Subject-independent EEG emotion recognition is challenged by pronounced inter-subject variability and the difficulty of learning robust representations from short, noisy recordings. To address this, we propose a fusion framework that integrates (i) local, channel-wise descriptors and (ii) global, trial-level descriptors, improving cross-subject generalization on the SEED-VII dataset. Local representations are formed per channel by concatenating differential entropy with graph-theoretic features, while global representations summarize time-domain, spectral, and complexity characteristics at the trial level. These representations are fused in a dual-branch transformer with attention-based fusion and domain-adversarial regularization, with samples filtered by an intensity threshold. Experiments under a leave-one-subject-out protocol demonstrate that the proposed method consistently outperforms single-view and classical baselines, achieving approximately 40% mean accuracy in 7-class subject-independent emotion recognition. The code has been released at https://github.com/Danielz-z/LGF-EEG-Emotion.
Spatiotemporal EEG-Based Emotion Recognition Using SAM Ratings from Serious Games with Hybrid Deep Learning
Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences（计算机科学、电气工程与数学科学系，西挪威应用科学大学）
University of Canberra（堪培拉大学）；Kuwait College of Science and Technology（科威特科技学院）
While Parkinson's disease (PD) is typically characterized by motor disorder, there is evidence of diminished emotion perception in PD patients. This study examines the utility of affective Electroencephalography (EEG) signals to understand emotional differences between PD vs Healthy Controls (HC), and for automated PD detection. Employing traditional machine learning and deep learning methods, we explore (a) dimensional and categorical emotion recognition, and (b) PD vs HC classification from emotional EEG signals. Our results reveal that PD patients comprehend arousal better than valence, and…
HwaMei Hospital, University of Chinese Academy of Sciences（中国科学院大学华美医院）；Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences（中国科学院大学宁波生命与健康产业研究院）；Anhui University（安徽大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
As an essential element for the diagnosis and rehabilitation of psychiatric disorders, the electroencephalogram (EEG) based emotion recognition has achieved significant progress due to its high precision and reliability. However, one obstacle to practicality lies in the variability between subjects and sessions. Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation t…
Previous electroencephalogram (EEG) emotion recognition relies on single-task learning, which may lead to overfitting and learned emotion features lacking generalization. In this paper, a graph-based multi-task self-supervised learning model (GMSS) for EEG emotion recognition is proposed. GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks. By learning from multiple tasks simultaneously, GMSS can find a representation that captures all of the tasks thereby d…
Xidian University（西安电子科技大学）；Southeast University（东南大学）；Beijing Institute of Mechanical Equipment（北京机械设备研究所）
Studies in the area of neuroscience have revealed the relationship between emotional patterns and brain functional regions, demonstrating that dynamic relationships between different brain regions are an essential factor affecting emotion recognition determined through electroencephalography (EEG). Moreover, in EEG emotion recognition, we can observe that clearer boundaries exist between coarse-grained emotions than those between fine-grained emotions, based on the same EEG data; this indicates the concurrence of large coarse- and small fine-grained emotion variations. Thus, the progressive cl…
Lanzhou University（兰州大学）；Beijing Institute of Technology（北京理工大学）；Inner Mongolia University（内蒙古大学）；Shandong Academy of Intelligent Computing Technology（山东智能计算技术研究院）；Chinese Academy of Sciences（中国科学院）
Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results. Facing the situation that lacks EEG information decoding research, we first analyzed the impact of different EEG information(individual, session, emotion and trial) for emotion recognition by sample space visualization, sample aggregation phenomena quantification, and energy pattern analysis on five public datasets. Based on these phenomena and patterns, we provided the processing methods a…
Ping An Technology (Shenzhen) Co., Ltd.（平安科技（深圳）有限公司）
Using deep learning methods to classify EEG signals can accurately identify people's emotions. However, existing studies have rarely considered the application of the information in another domain's representations to feature selection in the time-frequency domain. We propose a classification network of EEG signals based on the cross-domain feature fusion method, which makes the network more focused on the features most related to brain activities and thinking changes by using the multi-domain attention mechanism. In addition, we propose a two-step fusion method and apply these methods to the …
A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院）山东省计算中心（国家超级计算济南中心）算力网络与信息安全教育部重点实验室）；Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science（山东省计算机网络重点实验室、山东省计算机科学基础研究中心）；Institute of Engineering Medicine, Beijing Institute of Technology（北京理工大学医学工程学院）
