu, C., Ciupa, R. Moga, R. (2007). Integrated System for Monitoring and Storing Biomedical Signals. in proceedings of MediTech, Acta Electrotehnica, pp. 277-280 vi