Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications

Name: Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications | 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Puerto de la Cruz, Tenerife, Spain, May 31 - June 3, 2022, Proceedings, Part I
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9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, Puerto de la Cruz, Tenerife, Spain, May 31 - June 3, 2022, Proceedings, Part I

José Manuel Ferrández Vicente José Ramón Álvarez-Sánchez Félix de la Paz López Hojjat Adeli(Herausgeber*in)

Springer (Verlag)

Erschienen am 24. Mai 2022

XXVIII, 651 Seiten

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Inhalt

Intro
Preface
Organization
Contents - Part I
Contents - Part II
Machine Learning in Neuroscience
ConvNet-CA: A Lightweight Attention-Based CNN for Brain Disease Detection
1 Introduction
2 Data
3 Methodology
3.1 Convolutional Neural Network
3.2 Channel Attention Mechanism
3.3 ConvNet-CA
3.4 Evaluation Metrics
4 Experiments and Results
4.1 Experiment Set-Up
4.2 Performance on Multi-class Classification
4.3 The Effectiveness of Channel Attention Mechanism
4.4 Comparison with State-of-the-Art Methods
5 Conclusion
References
Temporal Phase Synchrony Disruption in Dyslexia: Anomaly Patterns in Auditory Processing
1 Introduction
2 Methods
2.1 Data
2.2 Connectivity Metric
2.3 Classification Pipeline
3 Results
4 Discussion
5 Conclusions
References
CAD System for Parkinson's Disease with Penalization of Non-significant or High-Variability Input Data Sources
1 Introduction
1.1 Parkinson's Disease
1.2 CAD Systems for Parkinson's Diagnosis
1.3 Ensemble Learning for Multimodal Data Analysis
2 Materials and Methods
2.1 Parkinson's Progression Markers Initiative
2.2 Image Preprocessing
2.3 Feature Selection and Dimensionality Reduction Algorithms
2.4 Classification Schema
3 Results
4 Discussion
5 Conclusions
References
Automatic Classification System for Diagnosis of Cognitive Impairment Based on the Clock-Drawing Test
1 Introduction
1.1 Clock Drawing Test
1.2 Artificial Intelligence
1.3 Automatic CDT Scoring Systems
2 Materials and Methods
2.1 CDT Database
2.2 Image Preprocessing
2.3 Classification Model
3 Results
4 Discussion
5 Conclusion
References
Unraveling Dyslexia-Related Connectivity Patterns in EEG Signals by Holo-Hilbert Spectral Analysis
1 Introduction
2 Materials and Methods
2.1 Holo-Hilbert Spectral Analysis
2.2 Functional Connectivity and Classification
3 Results and Interpretation
4 Conclusions and Future Work
References
Inter-channel Granger Causality for Estimating EEG Phase Connectivity Patterns in Dyslexia
1 Introduction
2 Material and Methods
2.1 Data Acquisition
2.2 Preprocessing
2.3 Hilbert Transform
2.4 Granger Causality Test
2.5 Machine Learning Classification
3 Results
4 Conclusions and Future Work
References
Automatic Diagnosis of Schizophrenia in EEG Signals Using Functional Connectivity Features and CNN-LSTM Model
1 Introduction
2 Material and Methods
2.1 Dataset
2.2 Feature Extraction
2.3 CNN-LSTM Model
3 Experiment Result
4 Discussion, Conclusion, and Future Works
References
Sleep Apnea Diagnosis Using Complexity Features of EEG Signals
1 Introduction
2 Materials and Methods
2.1 Experimental Data
2.2 Proposed Method
2.3 Feature Extraction
2.4 Feature Selection
2.5 Classification
3 Results and Discussion
4 Conclusion
References
Representational Similarity Analysis: A Preliminary Step to fMRI-EEG Data Fusion in MVPAlab
1 Introduction
2 Materials and Methods
2.1 Materials
2.2 Methods
3 Results and Discussion
4 Conclusions
References
Towards Mixed Mode Biomarkers: Combining Structural and Functional Information by Deep Learning
1 Introduction
2 Material and Methods
2.1 GM Density Map
2.2 Feature Extraction Using Deep Learning
2.3 Mixed Mode Images: Image Fusion Procedure
