A Domain Generative Graph Network for EEG-Based Emotion Recognition

Article Computer Science, Artificial Intelligence

EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention

Wei Tao et al.

Summary: This article proposes an attention-based convolutional recurrent neural network (ACRNN) to extract more discriminative features from EEG signals and improve the accuracy of emotion recognition.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2023)

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Article Computer Science, Artificial Intelligence

SparseDGCNN: Recognizing Emotion From Multichannel EEG Signals

Guanhua Zhang et al.

Summary: In this article, a sparse DGCNN model is proposed to improve the emotion recognition performance by imposing a sparseness constraint on the graph G. The research reveals that different brain regions may have different functions and the functional relations among electrodes are possibly highly localized and sparse. The experiments show that the sparse DGCNN model has consistently better accuracy than representative methods and has good scalability.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2023)

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Article Computer Science, Theory & Methods

Real-time epileptic seizure recognition using Bayesian genetic whale optimizer and adaptive machine learning

Ahmed M. Anter et al.

Summary: In this study, a new model is proposed to recognize seizure states from EEG within an IoT framework, utilizing innovative algorithms for feature selection and classification. Experimental results demonstrate that the model performs better and more accurately in identifying seizure states compared to existing methods.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2022)

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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)

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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)

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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)

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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)

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Article Neurosciences

Designing individual-specific and trial-specific models to accurately predict the intensity of nociceptive pain from single-trial fMRI responses

Qianqian Lin et al.

Summary: A new approach was proposed to design individual-specific or trial-specific pain prediction models for more accurate prediction of nociceptive pain intensity from fMRI responses. The approach utilized supervised k-means clustering on nociceptive-evoked fMRI responses to train models based on similar activation patterns, resulting in significantly higher prediction accuracy compared to conventional models. The generalizability of the approach was further validated by testing specific models on independent datasets, showing potential for developing personalized pain assessment tools in clinical practice.

NEUROIMAGE (2021)

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Article Physics, