The two volume set LNCS 13258 and 13259 constitutes the proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, held in Puerto de la Cruz, Tenerife, Spain in May – June 2022. The total of 121 contributions was carefully reviewed and selected from 203 submissions. The papers are organized in two volumes, with the following topical sub-headings:   Part I: Machine Learning in Neuroscience; Neuromotor and Cognitive Disorders; Affective Analysis; Health Applications, Part II: Affective Computing in Ambient Intelligence; Bioinspired Computing Approaches; Machine Learning in Computer Vision and Robot; Deep Learning; Artificial Intelligence Applications.

lgli/Artificial Intelligence in Neuroscience Affective Analysis and Health Applications.pdf

lgrsnf/Artificial Intelligence in Neuroscience Affective Analysis and Health Applications.pdf

zlib/Science (General)/International Conferences and Symposiums/José Manuel Ferrández Vicente, José Ramón Álvarez-Sánchez, Félix de la Paz López, Hojjat Adeli/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 Proce_23607289.pdf

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 1, Artificial intelligence in neuroscience : affective analysis and health applications

Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications: 9th International Work-Conference on the Interplay Between ... I (Lecture Notes in Computer Science, 13258)

International Work-Conference on the Interplay Between Natural and Artificial Computation

Moritz Kappler

Springer International Publishing AG

Springer Nature Switzerland AG

Lecture Notes in Computer Science, 1st ed. 2022, Cham, Cham, 2022

Lecture notes in computer science, Cham, Switzerland, 2022

Lecture notes in computer science, Part 1, Cham, 2022

Springer Nature, Cham, 2022

Switzerland, Switzerland

2, 20220524

{"container_title":"Lecture Notes in Computer Science","isbns":["3031062418","3031062426","9783031062414","9783031062421"],"issns":["0302-9743","1611-3349"],"last_page":674,"publisher":"Springer","series":"Lecture Notes in Computer Science, 13258"}

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

Lecture Notes in Computer Science
Erscheinungsdatum: 19.05.2022

2022-11-11