3 Results
4 Conclusions and Future Work
References
Modelling the Progression of the Symptoms of Parkinsons Disease Using a Nonlinear Decomposition of 123I FP-CIT SPECT Images
1 Introduction
2 Methodology
2.1 Dataset Description and Image Preprocessing
2.2 Manifold Learning
2.3 Classification and Regression Experiments
3 Results and Discussion
4 Conclusions
References
Capacity Estimation from Environmental Audio Signals Using Deep Learning
1 Introduction
2 Materials and Methods
2.1 The DISCO Dataset
2.2 Data Processing
3 Neural Network Architectures
3.1 Architectures for Image Data
3.2 Architectures for Audio Signals
4 Results
5 Discussion
6 Conclusions
References
Covid-19 Detection by Wavelet Entropy and Self-adaptive PSO
1 Introduction
2 Dataset
3 Methodology
3.1 Wavelet Entropy
3.2 Feedforward Neural Network
3.3 Self-adaptive Particle Swarm Optimisation
3.4 K-fold Cross-validation
4 Experiment Results and Discussions
4.1 WE Results
4.2 Statistical Results
4.3 Comparison to State-of-the-Art Approaches
5 Conclusions
References
RDNet: ResNet-18 with Dropout for Blood Cell Classification
1 Introduction
2 Materials
3 Methodology
3.1 Proposed RDNet
3.2 The Backbone of the Proposed RDNet
3.3 Dropout
4 Results
4.1 Experiment Settings
4.2 The Performance of the Proposed Model
4.3 Comparison of the Proposed Model with TRNet
5 Conclusion
References
Automatic Diagnosis of Myocarditis in Cardiac Magnetic Images Using CycleGAN and Deep PreTrained Models
1 Introduction
2 Material and Methods
2.1 Dataset
2.2 Data Augmentation Using Cycle GAN
2.3 Deep Pertained Models
3 Experiment Results
4 Discussion and Conclusion
References
Quantifying Inter-hemispheric Differences in Parkinson's Disease Using Siamese Networks
1 Introduction
2 Material and Methods
2.1 Dataset
2.2 Preprocessing
2.3 Siamese Neural Network
3 Results
4 Conclusions and Future Work
References
Analyzing Statistical Inference Maps Using MRI Images for Parkinson's Disease
1 Introduction
2 Materials and Methods
2.1 Parkinson's Progression Markers Initiative
2.2 Image Preprocessing
2.3 Statistical Parametric Mapping (SPM)
2.4 Statistical Agnostic Mapping (SAM)
3 Results
4 Discussion
5 Conclusions
References
Evaluating Intensity Concentrations During the Spatial Normalization of Functional Images for Parkinson's Disease
1 Introduction
2 Materials and Methods
2.1 HUVN Dataset
2.2 Image Preprocessing: Spatial Registration
2.3 Overview
3 Results
4 Discussion
5 Conclusions
References
Neuromotor and Cognitive Disorders
Monitoring Motor Symptoms in Parkinson's Disease Under Long Term Acoustic Stimulation
1 Introduction
2 Fundamentals
3 Materials and Methods
4 Results
4.1 Tremor Indicators
4.2 Bradykinesia Indicator
5 Conclusions
References
Evaluation of TMS Effects on the Phonation of Parkinson's Disease Patients
1 Introduction
1.1 Background
1.2 Working Hypotheses
2 Materials and Methods
2.1 Experimental Protocol
2.2 Feature Estimation
2.3 Feature Assessment
3 Results
4 Discussion
5 Conclusions
References
Effects of Neuroacoustic Stimulation on Two Study Cases of Parkinson's Disease Dysarthria
1 Introduction
2 Materials and Methods
3 Results
4 Discussion
5 Conclusions
References
Characterizing Masseter Surface Electromyography on EEG-Related Frequency Bands in Parkinson's Disease Neuromotor Dysarthria
1 Introduction
2 Experimental Framework
2.1 Methods
2.2 Materials
3 Results
4 Discussion
5 Conclusions
References
Acquisition of Relevant Hand-Wrist Features Using Leap Motion Controller: A Case of Study
1 Introduction
2 Methods and Materials
2.1 Leap Motion Controller
2.2 Objectives
2.3 Methodology
2.4 Framework and Hardware
2.5 Corpus
3 Results
4 Conclusions
References
A Pilot and Feasibility Study of Virtual Reality as Gamified Monitoring Tool for Neurorehabilitation
1 Introduction
2 Objectives
