Human Brain and Artificial Intelligence: Third International Workshop, HBAI 2022, Held in Conjunction with IJCAI-ECAI 2022,Vienna, Austria, July 23, 2022 Revised Selected Papers

Preface
Organization
Contents
AI for Brain Related Data Analysis
Classification of EEG Signals Based on GA-ELM Optimization Algorithm
	1 Introduction
	2 Optimization of Extreme Learning Machine by Genetic Algorithm
	3 The Experiment Design
		3.1 Experimental System Framework
		3.2 Data Acquisition
	4 The Data Analysis
		4.1 Preprocessing
		4.2 Feature Extraction
		4.3 Genetic Algorithm Optimized Parameter Setting
	5 Results
	6 Discussion
	7 Conclusion
	References
Delving into Temporal-Spectral Connections in Spike-LFP Decoding by Transformer Networks
	1 Introduction
	2 Methods
		2.1 Temporal Connection Learning with Spikes
		2.2 Spectral Connection Learning with LFPs
		2.3 Temporal-Spectral Connection Learning with Spike-LFPs
		2.4 Task-Related Output Layer
	3 Experiments and Results
		3.1 Clinical Dataset
		3.2 Spike-LFP Fusion Improves Neural Decoding Accuracy
		3.3 Temporal Connections Improve Robustness to Temporal Shifts
		3.4 Temporal-Spectral Connections Improve Robustness to Noises
	4 Conclusion
	A Detail Settings Of Neural Decoders
	B Estimating Movement Conduction Durations With Neuron Responses
	C Robustness To Gaussian Noises
	References
A Mask Image Recognition Attention Network Supervised by Eye Movement
	1 Introduction
	2 Methods
		2.1 Datasets
		2.2 The Generation of Gaze Heat Map
		2.3 Network Architecture
	3 Results
		3.1 Eye Movement Heat Map
		3.2 Network Performance
		3.3 Network Attention Visualization
	4 Conclusion
	References
DFC-SNN: A New Approach for the Recognition of Brain States by Fusing Brain Dynamics and Spiking Neural Network
	1 Introduction
	2 Methods
		2.1 DFC-SNN Framework
		2.2 Dataset
	3 Results
	4 Conclusion
	References
DSNet: EEG-Based Spatial Convolutional Neural Network for Detecting Major Depressive Disorder
	1 Introduction
	2 Materials and Methods
		2.1 Dataset and Data Preprocessing
		2.2 The Architecture of DSNet
		2.3 Baseline Methods
		2.4 Model Implementation and Experimental Evaluation
	3 Results and Discuss
	4 Conclusion
	References
SE-1DCNN-LSTM: A Deep Learning Framework for EEG-Based Automatic Diagnosis of Major Depressive Disorder and Bipolar Disorder
	1 Introduction
	2 Materials and Methods
		2.1 Data and Preprocessing
		2.2 1DCNN and LSTM Network
		2.3 Channel Attention
		2.4 Evaluation Metrics and Parameters
	3 Results and Discussion
		3.1 Comparison with Baseline Method
		3.2 Ablation Study
		3.3 Interpretability Analysis of Channel Attention
		3.4 Effects of Window Size
	4 Conclusion
	References
Emotion Recognition from EEG Using All-Convolution Residual Neural Network
	1 Introduction
	2 Methods
		2.1 Pre-processing and Feature Extraction
		2.2 3D Input Construction
		2.3 The All-Convolutional Neural Network
		2.4 Deep Residual Learning Framework
	3 Experiments
		3.1 DEAP Dataset
		3.2 Model Implementation
		3.3 Parameter Setting
	4 Experimental Results and Discussion
	5 Conclusions and Future Works
	References
Salient Object Detection with Fusion of RGB Image and Eye Tracking Data
	1 Introduction
	2 Method
		2.1 Acquisition of ETSM
		2.2 Cross-Modal Fusion Module
		2.3 Improved Cascade Decoder
		2.4 Optimization Module
	3 Experimental Setup and Result Analysis
		3.1 Experimental Details
		3.2 Evaluation Indicators
		3.3 Performance Comparison with Other Algorithms
	4 Conclusion
	References
Multi-source Domain Adaptation Based on Data Selector with Soft Actor-Critic
	1 Introduction
	2 Related Work
	3 Method
		3.1 Reinforcement Learning Data Selector
		3.2 Soft Actor-Critic
	4 Experiments
		4.1 Datasets and Experiment Settings
		4.2 Comparison with the Latest Technology
		4.3 Training Efficiency of DSAC
		4.4 Source to Single-Target Adaptation
	5 Conclusion
	References
Transfer Learning to Decode Brain States Reflecting the Relationship Between Cognitive Tasks
	1 Introduction
	2 Related Work
		2.1 Cognitive Task Relations from Neuroscience Perspective
		2.2 Cognitive Task Relations From Transfer Learning Perspective
	3 Methods
