Intro
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