3 Methods and Materials
3.1 Participants
3.2 Frameworks and Hardware
3.3 Description of Two Scenarios
3.4 Indices Collected
3.5 Questionnaire
4 Results
5 Conclusion
References
Pairing of Visual and Auditory Stimuli: A Study in Musicians on the Multisensory Processing of the Dimensions of Articulation and Coherence
1 Introduction
2 Methods
2.1 Subjects
2.2 Stimuli Used
2.3 fMRI Scanning
2.4 Paradigm Design
2.5 fMRI Data Analysis
3 Results
3.1 Differences Between Coherent Articulations
3.2 Differences Between Coherent and Incoherent Articulations
4 Conclusions and Future Development
References
Design of Educational Scenarios with BigFoot Walking Robot: A Cyber-physical System Perspective to Pedagogical Rehabilitation
1 Cyber-physical Systems for Pedagogical Rehabilitation
1.1 Background Studies of the Appropriateness of Using Toy-like Robots in the Pedagogical Rehabilitation of Children with Autism
2 Functionalities of the BigFoot Robot
3 Educational Scenarios with BigFoot
4 Evaluation of the Fitness of the BigFoot Scenarios for Pedagogical Rehabilitation
5 Conclusions
References
Feasibility Study of a ML-Based ASD Monitoring System
1 Introduction
2 Materials and Methods
2.1 Monitoring System
2.2 Setup of the Clinical Environment
2.3 Protocol for Data Acquisition
2.4 Signal Processing and Dataset Generation
3 Results
3.1 Use of ML Algorithms for Information Extraction
4 Conclusions
References
ApEn: A Stress-Aware Pen for Children with Autism Spectrum Disorder
1 Introduction
2 Related Work
3 Method and Design Process
4 Final Design
5 Experiments
6 Results
7 Discussion
8 Conclusion
References
Anxiety Monitoring in Autistic Disabled People During Voice Recording Sessions
1 Introduction
2 Fundamentals
3 Experimental Framework
3.1 Materials
3.2 Methods
4 Results
5 Discussion
6 Conclusions
References
What Can Technology Do for Autistic Spectrum Disorder People?
1 Introduction
2 Description of Autism Spectrum Disorders
3 Overview of Supportive Applications for ASD
4 Needs for New Applications
5 Conclusions
References
Autism Spectrum Disorder (ASD): Emotional Intervention Protocol
1 Introduction
2 Objectives
3 Materials
4 Participants
5 Measures
5.1 IQ
5.2 Attitude Towards the Robotic Therapy
5.3 Engagement Level
5.4 Evolution of the Disorder
6 Procedure
7 Discussion
8 Conclusion
References
Creating Vignettes for a Robot-Supported Education Solution for Children with Autism Spectrum Disorder
1 Introduction
2 The ROSA Toolbox
3 Other Studies Using Robots with Children with ASD
4 Involving Children, Parents, and Teachers in the Creation Process
5 Finding Vignettes
6 Challenges and Next Steps
References
Identification of Parkinson's Disease from Speech Using CNNs and Formant Measures
1 Introduction
2 Materials and Methods
2.1 Formant Features
2.2 CNN Architecture
3 Experimental Framework
4 Results and Discussion
5 Conclusions
References
Characterization of Hypokinetic Dysarthria by a CNN Based on Auditory Receptive Fields
1 Introduction
2 Methods
3 Experimental Framework
4 Results
5 Discussion
6 Conclusions
References
Evaluation of the Presence of Subharmonics in the Phonation of Children with Smith Magenis Syndrome
1 Introduction
2 Materials and Methods
2.1 Participants
2.2 Recording Procedure
3 Acoustic Processing
3.1 Preprocessing of Data
3.2 Subharmonic Detection
4 Results
5 Discussion
6 Conclusion
References
Speech Analysis in Preclinical Identification of Alzheimer's Disease
1 Introduction
2 Speech Traits of Alzheimer's Disease (AD)
3 Automatic Speech Analysis of AD
References
The Effect of Breathing Maneuvers on the Interaction Between Pulse Fluctuation and Heart Rate Variability
1 Introduction
2 Materials and Methods
3 Results
4 Discussion and Conclusions
References
Horizon Cyber-Vision: A Cybernetic Approach for a Cortical Visual Prosthesis
