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[null,null,"Titolo",null,"Brain Informatics [[electronic resource] ] : International Conference, BI 2017, Beijing, China, November 16-18, 2017, Proceedings / / edited by Yi Zeng, Yong He, Jeanette Hellgren Kotaleski, Maryann Martone, Bo Xu, Hanchuan Peng, Qingming Luo"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017"]
[null,null,"ISBN",null,"3-319-70772-8"]
[null,null,"Edizione",null,"[1st ed. 2017.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVI, 336 p. 100 illus.)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence ; ; 10654"]
[null,null,"Disciplina",null,"006.32"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Pattern recognition"]
[{"$nulls":4},"Optical data processing"]
[{"$nulls":4},"User interfaces (Computer systems)"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Operating systems (Computers)"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Pattern Recognition"]
[{"$nulls":4},"Image Processing and Computer Vision"]
[{"$nulls":4},"User Interfaces and Human Computer Interaction"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Operating Systems"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Cognitive and Computational Foundations of Brain Science -- Speech Emotion Recognition Using Local and Global Features -- 1 Introduction -- 2 Materials and Methods -- 2.1 Database -- 2.2 Features for Speech Emotion Recognition -- 3 Results/Discussion -- 3.1 Classification Results for EMODB -- 3.2 Classification Results for RAVDESS -- 3.3 SFFS -- 4 Conclusions -- References -- Advertisement and Expectation in Lifestyle Changes: A Computational Model -- 1 Introduction -- 2 Temporal-Causal Modeling -- 3 The Computational Model -- 3.1 Graphical Representation of the Model -- 3.2 Numerical Representations and Parameters -- 4 Simulation Experiments -- 4.1 "]
[{"$nulls":4},"Hypotheses -- 4.2 Scenarios and Results -- 4.3 Explanation -- 5 Conclusion -- References -- A Computational Cognitive Model of Self-monitoring and Decision Making for Desire Regulation -- Abstract -- 1 Introduction -- 2 Background -- 3 Conceptual Representation of the Model -- 3.1 Desire Generation and Choosing Actions -- 3.2 Self-monitoring and Regulation Strategies -- 3.3 Numerical Representation of the Model -- 4 Simulation Results -- 5 Conclusion -- References -- Video Category Classification Using Wireless EEG -- Abstract -- 1 Introduction -- 2 Experimental Setup and Data Acquisition Techniques -- 2.1 Demographics of Subjects -- 2.2 EEG Recordings -- 2.3 Experimental Setup -- 3 Experimental Study and Findings -- 3.1 Algorithms and Methods -- 3.2 Experimental Results -- 4 Discussion -- 5 Conclusion -- References -- Learning Music Emotions via Quantum Convolutional Neural Network -- 1 Introduction -- 2 Related Work on Quantum Information -- 3 Quantum Convolutional Neural Network for Music Emotion Analysis -- 3.1 Rationale -- 3.2 Quantum Convolutional Neural Network -- 4 Experiments -- 5 Conclusions -- References."]
