A review of Deep Learning based methods for Affect Analysis using Physiological Signals

Article Neurosciences

Fused CNN-LSTM deep learning emotion recognition model using electroencephalography signals

Munaza Ramzan et al.

Summary: In this study, deep learning models were used to classify emotions based on EEG signals. The fusion of CNN and LSTM-RNN models achieved high accuracies in various emotion databases.

INTERNATIONAL JOURNAL OF NEUROSCIENCE (2023)

Add to Collection

Article Computer Science, Artificial Intelligence

EEG-Based Emotion Recognition via Neural Architecture Search

Chang Li et al.

Summary: In this paper, a novel neural architecture search (NAS) framework based on reinforcement learning (RL) is proposed for EEG-based emotion recognition. The proposed NAS can automatically design network architectures. Experimental results show that this method outperforms the state-of-the-art CNN-based methods in emotion recognition.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2023)

Add to Collection

Article Computer Science, Artificial Intelligence

Behavioral and Physiological Signals-Based Deep Multimodal Approach for Mobile Emotion Recognition

Kangning Yang et al.

Summary: This paper proposes a novel attention-based LSTM system that combines sensors from a smartphone and a wristband to accurately determine user's emotional states. The system achieved an average accuracy of 89.2% for binary positive and negative emotion classification in a user study.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2023)

Add to Collection

Article Computer Science, Artificial Intelligence

Automated Video Classification System Driven by Characteristics of Emotional Human Brainwaves Caused by Audiovisual Stimuli

Dong-Ki Jeong et al.

Summary: This article proposes a method that automatically classifies videos into human emotional categories by imitating human neural processes. Sophisticated emotional features are extracted from EEG signals generated while a subject watches a video, and the mapping relationship between the emotional EEG features and the audiovisual features of the videos is learned. The proposed method outperforms existing methods with an average accuracy of about 95% for classifying four emotional classes of the video, and can be generalized to various audiovisual data sets.

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS (2023)

Add to Collection

Article Engineering, Electrical & Electronic

Coronary Artery Heart Disease Prediction: A Comparative Study of Computational Intelligence Techniques

Safial Islam Ayon et al.

Summary: This study compared several computational intelligence techniques for predicting coronary artery heart disease and found that the deep neural network achieved the highest accuracy of 98.15% with sensitivity and precision of 98.67% and 98.01% respectively. This outperformed previous studies in heart disease prediction.

IETE JOURNAL OF RESEARCH (2022)

Add to Collection

Article Neurosciences

4D attention-based neural network for EEG emotion recognition

Guowen Xiao et al.

Summary: This paper proposes a four-dimensional attention-based neural network (4D-aNN) for EEG emotion recognition. By transforming raw EEG signals into 4D spatial-spectral-temporal representations and utilizing attention mechanisms, the model achieves state-of-the-art performances.

COGNITIVE NEURODYNAMICS (2022)

Add to Collection

Article Engineering, Biomedical

A gradient-based automatic optimization CNN framework for EEG state recognition

He Wang et al.

Summary: This paper proposes a model architecture for EEG signal analysis using a gradient-based neural architecture search algorithm. The results show that the proposed model achieves competitive accuracy and better standard deviation compared to existing methods in emotion recognition and driver drowsiness assessment tasks.

JOURNAL OF NEURAL ENGINEERING (2022)

Add to Collection

Article Mathematical & Computational Biology

Emotion Recognition Based on Dynamic Energy Features Using a Bi-LSTM Network

Meili Zhu et al.

Summary: This study proposes a deep network model based on dynamic energy features for emotion recognition in EEG signals. Experimental results show that the proposed method achieves high accuracy on the SEED and DEAP datasets.

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE (2022)

Add to Collection

Article Computer Science, Information Systems

Investigating of Deaf Emotion Cognition Pattern By EEG and Facial Expression Combination

Yi Yang et al.

Summary: With the development of sensor technology and learning algorithms, multimodal emotion recognition has become a popular research topic. Existing studies have mainly focused on emotion recognition in normal individuals, but there is a greater need for emotion recognition in deaf individuals who cannot express emotions through words. This paper proposes a method using deep belief network (DBN) to classify emotions based on electroencephalograph (EEG) and facial expressions in deaf subjects. The results show a classification accuracy of 99.92% with feature selection in deaf emotion recognition.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Add to Collection

Article Engineering, Biomedical

Recognition of emotional states using frequency effective connectivity maps through transfer learning approach from electroencephalogram signals

Sara Bagherzadeh et al.

