Emotion recognition (ER) technology is an integral part for developing innovative applications such as drowsiness detection and health monitoring that plays a pivotal role in contemporary society. This study delves into ER using electroencephalography (EEG), within immersive virtual reality (VR) environments. There are four main stages in our proposed methodology including data acquisition, pre-processing, feature extraction, and emotion classification. Acknowledging the limitations of existing 2D datasets, we introduce a groundbreaking 3D VR dataset to elevate the precision of emotion elicitation. Leveraging the Interaxon Muse headband for EEG recording and Oculus Quest 2 for VR stimuli, we meticulously recorded data from 40 participants, prioritizing subjects without reported mental illnesses. Pre-processing entails rigorous cleaning, uniform truncation, and the application of a Savitzky-Golay filter to the EEG data. Feature extraction encompasses a comprehensive analysis of metrics such as power spectral density, correlation, rational and divisional asymmetry, and power spectrum. To ensure the robustness of our model, we employed a 10-fold cross-validation, revealing an average validation accuracy of 85.54\%, with a noteworthy maximum accuracy of 90.20\% in the best fold. Subsequently, the trained model demonstrated a commendable test accuracy of 82.03\%, promising favorable outcomes.
From Segments to Trajectories: Evolving Affective Graphs with Evidence Retrieval for Continuous EEG Emotion Recognition
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Intelligent Science and Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）智能科学与工程学院）；School of Public Health, National University of Singapore（新加坡国立大学公共卫生学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Beihang University（北京航空航天大学）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）
School of Information Science and Technology, Beijing University of Technology（信息科学与技术学院，北京理工大学）；School of Artificial Intelligence, Anhui University（人工智能学院，安徽大学）
DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis
School of Biomedical Engineering, Medical School, Shenzhen University（深圳大学医学院生物医学工程学院）；Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging（广东省生物医学测量与超声成像重点实验室）
REFS: Robust EEG feature selection with missing multi-dimensional annotation for emotion recognition
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China（信息科学与技术学院，北京理工大学，北京）；School of Artificial Intelligence, Anhui University, Beijing 100124, China（人工智能学院，安徽大学，北京）
Learning from Brain Topography: A Hierarchical Local-Global Graph-Transformer Network for EEG Emotion Recognition
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, the School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院教育部智能感知与图像理解重点实验室）；Fourth Military Medical University（第四军医大学）；Beijing Institute of Mechanical Equipment（北京机械工业研究所）
See the Emotion: A Facial Emoji Proxy Modeling for EEG Emotion Recognition
Hefei University of Technology（合肥工业大学）；PLA Information Engineering University（中国人民解放军信息工程大学）；University of Science and Technology of China（中国科学技术大学）；MBZUAI
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)（南洋理工大学-不列颠哥伦比亚大学活跃老年生活卓越联合研究中心（LILY））；Nanyang Technological University（南洋理工大学）；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-南洋理工大学新加坡联合研究院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognition
Shenzhen University（深圳大学）；Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging（广东省生物医学测量与超声成像重点实验室）；City University of Hong Kong（香港城市大学）；Hong Kong Center for Cerebro-Cardiovascular Health Engineering（香港心脑血管健康工程中心）
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学电子计算学系）
CMCRD: Cross-Modal Contrastive Representation Distillation for Emotion Recognition
Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（图像处理与智能控制重点实验室、教育部、自动化学院、华中科技大学）；Wuhan Institute of Digital Engineering（武汉数字工程研究所）；Shanghai Jiao Tong University（上海交通大学）
School of Biomedical Engineering, Medical School, Shenzhen University（深圳大学医学院生物医学工程学院）；Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging（广东省生物医学测量与超声成像重点实验室）；Institute of Computing and Intelligence, Harbin Institute of Technology（哈尔滨工业大学计算与智能研究院）；Peng Cheng Laboratory（鹏城实验室）；Shenzhen Mental Health Center, Shenzhen Kangning Hospital（深圳市精神卫生中心（深圳市康宁医院））