[{"$nulls":4},"Supervised EEG Source Imaging with Graph Regularization in Transformed Domain -- 1 Introduction -- 2 Inverse Problem -- 3 Graph Regularized EEG Source Imaging in Transformed Domain -- 3.1 EEG Source Imaging in Transformed Domain -- 3.2 Discriminative Source Reconstruction with Graph Regularization -- 4 Optimization with ADMM Algorithm -- 5 Numerical Experiment -- 6 Conclusion -- References -- Insula Functional Parcellation from FMRI Data via Improved Artificial Bee-Colony Clustering -- 1 Introduction -- 2 Related Content -- 2.1 Insula Functional Parcellation Based on FMRI Data -- 2.2 Artificial Bee Colony (ABC) Algorithm -- 3 DABCC Algorithm -- 3.1 Food Source Representation -- 3.2 Initialization -- 3.3 Self-adaptive Multidimensional Search Mechanism Based on Difference Bias for Employed Bee Search -- 3.4 Algorithm Description -- 4 Experimental Results and Analysis -- 4.1 Data Description and Preprocessing -- 4.2 Evaluation Metrics -- 4.3 Search Capability -- 4.4 Parcellation Results -- 4.5 Functional Consistency -- 5 Conclusion -- References -- EEG-Based Emotion Recognition via Fast and Robust Feature Smoothing -- 1 Introduction -- 2 Related Work -- 3 Moving Average Smoothing on Statistical Feature Set -- 3.1 Feature Extraction -- 3.2 Moving Average Smoothing on Extracted Features -- 3.3 Classification Algorithm -- 4 Emotion Recognition on DEAP Dataset -- 4.1 Experimental Setup -- 4.2 Results and Discussions -- 5 Conclusion -- References -- Human Information Processing Systems -- Stronger Activation in Widely Distributed Regions May not Compensate for an Ineffectively Connected Neural Network When Reading a Second Language -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Participants -- 2.2 Materials -- 2.3 Experimental Procedure -- 2.4 Data Acquisition -- 2.5 Data Processing -- 3 Results -- 4 Discussion."]
[{"$nulls":4},"4.1 Assimilated and Accommodated Neural Network for L2 -- 4.2 Stronger Activation but an Ineffectively Connected Neural Network -- Acknowledgments -- References -- Objects Categorization on fMRI Data: Evidences for Feature-Map Representation of Objects in Human Brain -- Abstract -- 1 Introduction -- 2 Method -- 2.1 Subjects and fMRI Data Acquisition -- 2.2 Stimuli and Experimental Procedure -- 2.3 Data Preprocessing -- 2.4 Voxel Selection -- 2.5 SVM Method -- 3 Results -- 3.1 Classification Results for One vs. One Classifiers -- 3.2 Classification Results for One vs. Two Classifiers -- 3.3 Classification Results for Two vs. Two Classifiers -- 3.4 Classification Results for Regions Maximally Responsive to One Category of Objects -- 4 Discussion and Conclusions -- Acknowledgments -- References -- "]
[{"$nulls":4},"Gender Role Differences of Female College Students in Facial Expression Recognition: Evidence from N170 and VPP -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 2.1 Participants -- 2.2 Stimuli -- 2.3 Experimental Procedure -- 2.4 Behavioral Data Analysis -- 2.5 EEG Recordings and Analysis -- 3 Results -- 3.1 Behavioral Results -- 3.2 ERP Results -- 4 Discussion -- 4.1 Gender Role Differences on Facial Expression Recognition: Evidence on Early ERP Components -- 4.2 Emotional Negativity Bias: Evidence on VPP -- 4.3 Emotion Congruency: Evidence on Behavior -- 5 Conclusion -- Acknowledgments -- References -- Brain Big Data Analytics, Curation and Management -- Overview of Acquisition Protocol in EEG Based Recognition System -- Abstract -- 1 Introduction -- 2 Signal Acquisition -- 2.1 The Noninvasive Electroencephalography Method -- 3 EEG Signal Based Recognition System -- 3.1 Relaxation -- 3.2 Motor/Non-motor Imaginary -- 3.3 Exposed to Stimuli (Evoked Potentials) -- 4 Analysis and Discussions -- 5 Conclusion -- Acknowledgments -- References."]