Summary: This study proposes a novel combined emotion recognition (ER) schema based on sophisticated frequency effective connectivity (FEC) maps and transfer learned convolutional neural network (CNN) models. The experimental results show that the proposed EC-based CNN model achieves high accuracy in recognizing emotional states from EEG signals.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Add to Collection

Article Engineering, Biomedical

Subject independent emotion recognition using EEG signals employing attention driven neural networks

Arjun et al.

Summary: In this study, a deep learning framework capable of subject-independent emotion recognition using EEG data was proposed. The framework consisted of an unsupervised LSTM autoencoder and a CNN attention framework, achieving high classification accuracy. The proposed method was validated using publicly available EEG datasets and surpassed state-of-the-art results in various tasks without the need for manual feature engineering.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Add to Collection

Article Biology

EEG-based emotion charting for Parkinson's disease patients using Convolutional Recurrent Neural Networks and cross dataset learning

Muhammad Najam Dar et al.

Summary: EEG-based emotion classification enables reliable and meaningful human-computer interaction, with applications in entertainment consumption, brain-computer interface, and psychological healthcare. In this study, a new architecture combining a 1D-CRNN and ELM is proposed for robust emotion detection in Parkinson's disease patients. The method achieves high accuracy across different datasets and emotions.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Add to Collection

Article Computer Science, Information Systems

Multimodal emotion recognition based on manifold learning and convolution neural network

Yong Zhang et al.

Summary: The multimodal emotion recognition task based on physiological signals is a current research hotspot. Traditional methods have limitations in feature extraction, while the proposed multimodal emotion recognition model in this paper utilizes manifold learning and convolutional neural networks to more effectively utilize the relevant information between different signals for emotion recognition.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

Add to Collection

Article Chemistry, Analytical

Emotion Recognition Using a Reduced Set of EEG Channels Based on Holographic Feature Maps

Ante Topic et al.

Summary: This study develops a model to recognize emotions from EEG signals in the construction of the Brain-Computer Interface (BCI) device. It finds that using holographic features and optimized electrode selection can improve the accuracy of emotion recognition, achieving better results than previous research.

SENSORS (2022)

Add to Collection

Article Chemistry, Analytical

Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM)

Mona Algarni et al.

Summary: This research presents a deep learning-based approach for emotion recognition through EEG signals, achieving high accuracy by using data selection, feature extraction, feature selection, and classification stages. It contributes to the medical field by aiding in the diagnosis of psychological and behavioral disorders. The proposed approach outperforms previous methods with high average accuracies of valence, arousal, and liking.

SENSORS (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

Linking Multi-Layer Dynamical GCN With Style-Based Recalibration CNN for EEG-Based Emotion Recognition

Guangcheng Bao et al.

Summary: This paper proposes a new model for EEG-based emotion computing, which combines graph convolutional networks and convolutional neural networks to distinguish different emotions by extracting channel connectivity features from different receptive fields and deep layer abstract features. The experiments demonstrate the effectiveness of the model and its superior performance compared to other methods.

FRONTIERS IN NEUROROBOTICS (2022)

Add to Collection

Article Biology

Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition

Yihan Wu et al.

Summary: In recent years, there has been a growing interest in emotion recognition based on electroencephalography (EEG) in the brain-computer interaction (BCI) field. This study proposed a Multi-Scales Bi-hemispheric Asymmetric Model (MSBAM) based on a convolutional neural network (CNN) structure, taking into account both the activity differences in the left and right brain hemispheres and the nonstationarity of EEG signals. The model achieved over 99% accuracy for the two-class classification of low-level and high-level states in each of four emotional dimensions, demonstrating its potential for designing deep learning models for emotion recognition.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Add to Collection

Article Computer Science, Information Systems

EEG-Based Emotion Recognition Using Spatial-Temporal Graph Convolutional LSTM With Attention Mechanism

Lin Feng et al.

Summary: This study proposes a hybrid model, ST-GCLSTM, that effectively extracts representative spatial-temporal features by considering the biological topological information among brain regions. The model achieves significant performance improvement in emotion recognition tasks.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Add to Collection

Article Computer Science, Information Systems

3DCANN: A Spatio-Temporal Convolution Attention Neural Network for EEG Emotion Recognition

Shuaiqi Liu et al.

