Brain neural networks characterize various information propagation patterns for different emotional states. However, the statistical features based on traditional graph theory may ignore the spacial network difference. To reveal these inherent spatial features and increase the stability of emotional recognition, we proposed a hierarchical framework that can perform the multiple emotion recognitions with the multiple emotion-related spatial network topology patterns (MESNP) by combining a supervised learning with ensemble co-decision strategy. To evaluate the performance of our proposed MESNP approach, we conduct both off-line and simulated on-line experiments with two public datasets i.e., MAHNOB and DEAP. The experiment results demonstrated that MESNP can significantly enhance the classification performance for the multiple emotions. The highest accuracies of off-line experiments for MAHNOB-HCI and DEAP achieved 99.93% (3 classes) and 83.66% (4 classes), respectively. For simulated on-line experiments, we also obtained the best classification accuracies with 100% (3 classes) for MAHNOB and 99.22% (4 classes) for DEAP by proposed MESNP. These results further proved the efficiency of MESNP for structured feature extraction in mult-classification emotional task.
Multi-view Multi-label Fine-grained Emotion Decoding from Human Brain Activity
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
Variation is the Norm: Brain State Dynamics Evoked By Emotional Video Clips
Northeastern University（东北大学）；Victoria University of Wellington（惠灵顿维多利亚大学）；University of Miami（迈阿密大学）；University of Arizona（亚利桑那大学）；Dartmouth College（达特茅斯学院）；Massachusetts General Hospital（麻省总医院）
Energy Landscapes of Emotion: Quantifying Brain Network Stability During Happy and Sad Face Processing Using EEG-Based Hopfield Energy
College of Information and Control Engineering, Xi'an University of Architecture and Technology（西安建筑科技大学信息与控制工程学院）；Department of Neurosurgery, PLA General Hospital of Central Theater Command（中部战区总医院神经外科）
A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence
Tulane University（杜兰大学）；University of Nebraska Medical Center（内布拉斯加大学医学中心）；Mind Research Network（心智研究网络）；Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（三机构神经影像与数据科学转化研究中心（TReNDS））；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
A Deep-Learning-Based Neural Decoding Framework for Emotional Brain-Computer Interfaces
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions（深港脑科学研究院-深圳基础研究机构）；Shenzhen Technological Research Center for Primate Translational Medicine（深圳灵长类转化医学技术研究中心）；University of Chinese Academy of Sciences（中国科学院大学）
Interpretable Dual-Filter Fuzzy Neural Networks for Affective Brain-Computer Interfaces
University of Technology Sydney（悉尼科技大学）；Australian AI Institute（澳大利亚人工智能研究所）；Henan University（河南大学）；Indian Statistical Institute（印度统计研究所）；Chinese Academy of Sciences（中国科学院）
EmoMind: Decoding Affective Captions from Human Brain fMRI
Department of Biomedical Engineering（生物医学工程系）；Vanderbilt University（范德比大学）；Research Institute of Electrical Communication（电气通信研究所）；Tohoku University（东北大学）；University of Florida（佛罗里达大学）
New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（三校神经影像与数据科学转化研究中心（TReNDS））；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of the Chinese Academy of Sciences（中国科学院大学）；Stanford University（斯坦福大学）；Beijing Normal University（北京师范大学）；Institute of Biophysics, Chinese Academy of Sciences（中国科学院生物物理研究所）