1 Introduction
2 Materials and Methods
2.1 Hardware
2.2 Simulated Prosthetic Vision
2.3 Visual Cortical Prosthesis
2.4 Graphical Interface Software
2.5 Technical Description of Image Processing Strategies
2.6 Environments
2.7 Test Battery
3 Results
4 Discussion
5 Conclusions
References
The Assessment of Activities of Daily Living Skills Using Visual Prosthesis
1 Introduction
2 Conclusion
References
Affective Analysis
Artificial Intelligence Applied to Spatial Cognition Assessment
1 Introduction
2 Paper-and-Pencil and Digital Assessment Tools
3 The Baking Tray Task and E-BTT
4 Material and Method
4.1 Artificial Neural Networks
4.2 The Dataset
4.3 Results
5 Conclusions and Future Directions
References
Automatic Diagnosis of Mild Cognitive Impairment Using Siamese Neural Networks
1 Introduction
2 The Datasets
3 The Architectures
3.1 Phase 1: Pretraining with SQD Dataset
3.2 Phase 2: Tuning with ROCF Dataset
4 Results
4.1 Results of Phase 1
4.2 Results of Phase 2
5 Conclusions
References
A Comparison of Feature-based Classifiers and Transfer Learning Approaches for Cognitive Impairment Recognition in Language
1 Introduction
2 Proposed Approach
2.1 Conventional Approaches
2.2 Transfer Learning Approaches
3 Experiments
3.1 Data Set
3.2 Data Processing
3.3 Evaluation Techniques
4 Results
5 Conclusion
References
Detection of Alzheimer's Disease Using a Four-Channel EEG Montage
1 Introduction
2 Materials and Methods
2.1 Participants
2.2 EEG Acquisition
2.3 Preprocessing
2.4 Classification
2.5 Selection of Most Relevant Channels
3 Results and Discussion
4 Conclusion
References
Evaluating Imputation Methods for Missing Data in a MCI Dataset
1 Introduction
2 Methodology
2.1 Database Description
2.2 Missing Values Analysis
2.3 Imputation Strategy
3 Results
4 Conclusions
References
Automatic Scoring of Rey-Osterrieth Complex Figure Test Using Recursive Cortical Networks
1 Introduction
1.1 Neuropsychological Tests
1.2 Automation of Tests
2 Methods
3 Rey-Osterrieth Complex Figure Test
4 Component Classification Using RCN
5 Major Conclusion
References
Influence of the Level of Immersion in Emotion Recognition Using Virtual Humans
1 Introduction
2 Materials and Methods
2.1 Participants
2.2 Experimental Setup
2.3 Stimuli
2.4 Procedure
2.5 Data Analysis
3 Results
4 Discussion
5 Conclusions
References
Influence of Neutral Stimuli on Brain Activity Baseline in Emotional Experiments
1 Introduction
2 Materials and Methods
2.1 Database
2.2 Preprocessing of EEG Signals
2.3 Experimental Procedure
2.4 Feature Extraction
2.5 Statistical Analysis
3 Results
4 Discussion
5 Conclusions
References
Classification of Psychophysiological Patterns During Emotional Processing Using SVM
1 Introduction
2 Materials and Methods
2.1 Experimental Data
2.2 EEG Data Preprocessing
2.3 Reconstruction of EEG Sources
2.4 Functional Connectivity Network Construction
2.5 Feature Extraction
2.6 Classification and Evaluation
3 Results
4 Conclusions
References
Measuring Motion Sickness Through Racing Simulator Based on Virtual Reality
1 Introduction
2 Methods and Materials
2.1 Objectives
2.2 About VR Driving Simulator
2.3 Technical Description of the Simulator
2.4 VRSQ Test
2.5 Testing Sessions
2.6 Corpus
3 Results
4 Conclusions
References
Health Applications
Analysis of the Asymmetry in RNFL Thickness Using Spectralis OCT Measurements in Healthy and Glaucoma Patients
1 Introduction
2 Materials and Methods
2.1 Image Acquisition
2.2 RNFL Segmentation and Thickness Calculation
2.3 Calculation of Thickness Asymmetry
3 Results
4 Conclusions
References
Performance Evaluation of a Real-Time Phase Estimation Algorithm Applied to Intracortical Signals from Human Visual Cortex
1 Introduction
2 Methods
2.1 Experiment, Data Acquisition and Preprocessing
2.2 Real Time Phase Estimation Algorithm
2.3 Performance Indexes