[{"$nulls":4},"A Study on Automatic Sleep Stage Classification Based on Clustering Algorithm -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Automatic Sleep Staging Classification Algorithm Based on K-Means Clustering -- 3.1 Denoising -- 3.2 Feature Extraction and Feature Selection -- 3.3 Automatic Sleep Stage Classification Based on Improving K-Means Algorithm -- 4 Experimental Results and Analysis -- 4.1 Sleep Data Set -- 4.2 Evaluation Metrics -- 4.3 Experimental Results and Discussion -- 5 Conclusion -- References -- Speaker Verification Method Based on Two-Layer GMM-UBM Model in the Complex Environment -- 1 Introduction -- 2 Methods -- 2.1 Voice Data Acquisition and Preprocessing -- 2.2 Feature Extraction -- 2.3 Speaker Verification Architecture Based on Two-Layer GMM-UBM Model -- 3 Results -- 3.1 Evaluation Criterion -- 3.2 GMM-UBM Speaker Verification Based on Segmented Voice Data -- 3.3 GMM-UBM Speaker Verification Based on Continuous Long-Term Voice Data -- 4 Discussion -- References -- Emotion Recognition from EEG Using Rhythm Synchronization Patterns with Joint Time-Frequency-Space Correlation -- 1 Introduction -- 2 Architecture of Emotional Recognition Model Based on Rhythm Synchronization Patterns (RSP-ERM) -- 2.1 Functions of Each Layer -- 2.2 Defining Emotional States - Class Label -- 3 Experimental Design -- 3.1 Data Description -- 3.2 Learning and Testing Process -- 3.3 Contrast Methods -- 4 Experimental Results and Discussion -- 5 Conclusions -- References -- Informatics Paradigms for Brain and Mental Health -- Patients with Major Depressive Disorder Alters Dorsal Medial Prefrontal Cortex Response to Anticipation with Different Saliences -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Subjects -- 2.2 Task Design -- 2.3 Data Acquisition and Analysis -- 3 Results -- 3.1 Anticipation Period Findings."]
[{"$nulls":4},"3.2 The Findings of Anticipation Effect on Picture Viewing -- 4 Discussion -- Acknowledgment -- References -- Abnormal Brain Activity in ADHD: A Study of Resting-State fMRI -- Abstract -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Image Processing -- 3 Statistics Analysis -- 4 Result -- 4.1 The Comparison of ALFF, fALFF and ReHo Between Two Groups -- 4.2 The Comparison of ALFF, fALFF and ReHo of Two Age Groups -- 5 Discussion -- Acknowledgements -- References -- Wearable EEG-Based Real-Time System for Depression Monitoring -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Making Sense of the Raw Data -- 4.1 Hardware -- 4.2 Resting EEG -- 4.3 Stimulus -- 4.4 Real-Time Signal Preprocessing -- 4.5 Feature Extraction -- 4.6 Classification -- 4.7 Visualization -- 5 Experiment -- "]
[{"$nulls":4},"5.1 Participants -- 5.2 Results -- 6 Conclusions and Future Work -- References -- Group Guided Sparse Group Lasso Multi-task Learning for Cognitive Performance Prediction of Alzheimer's Disease -- 1 Introduction -- 2 Proposed Method -- 2.1 Group Guided Sparse Group Lasso Multi-task Learning -- 2.2 Optimization -- 3 Experimental Results -- 3.1 Data and Experimental Setting -- 3.2 The Results of Comparing with the Comparable Methods -- 3.3 Identification of MRI Biomarkers -- 4 Conclusions -- References -- A Novel Deep Learning Based Multi-class Classification Method for Alzheimer's Disease Detection Using Brain MRI Data -- 1 Introduction -- 2 Related Work -- 3 Proposed Network Architecture -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Results -- 5 Conclusion -- References -- A Quantitative Analysis Method for Objectively Assessing the Depression Mood Status Based on Portable EEG and Self-rating Scale -- 1 Introduction -- 2 Material and Method -- 2.1 Experimental Design -- 2.2 Data Analysis -- 3 Results."]
[{"$nulls":4},"3.1 Depressive Mood Status Assessment Based on POMS-BCN Data."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of the International Conference on Brain Informatics, BI 2017, held in Beijing, China, in November 2017. The 31 revised full papers were carefully reviewed and selected from 64 submissions. BI addresses the computational, cognitive, physiological, biological, physical, ecological and social perspectives of brain informatics, as well as topics related to mental health and well-being."]