Summary: This paper proposes a deep learning model called 3DCANN for EEG emotion recognition. The model is able to extract spatio-temporal features from EEG signals and achieves superior performance over existing models in emotion classification.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Add to Collection

Article Engineering, Multidisciplinary

Modelling and statistical analysis of emotions in 3D space

Divya Garg et al.

Summary: Emotional intelligence provides a new approach for developing machines with emotional perception, using statistical methods to evaluate the correlation between different emotional states and analyzing the distribution of emotional states through a three-dimensional model. This is the first step towards this direction, successfully implemented in the fields of robotics and gaming.

ENGINEERING RESEARCH EXPRESS (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

Self-Supervised ECG Representation Learning for Emotion Recognition

Pritam Sarkar et al.

Summary: We propose a self-supervised deep multi-task learning framework for emotion recognition based on electrocardiogram (ECG). By learning ECG representations through pretext tasks and transferring them to an emotion recognition network, our solution shows significantly improved performance compared to fully-supervised learning methods.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2022)

Add to Collection

Article Biotechnology & Applied Microbiology

EEG-Based Emotion Recognition Using a 2D CNN with Different Kernels

Yuqi Wang et al.

Summary: This article proposes a novel deep neural network based on EEG for emotion classification, and verifies its accuracy through experiments. The results show great potential for research and application in the field of emotion recognition.

BIOENGINEERING-BASEL (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals

Xiaobing Du et al.

Summary: This article introduces a deep learning method for EEG-based emotion recognition, which can automatically extract spatial features between different EEG electrodes and achieve state-of-the-art performance in emotion recognition.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2022)

Add to Collection

Article Computer Science, Information Systems

Deep Learning Model With Adaptive Regularization for EEG-Based Emotion Recognition Using Temporal and Frequency Features

Alireza Samavat et al.

Summary: This paper proposes a hybrid multi-input deep model using convolution neural networks (CNNs) and bidirectional Long Short-term Memory (Bi-LSTM) to recognize emotions from raw EEG signals. The method extracts temporal and frequency features and considers the spatial information of EEG, achieving significant improvement in accuracy compared to the baseline.

IEEE ACCESS (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

A 3D-convolutional neural network framework with ensemble learning techniques for multi-modal emotion recognition

Elham S. Salama et al.

Summary: This paper proposes a novel multi-modal human emotion recognition framework that combines EEG signals and video data of human faces for emotion recognition. Deep learning and ensemble learning techniques are utilized to improve recognition accuracy and achieve fusion predictions. The approach outperforms recent works in the multi-modal emotion recognition field.

EGYPTIAN INFORMATICS JOURNAL (2021)

Add to Collection

Article Engineering, Biomedical

EEG-based emotion recognition with deep convolutional neural networks

Mehmet Akif Ozdemir et al.

Summary: This paper proposes a novel method for emotion recognition using deep convolutional neural networks on multi-channel EEG signals from the DEAP database. By preserving temporal, spectral, and spatial information of the EEG signals, significant improvements in classification accuracy were achieved compared to other studies.

BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

A LSTM based deep learning network for recognizing emotions using wireless brainwave driven system

Aditi Sakalle et al.

Summary: The research aims to develop a brainwave-driven system for recognizing positive, negative, and neutral emotions, with a focus on the LSTM model. The classification performance is evaluated through comparisons with different models and cross-validation techniques, with results showing that brain signals from the 18-25 age group are most effective in emotion identification, and females are more emotionally active than males.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Add to Collection

Article Engineering, Electrical & Electronic

Emotion Recognition Using Three-Dimensional Feature and Convolutional Neural Network from Multichannel EEG Signals

Hao Chao et al.

Summary: This article introduces a method of emotion recognition based on EEG signals and deep learning algorithm, which extracts emotion-related information from multichannel EEG signals by processing time-domain features and constructing a feature matrix, and employs CNN for emotion recognition.

IEEE SENSORS JOURNAL (2021)

Add to Collection

Article Mathematics, Interdisciplinary Applications

Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning

Lin Zhou

Summary: When facing various pressures, human beings may experience severe psychological issues, especially depression and anxiety. Utilizing brain functional imaging and deep learning for personal emotional tendency analysis can accurately identify and make decisions regarding emotions, which is a research hotspot in psychology and computer science.