3 Results
4 Discussion
5 Conclusions and Future Development
References
Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models
1 Introduction
2 Methods
2.1 CNN Segmentation of Peripheral Nerve Cross-sectional Images
2.2 Nerve Image Selection
2.3 Model Building and Admittance Method
3 Results
3.1 Current Distribution Inside Two Nerve Models
3.2 Current Density in Different Nerve Components
4 Discussion
References
Statistical and Symbolic Neuroaesthetics Rules Extraction from EEG Signals
1 Introduction
2 Data Origin, Description, and Preparation
3 Statistical Analysis Phase
4 Knowledge Extraction
5 Conclusions
References
Brain Shape Correspondence Analysis Using Variational Mixtures for Gaussian Process Latent Variable Models
1 Introduction
2 Materials and Methods
2.1 Normalized Geodesic Error for Evaluation Measure
2.2 Scale-invariant Heat Kernel Signature (SI-HKS)
2.3 Mixtures for Gaussian Process Latent Variable Models
2.4 Variational Inference
2.5 The Evidence Lower Bound (ELBO)
3 Results
3.1 Tosca Non-rigid World Dataset
3.2 SHREC'16 Dataset
3.3 Brain Structure Dataset
4 Conclusions
References
Explainable Artificial Intelligence to Detect Breast Cancer: A Qualitative Case-Based Visual Interpretability Approach
1 Introduction
2 Materials and Methods
3 Results
3.1 INbreast
3.2 MNIST
4 Discussion
5 Conclusions
References
Evaluation of a Gaussian Mixture Model for Generating Synthetic ECG Signals During an Angioplasty Procedure
1 Introduction
2 Materials and Methods
2.1 Database
2.2 Pre-processing
2.3 Inclusion and Exclusion Criteria
2.4 ST Classification Criteria
2.5 Gaussian Mixture Model
2.6 Model Training
2.7 Validation
3 Results
4 Discussion
5 Conclusions
References
Automatic Left Bundle Branch Block Diagnose Using a 2-D Convolutional Network
1 Introduction
2 Materials and Methods
3 Results
4 Discussion and Conclusions
References
QRS-T Angle as a Biomarker for LBBB Strict Diagnose
1 Introduction
2 Materials and Methods
3 Statistical Analysis
4 Results
4.1 QRS-T Angles
4.2 Classification Results
5 Discussion and Conclusions
References
Variable Embedding Based on L-statistic for Electrocardiographic Signal Analysis
1 Introduction
2 Materials and Methods
2.1 Electrocardiography Databases
2.2 Phase Space Reconstruction
2.3 L-statistic
2.4 Proposed Variable Embedding Approach
2.5 Experimental Framework
3 Results and Discussion
4 Conclusions
References
Uniform and Non-uniform Embedding Quality Using Electrocardiographic Signals
1 Introduction
2 Materials and Methods
2.1 Databases
2.2 Preprocessing Data
2.3 Uniform Embedding
2.4 Non-uniform Embedding: Hankel Singular Value Decomposition (HSVD)
2.5 Reconstruction Quality
3 Results and Discussions
4 Conclusion
References
Decoding Lower-Limbs Kinematics from EEG Signals While Walking with an Exoskeleton
1 Introduction
2 Materials and Methods
2.1 Subjects
2.2 Procedure
2.3 Data Preprocessing
2.4 Decoding Method
3 Results and Discussion
4 Conclusions
References
EEG Signals in Mental Fatigue Detection: A Comparing Study of Machine Learning Technics VS Deep Learning
1 Introduction
References
Application of RESNET and Combined RESNET+LSTM Network for Retina Inspired Emotional Face Recognition System
1 Introduction
2 Related Works
3 Methods
3.1 Imaging Pre-processing
3.2 Deep Feature Extraction and Classification Using Residual Neural Network (RESNET)
3.3 Deep Feature Extraction and Classification Using Combined Residual Neural Network+Long Short-Term Memory (RESNET+LSTM)
4 Results
4.1 Deep Feature Extraction and Classification Using Residual Neural Network (RESNET)
4.2 4.2 Deep Feature Extraction and Classification Using Combined Residual Neural Network+Long Short-Term Memory (RESNET+LSTM)
5 Discussion
6 Conclusions
References
Author Index

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