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）
Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and spa…
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）
The data scarcity problem in Electroencephalography (EEG) based affective computing results into difficulty in building an effective model with high accuracy and stability using machine learning algorithms especially deep learning models. Data augmentation has recently achieved considerable performance improvement for deep learning models: increased accuracy, stability, and reduced over-fitting. In this paper, we propose a novel data augmentation framework, namely Generative Adversarial Network-based Self-supervised Data Augmentation (GANSER). As the first to combine adversarial training with …
Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Zhengzhou University of Light Industry（郑州轻工业大学）；Nanjing University（南京大学）；Aalto University（阿尔托大学）；Beijing Institute of Technology（北京理工大学）
Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review…
Learning Domain- and Class-Disentangled Prototypes for Domain-Generalized EEG Emotion Recognition
School of Biological Science and Medical Engineering, Hunan University of Technology（湖南理工大学生物科学与医学工程学院）；School of Biomedical Engineering, Health Science Center, Shenzhen University（深圳大学生物医学工程学院）；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）；Shenzhen Pengrui Brain Science Technology（深圳鹏瑞脑科学科技）
Electroencephalograph (EEG) emotion recognition is a significant task in the brain-computer interface field. Although many deep learning methods are proposed recently, it is still challenging to make full use of the information contained in different domains of EEG signals. In this paper, we present a novel method, called four-dimensional attention-based neural network (4D-aNN) for EEG emotion recognition. First, raw EEG signals are transformed into 4D spatial-spectral-temporal representations. Then, the proposed 4D-aNN adopts spectral and spatial attention mechanisms to adaptively assign the …
Dr. Ing. h.c. F. Porsche AG（保时捷股份公司）；Ludwig Maximilian University（路德维希-马克西米利安大学）；University of Tübingen（蒂宾根大学）；Chemnitz University of Technology（开姆尼茨工业大学）；Silicon Austria Labs（奥地利硅谷实验室）
There is a growing need for sparse representational formats of human affective states that can be utilized in scenarios with limited computational memory resources. We explore whether representing neural data, in response to emotional stimuli, in a latent vector space can serve to both predict emotional states as well as generate synthetic EEG data that are participant- and/or emotion-specific. We propose a conditional variational autoencoder based framework, EEG2Vec, to learn generative-discriminative representations from EEG data. Experimental results on affective EEG recording datasets demo…
EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition
Institute of Life and Health Industry, University of Chinese Academy of Sciences（中国科学院大学生命与健康产业研究院）；University of Hong Kong（香港大学）；Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所类脑智能研究中心）
The high temporal resolution and the asymmetric spatial activations are essential attributes of electroencephalogram (EEG) underlying emotional processes in the brain. To learn the temporal dynamics and spatial asymmetry of EEG towards accurate and generalized emotion recognition, we propose TSception, a multi-scale convolutional neural network that can classify emotions from EEG. TSception consists of dynamic temporal, asymmetric spatial, and high-level fusion layers, which learn discriminative representations in the time and channel dimensions simultaneously. The dynamic temporal layer consi…
University of Electronic Science and Technology of China（电子科技大学）
Emotion recognition based on EEG (electroencephalography) has been widely used in human-computer interaction, distance education and health care. However, the conventional methods ignore the adjacent and symmetrical characteristics of EEG signals, which also contain salient information related to emotion. In this paper, a spatial folding ensemble network (SFE-Net) is presented for EEG feature extraction and emotion recognition. Firstly, for the undetected area between EEG electrodes, an improved Bicubic-EEG interpolation algorithm is developed for EEG channels information completion, which all…
Khalifa University（哈利法大学）；Università degli Studi di Milano（米兰大学）
The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers' perspective. Human emotion evaluation using EEG signals has consistently attracted a lot of research attention. The identification of human emotional states based on EEG signals is effective to detect potential internal threats caused by insider individuals. Nevertheless, EEG signal-based human emotion evaluation systems have shown several vulnerab…
College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；Institute of Wenzhou, Zhejiang University（浙江大学温州市研究院）；College of Mathematical Medicine, Zhejiang Normal University（浙江师范大学数学医学学院）；Key Laboratory for Biomedical Engineering of MOE of China, Department of Biomedical Engineering, Zhejiang University（中国教育部生物医学工程重点实验室，浙江大学生物医学工程学院）