DISCRETE DYNAMICS IN NATURE AND SOCIETY (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM

Yongqiang Yin et al.

Summary: This paper presents a novel emotion recognition method based on a novel deep learning model, which outperforms state-of-the-art methods in emotion recognition, as demonstrated by extensive experiments on the DEAP dataset.

APPLIED SOFT COMPUTING (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

ScalingNet: Extracting features from raw EEG data for emotion recognition

Jingzhao Hu et al.

Summary: The paper introduces a novel convolutional neural network architecture that adaptsively extracts data-driven features from raw EEG signals and demonstrates excellent performance in emotion recognition tasks.

NEUROCOMPUTING (2021)

Add to Collection

Article Health Care Sciences & Services

Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network

Nagarajan Ganapathy et al.

Summary: This study attempts to classify emotional states using multiscale convolutional neural networks on electrodermal activity signals. Experimental results show that the proposed method can effectively differentiate different emotional states and outperforms single-layer convolutional neural networks.

JOURNAL OF MEDICAL SYSTEMS (2021)

Add to Collection

Article Neurosciences

Hierarchical Spatiotemporal Electroencephalogram Feature Learning and Emotion Recognition With Attention-Based Antagonism Neural Network

Pengwei Zhang et al.

Summary: Inspired by neuroscience research, a new EEG-based human emotion classification model named R2G-ST-BiLSTM is proposed, achieving higher accuracy in emotion recognition. The model uses a hierarchical neural network, introduces a regional attention mechanism and domain discriminator, learning discriminative spatiotemporal features.

FRONTIERS IN NEUROSCIENCE (2021)

Add to Collection

Article Computer Science, Information Systems

LSTM-Based Emotion Detection Using Physiological Signals: IoT Framework for Healthcare and Distance Learning in COVID-19

Muhammad Awais et al.

Summary: The study proposes an integrated IoT framework for real-time communication and emotion recognition, suitable for health monitoring and distance learning support during pandemics. The proposed IoT protocols achieve ultralow latency and improved reliability, while the deep learning scheme shows high performance.

IEEE INTERNET OF THINGS JOURNAL (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

A Channel-Fused Dense Convolutional Network for EEG-Based Emotion Recognition

Zhongke Gao et al.

Summary: This article introduces a novel deep learning framework, the channel-fused dense convolutional network, for EEG-based emotion recognition, capable of effectively extracting features from noisy EEG signals. Experimental results show that the framework achieved significant accuracies on the SEED and DEAP datasets, outperforming most comparative studies.

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS (2021)

Add to Collection

Article Engineering, Biomedical

EEG based emotion detection using fourth order spectral moment and deep learning

Vaishali M. Joshi et al.

Summary: This research introduces a method for emotion detection using EEG signals, achieving good results on different databases. By utilizing the LF-DfE feature extractor and BiLSTM network, the accuracy of emotion detection has been significantly improved.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

Add to Collection

Article Engineering, Biomedical

Leveraging spatial-temporal convolutional features for EEG-based emotion recognition

Yi An et al.

Summary: This paper proposed a novel framework for EEG-based emotion recognition, which includes DCNN and ConvLSTM modules. Extensive experiments show that the framework can effectively improve the accuracy of emotion classification, outperforming most of the existing methods.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

Add to Collection

Article Biology

EEG Channel Correlation Based Model for Emotion Recognition

Md Rabiul Islam et al.

Summary: The study proposed a deep machine-learning model using Convolutional Neural Network to convert EEG data and recognize emotions on images, overcoming the challenge of emotion recognition from low amplitude variation in EEG signals.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Add to Collection

Article Engineering, Civil

A Convolution Bidirectional Long Short-Term Memory Neural Network for Driver Emotion Recognition

Guanglong Du et al.

Summary: The paper proposes a new deep learning framework CBLNN for real-time recognition of driver emotions by extracting facial information and heart rate data. Tested and proven to be able to quickly and steadily identify happiness, anger, sadness, fear, and neutrality in real time.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Three-dimensional feature maps and convolutional neural network-based emotion recognition

Xiangwei Zheng et al.

Summary: The paper proposes an emotion recognition method based on three-dimensional feature maps and CNNs, which improves the accuracy of emotion recognition through steps such as calibration, segmentation, feature extraction, and CNN design. Experimental results demonstrate that the proposed method has better classification accuracy than state-of-the-art methods.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2021)

Add to Collection

Article Mathematical & Computational Biology

EEG-Based Personality Prediction Using Fast Fourier Transform and DeepLSTM Model

Harshit Bhardwaj et al.

