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Table of contents
1 Introduction
1.1 The Brain
1.2 Bioelectricity of the brain
1.2.1 Model of a Neuron
1.2.2 Main brain rhythms
1.2.3 Bioelectrical fields
1.3 Epilepsy
1.3.1 Seizure of epilepsy
1.3.2 Epilepsy treatment
1.3.3 The temporal lobe epilepsy
1.4 The multi-modalities for epilepsy diagnosis
1.4.1 Non-invasive methods
1.4.2 Invasive methods
1.5 EEG and SEEG measurements
1.5.1 The electrical stimulation
1.5.1.1 Stimulation and networks mechanism in epilepsy
1.5.1.2 The amygdala-hippocampal stimulation
1.6 Objectives of the thesis
2 Multidimensional decomposition. Application to DBS separation in SEEG/EEG
2.1 Introduction
2.2 Methods and Model
2.2.1 Filtering approaches
2.2.1.1 Savitzky-Golay Filter (SGF)
2.2.1.2 Singular Spectrum Analysis (SSA)
2.2.1.3 Empirical Mode Decomposition (EMD)
2.2.1.4 Fast Intrinsic Mode Decomposition (IMD)
2.2.2 Multidimensional analysis
2.2.2.1 Filtering-GEVD approach
2.2.2.2 Multi-channel SSA (MSSA)
2.2.2.3 Multivariate EMD (MEMD)
2.2.2.4 Blind Source Separation (BSS)
2.3 (S)EEG data analysis using presented methods: examples and discussion
2.3.1 Filtration and decomposition methods (mono-channel)
2.3.2 Multichannel separation methods
2.4 DBS source extraction from SEEG measurements
2.4.1 DBS-SEEG acquisition
2.4.2 Discussion of the DBS model
2.5 Simulations and real datasets
2.5.1 Synthetic datasets
2.5.2 Real datasets
2.6 Experiments and Results
2.6.1 Parameters selection
2.6.2 Estimation of performance
2.6.3 Simulated SEEG data
2.6.4 Real SEEG analysis
2.7 Conclusion
3 Localization of SEEG electrodes
3.1 Introduction and motivation of study
3.2 Image Acquisition
3.3 Registration
3.3.1 Mutual information
3.3.2 Optimization
3.4 Matter segmentation
3.4.1 Head tissue (matter) segmentation using MRI
3.4.2 Non-brain tissue segmentation using CT
3.4.2.1 Detailed algorithm
3.4.3 Summary of segmentation process for 5 matters
3.5 Electrode localization
3.5.1 Skull Stripping
3.5.2 Correlation of the pattern
3.5.3 Identification of the Multicaptor
3.5.4 Optimization of 3D localization
3.5.5 Localization results and conclusion
4 Forward modeling in vivo using DBS source
4.1 Physical and mathematical formulation of forward problem
4.1.1 Notations
4.1.2 Maxwell’s equations and quasi-static approximation
4.1.3 Primary and secondary currents
4.1.4 Final electric potential equation
4.2 Infinite homogeneous medium
4.3 Primary currents
4.4 Boundary conditions
4.5 Spherical models
4.5.1 Single sphere method
4.5.2 Multi-sphere method
4.6 Realistic head models
4.6.1 BEM
4.6.2 FEM
4.6.2.1 Linear shape functions
4.6.2.2 Solving Linear equation system
4.6.3 Meshing
4.7 Summary and implementation of methods
4.8 Conclusion
5 Validation and results: Forward models using real intracerebral DBS measurements
5.1 DBS source approximation models in FEM
5.1.1 Error criterion for forward models accuracy
5.1.2 Dipole and Source-Sink
5.2 Sensitivity analysis of (S)EEG froward models
5.2.1 Infinite Homogenous medium (IHM)
5.2.2 Spherical models
5.2.3 Realistic models
5.2.3.1 BEM
5.2.3.2 FEM
5.2.3.3 One compartment BEM/FEM
5.2.4 Performance and summary of applied methods
5.3 Validation of forward models in real deep brain stimulation (DBS) measurements
5.3.1 Propagation data extraction
5.3.2 Configuration of multi-electrode and stimulation dipole
5.3.3 Results and discussion
5.4 Influence of the CSF/Gray/White matter conductivity ratio in SEEG/EEG
5.4.1 Optimization of CSF/Gray/White conductivities
5.5 Conclusion
6 Conclusion and Perspectives
6.1 Summary of the thesis
6.2 Discussion and Perspectives
6.3 Conclusion
Bibliography
