Anmerkung: Intro -- Preface -- Organization -- Contents -- Cognitive and Computational Foundations of Brain Science -- Emotion Recognition Based on Gramian Encoding Visualization -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Encoding Time Series Data into Images by Gramian Angular Field -- 5 Tiled Convolutional Neural Networks -- 6 Emotion Classification -- 6.1 Experimental Setting -- 6.2 Results and Discussion -- 7 Conclusions and Future Work -- References -- EEG Based Brain Mapping by Using Frequency-Spatio-Temporal Constraints -- 1 Introduction -- 2 Materials and Methods -- 2.1 Multi-rate Filter Banks -- 2.2 Inverse Problem with Frequency-Spatio-Temporal Constraints -- 3 Experimental Framework -- 3.1 Simulated Active Sources -- 3.2 Real EEG Signals -- 3.3 Performance Measure -- 4 Results -- 5 Conclusions -- References -- An EEG-Based Emotion Recognition Model with Rhythm and Time Characteristics -- Abstract -- 1 Introduction -- 2 Method -- 2.1 Rhythm and Time Characteristics Analysis of EEG -- 2.2 Long-Short Memory Neural Network -- 3 LSTM-Based EEG Emotion Recognition Model -- 4 Results and Discussion -- 4.1 Data Description -- 4.2 Assessment Method Overview -- 4.3 Analysis of Binding Relationship Between Time and Rhythm -- 4.4 Emotion Recognition Results Comparison and Analysis -- 5 Conclusions -- Acknowledgements -- References -- Influence of Realistic Head Modeling on EEG Forward Problem -- 1 Introduction -- 2 Methods -- 2.1 EEG Forward Problem -- 2.2 Solving the Forward Problem with FDM -- 3 Experimental Setup -- 3.1 Realistic Patient-Specific Head Model (RHM) -- 3.2 Parametric Inverse Solution -- 4 Results -- 4.1 Influence of Multiple Tissue Conductivity Modeling in the Potential Fields Propagation -- 4.2 Dipole Estimation Error in Realistic Head Modeling -- 5 Concluding Remark -- References. , Computational Model for Reward-Based Generation and Maintenance of Motivation -- Abstract -- 1 Introduction -- 2 Background -- 2.1 Motivation Generation -- Reward Pathway -- 2.2 Motivation Maintenance -- Reward Prediction Error -- 3 Computational Model -- 4 Simulation -- 5 Mathematical Analysis for Hebbian Learning -- 6 Conclusion -- References -- Current Design with Minimum Error in Transcranial Direct Current Stimulation -- 1 Introduction -- 2 Methods -- 2.1 Simulation of a Realistic Head Model -- 2.2 Proposed Mathematical Model -- 2.3 Proposed Numerical Algorithm -- 3 Experimental Results -- 3.1 Experimental Design -- 3.2 Comparison Metrics -- 3.3 Results -- 4 Conclusions -- References -- Assessment of Source Connectivity for Emotional States Discrimination -- 1 Introduction -- 2 Materials and Methods -- 2.1 EEG Forward Problem Formulation Within Regions of Interest - ROIs -- 2.2 EEG Inverse Problem for Estimating ROI Time-Courses -- 2.3 Assessment of Pairwise Connectivity Between ROIs -- 3 Experimental Set-up -- 3.1 EEG Data of Emotion Analysis -- 3.2 Benchmarking Connectivity Scenarios -- 4 Results -- 5 Discussion and Conclusions -- References -- Rich Dynamics Induced by Synchronization Varieties in the Coupled Thalamocortical Circuitry Model -- 1 Introduction -- 2 Mathematical Models -- 3 Synchronization Dynamics of 2-Compartment Coupled Thalamocortical Models -- 3.1 Complete Synchronization (CS) by Bidirectional Sigmoidal Coupling (SC) -- 3.2 Lag Synchronization (LS) Control Using Unidirectional Adaptive Delay Feedback (ADF) -- 3.3 The Anticipated Synchronization (AS) by Unidirectional Active Control (AC) -- 4 Synchronization Dynamics of 3-Compartment Thalamocortical Motifs -- 5 Conclusion -- References -- Human Information Processing Systems -- Humans Have a Distributed, Molecular Long-Term Memory -- 1 Introduction -- 2 Primitive Memory. , 3 Neural Systems -- 4 Consolidation and Recall -- 5 Chordless Cycle Systems -- 6 Mechanisms of Distributed Storage -- 7 Discussion -- 8 Appendix -- References -- Functional Connectivity Analysis Using the Oddball Auditory Paradigm for Attention Tasks -- 1 Introduction -- 2 Methods -- 2.1 Experimental Paradigm of Cognitive Evoked Potentials -- 2.2 Extraction of Inter-channel Connectivity Features -- 2.3 Estimation of Significant Connections -- 3 Results and Discussion -- 3.1 Visualization and Analysis of Connectivity -- 3.2 Significant Connections -- 4 Conclusions -- 5 Future Work -- References -- Perspective Taking vs Mental Rotation: CSP-Based Single-Trial Analysis for Cognitive Process Disambiguation -- 1 Introduction -- 2 Materials and Methods -- 2.1 Experiment Setup -- 2.2 EEG Data Pre-processing -- 2.3 CSP-Based Single-Trial Analysis -- 2.4 Performance Metrics and Statistical Analysis -- 3 Results -- 4 Discussion and Conclusion -- References -- Using the Partial Directed Coherence to Understand Brain Functional Connectivity During Movement Imagery Tasks -- 1 Introduction -- 2 Methods -- 2.1 Traditional BCI Training -- 2.2 BCI Control Task -- 2.3 EEG Data Acquisition and Preprocessing -- 2.4 Feature Extraction and Classification -- 2.5 Partial Directed Coherence -- 3 Results -- 3.1 Alpha Band Analysis -- 3.2 Beta Band Analysis -- 4 Conclusions -- References -- Combining fMRI Data and Neural Networks to Quantify Contextual Effects in the Brain -- Abstract -- 1 Introduction -- 2 Concept Attribute Representation Theory -- 3 Data Collection and Processing -- 3.1 Sentence Collection and Semantic Word Vectors -- 3.2 Neural Images -- 3.3 Data Preparation -- 4 Computational Model -- 5 Results -- 5.1 Effects of Similar Context -- 5.2 Effects of Different Contexts -- 5.3 Characterizing Differences in Context -- 6 Discussion and Further Work -- 7 Conclusion. , Acknowledgments -- References -- Network Analysis of Brain Functional Connectivity in Mental Arithmetic Using Task-Evoked fMRI -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 2.1 Subjects -- 2.2 Experiment Design -- 2.3 fMRI Data Acquisition and Preprocessing -- 2.4 Construction of Functional Brain Networks -- 2.5 Thresholding of Graph Edges -- 2.6 Network Analysis Measures -- 2.7 Statistical Test -- 3 Results -- 4 Discussions -- 5 Conclusions -- References -- The Astrocytic Microdomain as a Generative Mechanism for Local Plasticity -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Discussion -- References -- Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective -- 1 Introduction -- 2 Data Collection -- 2.1 The MEG Unit -- 2.2 Participants and Protocol -- 2.3 Data Preprocessing -- 3 Methods -- 3.1 Wavelet Analysis -- 3.2 Artificial Neural Network -- 4 Results and Discussions -- 5 Conclusion -- References -- Brain Big Data Analytics, Curation and Management -- Use of Temporal Attributes in Detection of Functional Areas in Basal Ganglia -- 1 Introduction -- 2 DBS Data and Attributes -- 2.1 DBS Data -- 2.2 Microrecording Based Primary Attributes -- 2.3 Temporal Attributes -- 3 Evaluation and Interpretation -- 3.1 SVM with Linear Kernel -- 3.2 SVM with RBF Kernel -- 3.3 Random Forest -- 4 Conclusions -- References -- Influence of Time-Series Extraction on Binge Drinking Interpretability Using Functional Connectivity Analysis -- 1 Introduction -- 2 Methods -- 2.1 Estimation of Brain Source Activity -- 2.2 Time-Series Extraction from Measured MEG Data -- 2.3 Measure of Brain Connectivity -- 3 Experimental Set-Up -- 3.1 MEG Database and Preprocessing -- 3.2 Brain Activity Mapping -- 3.3 ROI Selection and Time-Series Extraction -- 3.4 Connectivity Analysis -- 4 Discussion and Concluding Remarks. , References -- Regularized State Observers for Source Activity Estimation -- 1 Introduction -- 2 Materials and Methods -- 2.1 Forward and Inverse Problem Formulation -- 2.2 Discrete Physiologically Non-linear Model -- 2.3 Regularization Parameter Estimation -- 3 Experimental Framework -- 3.1 Simulated EEG Data -- 3.2 Regularized Observers Evaluation over Real EEG Database -- 4 Results and Discussion -- 5 Conclusions -- References -- Distributional Representation for Resting-State Functional Brain Connectivity Analysis -- 1 Introduction -- 2 Problem Definition -- 3 Distributional Representation Analysis -- 3.1 Data Description -- 3.2 Single Brain Analysis -- 3.3 Group Analysis -- 4 Results and Discussion -- 4.1 Outliers -- 4.2 Group Difference -- 5 Conclusion and Future Work -- References -- Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Dataset Collection -- 2.2 EEG Pre-processing -- 2.3 Deep Learning Architectures -- 2.4 Experiments -- 3 Results -- 3.1 Architecture Comparison -- 3.2 Statistical Analysis - ANOVA Model -- 4 Discussion -- Acknowledgements -- References -- Efficient and Automatic Subspace Relevance Determination via Multiple Kernel Learning for High-Dimensional Neuroimaging Data -- 1 Introduction -- 2 Review -- 2.1 Support Vector Machine -- 2.2 Gaussian Processes for Regression -- 2.3 Gaussian Processes for Classification -- 2.4 Automatic Relevance Determination -- 3 Multiple Kernel Learning for Automatic Subspace Relevance Determination -- 3.1 Domain-Knowledge for Feature Bags -- 4 Experiments -- 4.1 Results -- 5 Conclusions -- References -- Improving SNR and Reducing Training Time of Classifiers in Large Datasets via Kernel Averaging -- 1 Introduction -- 2 Method -- 2.1 The Effect of Averaging -- 2.2 Averaging Instances in Input Space. , 2.3 Averaging Instances in Feature Space.