Summary: This paper implements a DeepLSTM network to classify personality traits using EEG signals, leveraging the MBTI model for prediction. The proposed model achieved a maximum classification accuracy of 96.94% and outperformed state-of-the-art methods when tested on both collected and publicly available EEG datasets.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition

Yang Li et al.

Summary: This study introduces a novel bi-hemispheric discrepancy model to improve EEG emotion recognition by learning the differences between the left and right hemispheres. Experimental results demonstrate the effectiveness and advantages of this model in solving EEG emotion recognition problems.

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS (2021)

Add to Collection

Article Computer Science, Information Systems

Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Mahmoud Nasr et al.

Summary: The significant increase in individuals with chronic ailments has led to an urgent need for an innovative healthcare model that is more personalized and less reliant on traditional institutions. The smart healthcare system, driven by advancements in AI and ML, is becoming increasingly necessary. This paper discusses current state-of-the-art smart healthcare systems, focusing on areas such as health monitoring devices, disease diagnosis using machine learning, and assistive frameworks like social robots for ambient assisted living.

IEEE ACCESS (2021)

Add to Collection

Article Engineering, Biomedical

EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG With an Application to Emotion Recognition

Zhen Liang et al.

Summary: This paper proposes a practical hybrid unsupervised deep convolutional recurrent generative adversarial network based EEG feature characterization and fusion model, EEGFuseNet, which automatically characterizes deep EEG features covering both spatial and temporal dynamics. The proposed model demonstrates robust and reliable performance in extracting dynamic EEG features in an unsupervised manner.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups

Juan Abdon Miranda-Correa et al.

Summary: AMIGOS is a multimodal dataset designed for studying affect, personality traits, and mood on individuals and groups, collected from neuro-physiological signals and videos recorded in different social contexts. The dataset enables in-depth analysis of affective responses in relation to personality, mood, and social context, with detailed correlation analysis and baseline methods provided for classification tasks.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2021)

Add to Collection

Article Computer Science, Information Systems

Effects of Data Augmentation Method Borderline-SMOTE on Emotion Recognition of EEG Signals Based on Convolutional Neural Network

Yu Chen et al.

Summary: This research delves into emotion recognition based on electroencephalogram signals, proposing a method using convolutional neural network and data augmentation. Through experimentation, the method has proven effective in emotion recognition, showcasing significant improvements in data balance and recognition accuracy.

IEEE ACCESS (2021)

Add to Collection

Article Computer Science, Information Systems

Multi-Modal Physiological Data Fusion for Affect Estimation Using Deep Learning

Murtadha D. Hssayeni et al.

Summary: This study investigates the use of deep learning for emotion estimation based on physiological signals, proposing two multi-modal data fusion methods that outperform traditional machine learning. When utilizing only two modalities, the model achieved correlations of 0.69 for positive affect and 0.79 for negative affect estimation, along with achieving 78% F1-score and 79% accuracy for emotion classification.

IEEE ACCESS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Deep Physiological Affect Network for the Recognition of Human Emotions

Byung Hyung Kim et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2020)

Add to Collection

Article Computer Science, Information Systems

Emotion recognition from spatiotemporal EEG representations with hybrid convolutional recurrent neural networks via wearable multi-channel headset

Jingxia Chen et al.

COMPUTER COMMUNICATIONS (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Deep Learning of Empirical Mean Curve Decomposition-Wavelet Decomposed EEG Signal for Emotion Recognition

Sujata Bhimrao Wankhade et al.

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Learning CNN features from DE features for EEG-based emotion recognition

Sunhee Hwang et al.

PATTERN ANALYSIS AND APPLICATIONS (2020)

Add to Collection

Article Neurosciences

Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks

Xiang Li et al.

FRONTIERS IN NEUROSCIENCE (2020)

Add to Collection

Article Computer Science, Information Systems

Exploring temporal representations by leveraging attention-based bidirectional LSTM-RNNs for multi-modal emotion recognition

Chao Li et al.

INFORMATION PROCESSING & MANAGEMENT (2020)

Add to Collection

Article Computer Science, Information Systems

Empirical analysis of deep learning networks for affective video tagging

Anju Mishra et al.