Shenzhen University（深圳大学）；Kyoto University（京都大学）；Peng Cheng Laboratory（鹏城实验室）；ATR Neural Information Analysis Laboratories（ATR神经信息分析实验室）
How to effectively and efficiently extract valid and reliable features from high-dimensional electroencephalography (EEG), particularly how to fuse the spatial and temporal dynamic brain information into a better feature representation, is a critical issue in brain data analysis. Most current EEG studies work in a task driven manner and explore the valid EEG features with a supervised model, which would be limited by the given labels to a great extent. In this paper, we propose a practical hybrid unsupervised deep convolutional recurrent generative adversarial network based EEG feature charact…
Electroencephalogram (EEG) is one of the most reliable physiological signal for emotion detection. Being non-stationary in nature, EEGs are better analysed by spectro temporal representations. Standard features like Discrete Wavelet Transformation (DWT) can represent temporal changes in spectral dynamics of an EEG, but is insufficient to extract information other way around, i.e. spectral changes in temporal dynamics. On the other hand, Empirical mode decomposition (EMD) based features can be useful to bridge the above mentioned gap. Towards this direction, we extract two novel features on top…
National Institute of Technology, Silchar（锡尔查国家理工学院）；University of Calgary（卡尔加里大学）
Emotion recognition using EEG has been widely studied to address the challenges associated with affective computing. Using manual feature extraction methods on EEG signals results in sub-optimal performance by the learning models. With the advancements in deep learning as a tool for automated feature engineering, in this work, a hybrid of manual and automatic feature extraction methods has been proposed. The asymmetry in different brain regions is captured in a 2D vector, termed the AsMap, from the differential entropy features of EEG signals. These AsMaps are then used to extract features aut…
Shanghai Jiao Tong University（上海交通大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity networks involving emotion are not well investigated. Previous studies on emotion recognition based on electroencephalography (EEG) signals mainly rely on single-channel-based feature extraction methods. In this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centra…
Northwestern Polytechnic University（西北工业大学）；Shaanxi Normal University（陕西师范大学）
One of the most significant challenges of EEG-based emotion recognition is the cross-subject EEG variations, leading to poor performance and generalizability. This paper proposes a novel EEG-based emotion recognition model called the domain adversarial graph attention model (DAGAM). The basic idea is to generate a graph to model multichannel EEG signals using biological topology. Graph theory can topologically describe and analyze relationships and mutual dependency between channels of EEG. Then, unlike other graph convolutional networks, self-attention pooling is applied to benefit salient EE…
EEG signals have been reported to be informative and reliable for emotion recognition in recent years. However, the inter-subject variability of emotion-related EEG signals still poses a great challenge for the practical applications of EEG-based emotion recognition. Inspired by recent neuroscience studies on inter-subject correlation, we proposed a Contrastive Learning method for Inter-Subject Alignment (CLISA) to tackle the cross-subject emotion recognition problem. Contrastive learning was employed to minimize the inter-subject differences by maximizing the similarity in EEG signal represen…
Use of the electroencephalogram (EEG) and machine learning approaches to recognize emotions can facilitate affective human computer interactions. However, the type of EEG data constitutes an obstacle for cross-individual EEG feature modelling and classification. To address this issue, we propose a deep-learning framework denoted as a dynamic entropy-based pattern learning (DEPL) to abstract informative indicators pertaining to the neurophysiological features among multiple individuals. DEPL enhanced the capability of representations generated by a deep convolutional neural network by modelling…
University of Shanghai for Science and Technology（上海理工大学）
Technique of emotion recognition enables computers to classify human affective states into discrete categories. However, the emotion may fluctuate instead of maintaining a stable state even within a short time interval. There is also a difficulty to take the full use of the EEG spatial distribution due to its 3-D topology structure. To tackle the above issues, we proposed a locally temporal-spatial pattern learning graph attention network (LTS-GAT) in the present study. In the LTS-GAT, a divide-and-conquer scheme was used to examine local information on temporal and spatial dimensions of EEG p…
A novel Fourier Adjacency Transformer for advanced EEG emotion recognition
Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；School of Artificial Intelligence and Advanced Computing, Xi’an Jiaotong-Liverpool University（西交利物浦大学人工智能与先进计算学院）