		3.1 HCP Tasks
		3.2 Transfer Learning
		3.3 Validation of Cognitive Taskonomy
	4 Results
		4.1 Affinity Matrix of Cognitive Tasks
		4.2 Compare with Task-Specific Networks
		4.3 Brain Decoding Accuracy with Transfer Learning
	5 Discussion
	References
AI and Brain Interface
Brain Network Analysis of Hand Motor Execution and Imagery Based on Conditional Granger Causality
	1 Introduction
	2 Methods
		2.1 Conditional Granger Causality Analysis
	3 Data Collection and Processing
		3.1 Data Collection
		3.2 Data Processing
	4 Result
	5 Discussion
	References
A Hybrid Brain-Computer Interface for Smart Car Control
	1 Introduction
	2 Materials and Methods
		2.1 System Overview
		2.2 Signal Acquisition
	3 Experiments and Results
		3.1 Experiment I: Single-Mode Control
		3.2 Experiment II: Multimodal Car Control
		3.3 Results
	4 Conclusions
	References
A Spiking Neural Network for Brain-Computer Interface of Four Classes Motor Imagery
	1 Introduction
	2 Methods
		2.1 BSA Based on Parameter-Wise Gradient Descent Optimization Method
		2.2 LIF Model and ALIF Model
		2.3 The Architecture of SNN
		2.4 Surrogate Gradient
		2.5 Channel-Wise Normalization
	3 Experiments
		3.1 Dataset and the Selection of Experimental Data
		3.2 The Performance of PW-GD Optimizing for BSA
		3.3 The Performance of SNN in MI Classification
		3.4 Comparison of Training Effects of MG and Slayer
	4 Conclusion
	References
Virtual Drone Control Using Brain-Computer Interface Based on Motor Imagery Brain Magnetic Fields
	1 Introduction
	2 Brain Magentic Fields Based BCI System Description
		2.1 Helmet Design
		2.2 Experimental Environment Construction
	3 Experiment Content and Data Processing
		3.1 Subject Training
		3.2 Data Collection and Processing
	4 Machine Learning and Result
		4.1 Data for Machine Learning
		4.2 Model Training
		4.3 Test Result
		4.4 Virtual Drone Controlling
	5 Conclusion and Future Work
	References
Brain Controlled Manipulator System Based on Improved Target Detection and Augmented Reality Technology
	1 Introduction
	2 Experimental and System Structure Design
		2.1 Experimental Design
		2.2 System Structure Design
	3 Methods
		3.1 Improvement of Faster-RCNN
		3.2 AR Technology Generates Stimulation Interface
		3.3 EEG Signal Analysis
	4 Experimental results
		4.1 Target Detection Model Test
		4.2 EEG Recognition Results Using AR
		4.3 System Test
	5 Concludes
	References
Optimization of Stimulus Color for SSVEP-Based Brain-Computer Interfaces in Mixed Reality
	1 Introduction
		1.1 A Subsection Sample
	2 Methods and Materials
		2.1 Experimental Protocol
		2.2 Participants
		2.3 EEG Acquisition and Data Pre-processing
		2.4 Classification Algorithm
		2.5 Calculation of Color Contrast
	3 Result and Analysis
	4 Conclusion
	References
Brain Related Research
White Matter Maturation and Hemispheric Asymmetry During Childhood Based on Chinese Population
	1 Introduction
	2 Method
		2.1 Participants
		2.2 Image Acquisition
		2.3 Image Analysis
		2.4 Tractography Regions of Interest
		2.5 Statistical Analysis
	3 Results
		3.1 Age-Related White Matter Maturation
		3.2 Asymmetry Effects
	4 Discussion
		4.1 Age-Related White Matter Maturation
		4.2 Asymmetry Effects
	5 Conclusions
	References
A Digital Gaming Intervention Combing Multitasking and Alternating Attention for ADHD: A Preliminary Study
	1 Introduction
	2 Methods
		2.1 Trial Design
		2.2 Participants
		2.3 Randomization and Blinding
		2.4 Interventions
		2.5 Outcomes
		2.6 Data Analysis
	3 Results
		3.1 Study Participation
		3.2 Primary Outcomes
		3.3 Secondary Outcomes
		3.4 Adverse Events
	4 Discussion
	5 Conclusion
	References
A BCI Speller with 120 Commands Encoded by Hybrid P300 and SSVEP Features
	1 Introduction
	2 Methods and Experiments
		2.1 Subjects
		2.2 Hybrid Paradigm Design and Implementation
		2.3 BCI Experiment
		2.4 EEG Recording and Processing
		2.5 Classification Algorithm and Decision Fusion Method
		2.6 System Performance Evaluation
	3 Results and Discussion
		3.1 Subjects EEG Feature Analysis
		3.2 Offline BCI Performance Analysis
		3.3 Online BCI Performance Analysis
	4 Conclusion
	References
Author Index