MULTIMEDIA TOOLS AND APPLICATIONS (2020)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Use of electroencephalogram and long short-term memory networks to recognize design preferences of users toward architectural design alternatives

Sunwoo Chang et al.

JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Bi-directional Long Short-Term Memory model to analyze psychological effects on gamers

Lidia Ghosh et al.

APPLIED SOFT COMPUTING (2020)

Add to Collection

Article Engineering, Electrical & Electronic

Efficient approach for EEG-based emotion recognition

D. Senguer et al.

ELECTRONICS LETTERS (2020)

Add to Collection

Review Chemistry, Analytical

EEG-Based BCI Emotion Recognition: A Survey

Edgar P. Torres et al.

SENSORS (2020)

Add to Collection

Review Mathematical & Computational Biology

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

Nazmi Sofian Suhaimi et al.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Affective EEG-Based Person Identification Using the Deep Learning Approach

Theerawit Wilaiprasitporn et al.

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS (2020)

Add to Collection

Article Neurosciences

EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder

Junxiu Liu et al.

FRONTIERS IN SYSTEMS NEUROSCIENCE (2020)

Add to Collection

Article Medical Informatics

Stress detection using deep neural networks

Russell Li et al.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2020)

Add to Collection

Article Neurosciences

Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network

Qinghua Zhong et al.

FRONTIERS IN HUMAN NEUROSCIENCE (2020)

Add to Collection

Article Neurosciences

An Investigation of Deep Learning Models for EEG-Based Emotion Recognition

Yaqing Zhang et al.

FRONTIERS IN NEUROSCIENCE (2020)

Add to Collection

Review Computer Science, Information Systems

Deep Learning Based Systems Developed for Fall Detection: A Review

Md. Milon Islam et al.

IEEE ACCESS (2020)

Add to Collection

Article Automation & Control Systems

A Coincidence-Filtering-Based Approach for CNNs in EEG-Based Recognition

Zhongke Gao et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Add to Collection

Article Computer Science, Information Systems

Emotion Recognition From Multi-Channel EEG Signals by Exploiting the Deep Belief-Conditional Random Field Framework

Hao Chao et al.

IEEE ACCESS (2020)

Add to Collection

Article Computer Science, Information Systems

Multimodal Emotion Recognition Based on Ensemble Convolutional Neural Network

Haiping Huang et al.

IEEE ACCESS (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Emotions Recognition Using EEG Signals: A Survey

Soraia M. Alarcao et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2019)

Add to Collection

Article Automation & Control Systems

Spatial-Temporal Recurrent Neural Network for Emotion Recognition

Tong Zhang et al.

IEEE TRANSACTIONS ON CYBERNETICS (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

SAE plus LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG

Xiaofen Xing et al.

FRONTIERS IN NEUROROBOTICS (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

Human emotion recognition using deep belief network architecture

Mohammad Mehedi Hassan et al.

INFORMATION FUSION (2019)

Add to Collection

Article Chemistry, Analytical

A Multi-Column CNN Model for Emotion Recognition from EEG Signals

Heekyung Yang et al.

SENSORS (2019)

Add to Collection

Article Neurosciences

EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

Hong Zeng et al.

BRAIN SCIENCES (2019)

Add to Collection

Article Engineering, Electrical & Electronic

Development and Validation of an EEG-Based Real-Time Emotion Recognition System Using Edge AI Computing Platform With Convolutional Neural Network System-on-Chip Design

Wai-Chi Fang et al.

IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS (2019)

Add to Collection

Article Computer Science, Information Systems

MPED: A Multi-Model Physiological Emotion Database for Discrete Emotion Recongnition

Tengfei Song et al.

IEEE ACCESS (2019)

Add to Collection

Proceedings Paper Engineering, Biomedical

EEG-Based Emotion Recognition with Similarity Learning Network

Yixin Wang et al.

2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) (2019)

Add to Collection

Article Computer Science, Information Systems

A Hierarchical Bidirectional GRU Model With Attention for EEG-Based Emotion Classification

J. X. Chen et al.

IEEE ACCESS (2019)

Add to Collection

Article Computer Science, Information Systems

Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks

J. X. Chen et al.

IEEE ACCESS (2019)

Add to Collection

Article Engineering, Biomedical

Providing a Four-layer Method Based on Deep Belief Network to Improve Emotion Recognition in Electroencephalography in Brain Signals

Seyed Mohammad Reza Mousavinasr et al.

