Linked e-resources
Details
Table of Contents
1 Brief History and Development of Electrophysiological Recording Techniques in Neuroscience; 1.1 The Brief History of Bioelectrical Investigation; 1.2 What Is Spike Train?; 1.3 The Measurement of Bioelectrical Signals; 1.4 Single-unit and Multi-unit Recording and Their Applications in Measuring Brain Activities; 1.5 Local Field Potentials; 1.6 Electroencephalography (EEG); References; 2 Adaptive Spike Sorting with a Gaussian Mixture Model; 2.1 Introduction; 2.2 Problem Statement; 2.3 Spike Sorting with a Gaussian Mixture Model
2.4 Clustering with the Expectation-Maximization Algorithm2.5 Gaussian Mixture Models and Adaptive Spike Sorting; 2.6 Hierarchical Model for GMM Parameters; 2.7 Parameter Updates; 2.8 Detecting Convergence; 2.9 Transition Model for Parameters; 2.10 Initial Spike Sorting; 2.11 Experiments on Synthetic Data; 2.12 Experiments on Real Neural Data; 2.13 Discussion; References; 3 Causality of Spike Trains Based on Entropy; 3.1 Introduction; 3.2 Entropy in Spike Trains; 3.2.1 Conditional Mutual Information; 3.2.2 Transfer Entropy; 3.2.3 Causal Entropy; 3.3 Izhikevich Model for Spike Trains
3.4 Characterization and Comparison of the Causality3.4.1 Parameter Choices in PCMI; 3.4.2 Comparison of Simulation Results; 3.5 Conclusions; References; 4 Quantification of Spike-LFP Synchronization; 4.1 Introduction; 4.2 Spike Field Coherence; 4.2.1 Spike-Triggered Average and Spike Field Coherence; 4.2.2 Bursty Spike Trains and Weighted Spike Field Coherence; 4.2.3 Simulation and Application; 4.2.3.1 Simulation Results; 4.2.3.2 Application to Real Data; 4.3 Spike-Triggered Correlation Matrix Synchronization; 4.3.1 Correlation Matrix and Spike-LFP Synchronization
4.3.2 Simulation and Application4.3.2.1 Simulation Results; 4.3.2.2 Application to Real Data; 4.4 Conclusion; References; 5 Artifact Removal in EEG Recordings; 5.1 Introduction; 5.2 Denoising Methods; 5.2.1 Regression Methods; 5.2.2 Filtering Methods; 5.2.3 Blind Source Separation Methods; 5.2.4 Source Decomposition Methods; 5.2.5 EEMD-ICA Method; 5.3 Simulation; 5.3.1 Data Simulation; 5.3.2 Performance Metrics; 5.3.3 Parameter Settings; 5.3.4 Results and Discussions; 5.4 The Effects of Artifact Rejection on Seizure Detection; 5.4.1 EEG Recordings; 5.4.2 Results and Discussion
5.5 ConclusionsReferences; 6 Order Time Series Analysis of Neural Signals; 6.1 Introduction; 6.2 Order Time Series Analysis; 6.2.1 Permutation Entropy; 6.2.2 Forbidden Order Patterns; 6.2.3 Dissimilarity Index; 6.3 Applications; 6.3.1 Results of EEG Data; 6.3.2 Detection of Pre-Seizure EEG Changes; 6.4 Conclusions; References; 7 Dynamical Similarity Analysis of EEG Recordings; 7.1 Introduction; 7.2 Dynamical Similarity Analysis; 7.2.1 Phase Space Reconstruction; 7.2.2 Correlation Sum; 7.2.3 Nonlinear Similarity Index; 7.3 Simulation Analysis and Results; 7.3.1 Neural Mass Model
2.4 Clustering with the Expectation-Maximization Algorithm2.5 Gaussian Mixture Models and Adaptive Spike Sorting; 2.6 Hierarchical Model for GMM Parameters; 2.7 Parameter Updates; 2.8 Detecting Convergence; 2.9 Transition Model for Parameters; 2.10 Initial Spike Sorting; 2.11 Experiments on Synthetic Data; 2.12 Experiments on Real Neural Data; 2.13 Discussion; References; 3 Causality of Spike Trains Based on Entropy; 3.1 Introduction; 3.2 Entropy in Spike Trains; 3.2.1 Conditional Mutual Information; 3.2.2 Transfer Entropy; 3.2.3 Causal Entropy; 3.3 Izhikevich Model for Spike Trains
3.4 Characterization and Comparison of the Causality3.4.1 Parameter Choices in PCMI; 3.4.2 Comparison of Simulation Results; 3.5 Conclusions; References; 4 Quantification of Spike-LFP Synchronization; 4.1 Introduction; 4.2 Spike Field Coherence; 4.2.1 Spike-Triggered Average and Spike Field Coherence; 4.2.2 Bursty Spike Trains and Weighted Spike Field Coherence; 4.2.3 Simulation and Application; 4.2.3.1 Simulation Results; 4.2.3.2 Application to Real Data; 4.3 Spike-Triggered Correlation Matrix Synchronization; 4.3.1 Correlation Matrix and Spike-LFP Synchronization
4.3.2 Simulation and Application4.3.2.1 Simulation Results; 4.3.2.2 Application to Real Data; 4.4 Conclusion; References; 5 Artifact Removal in EEG Recordings; 5.1 Introduction; 5.2 Denoising Methods; 5.2.1 Regression Methods; 5.2.2 Filtering Methods; 5.2.3 Blind Source Separation Methods; 5.2.4 Source Decomposition Methods; 5.2.5 EEMD-ICA Method; 5.3 Simulation; 5.3.1 Data Simulation; 5.3.2 Performance Metrics; 5.3.3 Parameter Settings; 5.3.4 Results and Discussions; 5.4 The Effects of Artifact Rejection on Seizure Detection; 5.4.1 EEG Recordings; 5.4.2 Results and Discussion
5.5 ConclusionsReferences; 6 Order Time Series Analysis of Neural Signals; 6.1 Introduction; 6.2 Order Time Series Analysis; 6.2.1 Permutation Entropy; 6.2.2 Forbidden Order Patterns; 6.2.3 Dissimilarity Index; 6.3 Applications; 6.3.1 Results of EEG Data; 6.3.2 Detection of Pre-Seizure EEG Changes; 6.4 Conclusions; References; 7 Dynamical Similarity Analysis of EEG Recordings; 7.1 Introduction; 7.2 Dynamical Similarity Analysis; 7.2.1 Phase Space Reconstruction; 7.2.2 Correlation Sum; 7.2.3 Nonlinear Similarity Index; 7.3 Simulation Analysis and Results; 7.3.1 Neural Mass Model