This paper proposes a novel two-stage framework for emotion recognition using EEG data that outperforms state-of-the-art models while keeping the model size small and computationally efficient. The framework consists of two stages; the first stage involves constructing efficient models named EEGNet, which is inspired by the state-of-the-art efficient architecture and employs inverted-residual blocks that contain depthwise separable convolutional layers. The EEGNet models on both valence and arousal labels achieve the average classification accuracy of 90%, 96.6%, and 99.5% with only 6.4k, 14k,…
MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition
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）；School of Biological Science and Medical Engineering, Southeast University（东南大学生物科学与医学工程学院）
School of Integrated Technology, Yonsei University（延世大学融合技术学院）
Emotion recognition based on electroencephalography (EEG) has received attention as a way to implement human-centric services. However, there is still much room for improvement, particularly in terms of the recognition accuracy. In this paper, we propose a novel deep learning approach using convolutional neural networks (CNNs) for EEG-based emotion recognition. In particular, we employ brain connectivity features that have not been used with deep learning models in previous studies, which can account for synchronous activations of different brain regions. In addition, we develop a method to ef…
Beijing University of Technology（北京工业大学）；Beijing-Dublin International College（北京都柏林国际学院）
The progress of EEG-based emotion recognition has received widespread attention from the fields of human-machine interactions and cognitive science in recent years. However, how to recognize emotions with limited labels has become a new research and application bottleneck. To address the issue, this paper proposes a Self-supervised Group Meiosis Contrastive learning framework (SGMC) based on the stimuli consistent EEG signals in human being. In the SGMC, a novel genetics-inspired data augmentation method, named Meiosis, is developed. It takes advantage of the alignment of stimuli among the EEG…
Indian Institute of Information Technology Allahabad（印度阿拉哈巴德信息技术学院）
Emotion recognition using EEG signals is an emerging area of research due to its broad applicability in BCI. Emotional feelings are hard to stimulate in the lab. Emotions do not last long, yet they need enough context to be perceived and felt. However, most EEG-related emotion databases either suffer from emotionally irrelevant details (due to prolonged duration stimulus) or have minimal context doubting the feeling of any emotion using the stimulus. We tried to reduce the impact of this trade-off by designing an experiment in which participants are free to report their emotional feelings simu…
Convolutional Neural Networks(CNNs) has achieved remarkable performance breakthrough in a variety of tasks. Recently, CNNs based methods that are fed with hand-extracted EEG features gradually produce a powerful performance on the EEG data based emotion recognition task. In this paper, we propose a novel convolutional layer allowing to adaptively extract effective data-driven spectrogram-like features from raw EEG signals, which we reference as scaling layer. Further, it leverages convolutional kernels scaled from one data-driven pattern to exposed a frequency-like dimension to address the sho…
Graph Neural Networks in EEG-based Emotion Recognition: A Survey
Nanyang Technological University, Singapore（南洋理工大学）；Xi’an Jiaotong University, Xi’an, China（西安交通大学）；Imperial College London, London, UK（伦敦帝国理工学院）；Amazon, Seattle, WA, USA（亚马逊）；Carnegie Mellon University, Pittsburgh, PA, USA（卡内基梅隆大学）；Uber Technologies, Inc., USA（Uber Technologies, Inc.）；School of Computer Science, Central China Normal University, Wuhan, China（中央财经大学计算机学院）；Institute of Automation, Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所）
University of Trento（特伦托大学）；Mila - Quebec Artifical Intelligence Institute（Mila -魁北克人工智能研究所）；StonyBrook University（石溪大学）
Machine learning methods, such as deep learning, show promising results in the medical domain. However, the lack of interpretability of these algorithms may hinder their applicability to medical decision support systems. This paper studies an interpretable deep learning technique, called SincNet. SincNet is a convolutional neural network that efficiently learns customized band-pass filters through trainable sinc-functions. In this study, we use SincNet to analyze the neural activity of individuals with Autism Spectrum Disorder (ASD), who experience characteristic differences in neural oscillat…
Queen’s University（女王大学）；Ingenuity Labs Research Institute（英格纽蒂实验室研究院）
We propose PARSE, a novel semi-supervised architecture for learning strong EEG representations for emotion recognition. To reduce the potential distribution mismatch between the large amounts of unlabeled data and the limited amount of labeled data, PARSE uses pairwise representation alignment. First, our model performs data augmentation followed by label guessing for large amounts of original and augmented unlabeled data. This is then followed by sharpening of the guessed labels and convex combinations of the unlabeled and labeled data. Finally, representation alignment and emotion classifica…