JOURNAL OF MEDICAL SIGNALS & SENSORS (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition

Jinpeng Li et al.

COGNITIVE COMPUTATION (2018)

Add to Collection

Article Computer Science, Artificial Intelligence

Deep learning based on Batch Normalization for P300 signal detection

Mingfei Liu et al.

NEUROCOMPUTING (2018)

Add to Collection

Article Chemistry, Analytical

Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System

Yea-Hoon Kwon et al.

SENSORS (2018)

Add to Collection

Article Computer Science, Information Systems

DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices

Stamos Katsigiannis et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2018)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Complex-valued unsupervised convolutional neural networks for sleep stage classification

Junming Zhang et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2018)

Add to Collection

Article Computer Science, Hardware & Architecture

Emotion recognition using empirical mode decomposition and approximation entropy

Tian Chen et al.

COMPUTERS & ELECTRICAL ENGINEERING (2018)

Add to Collection

Article Mathematical & Computational Biology

Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework

Hao Chao et al.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2018)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

A Multi-task Cascaded Network for Prediction of Affect, Personality, Mood and Social Context Using EEG Signals

Juan Abdon Miranda-Correa et al.

PROCEEDINGS 2018 13TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE & GESTURE RECOGNITION (FG 2018) (2018)

Add to Collection

Article Chemistry, Multidisciplinary

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

Youjun Li et al.

APPLIED SCIENCES-BASEL (2017)

Add to Collection

Article Neurosciences

Negligible Motion Artifacts in Scalp Electroencephalography (EEG) During Treadmill Walking

Kevin Nathan et al.

FRONTIERS IN HUMAN NEUROSCIENCE (2016)

Add to Collection

Article Computer Science, Artificial Intelligence

Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection

Mohammad Soleymani et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2016)

Add to Collection

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Add to Collection

Article Computer Science, Artificial Intelligence

Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

Wei-Long Zheng et al.

IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT (2015)

Add to Collection

Proceedings Paper Biophysics

Cross-subject and Cross-gender Emotion Classification from EEG

Jia-Yi Zhu et al.

WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING, 2015, VOLS 1 AND 2 (2015)

Add to Collection

Article Multidisciplinary Sciences

EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

Suwicha Jirayucharoensak et al.

SCIENTIFIC WORLD JOURNAL (2014)

Add to Collection

Review Mathematical & Computational Biology

A Review on the Computational Methods for Emotional State Estimation from the Human EEG

Min-Ki Kim et al.

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2013)

Add to Collection

Article Computer Science, Artificial Intelligence

A Multimodal Database for Affect Recognition and Implicit Tagging

Mohammad Soleymani et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2012)

Add to Collection

Article Computer Science, Artificial Intelligence

DEAP: A Database for Emotion Analysis Using Physiological Signals

Sander Koelstra et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2012)

Add to Collection

Article Computer Science, Artificial Intelligence

Deep Machine Learning-A New Frontier in Artificial Intelligence Research

Itamar Arel et al.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2010)

Add to Collection

Article Clinical Neurology

The cerebral sulci and gyri

Guilherme Carvalhal Ribas

NEUROSURGICAL FOCUS (2010)

Add to Collection

Article Computer Science, Cybernetics

Short-term emotion assessment in a recall paradigm

Guillaume Chanel et al.

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES (2009)

Add to Collection

Article Psychology, Biological

Brain oscillations and BIS/BAS (behavioral inhibition/activation system) effects on processing masked emotional cues. ERS/ERD and coherence measures of alpha band

Michela Balconi et al.

INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY (2009)

Add to Collection

Article Computer Science, Artificial Intelligence

Learning Deep Architectures for AI

Yoshua Bengio

FOUNDATIONS AND TRENDS IN MACHINE LEARNING (2009)

Add to Collection

Review Psychology, Experimental

Measures of emotion: A review

Iris B. Mauss et al.

COGNITION & EMOTION (2009)

Add to Collection

Review Psychology, Developmental

The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology

J Posner et al.

DEVELOPMENT AND PSYCHOPATHOLOGY (2005)

Add to Collection

Article Biochemistry & Molecular Biology

Neural activities associated with emotion recognition observed in men and women

TMC Lee et al.

MOLECULAR PSYCHIATRY (2005)

Add to Collection

A review of Deep Learning based methods for Affect Analysis using Physiological Signals

Related references

Export Citation

Share Paper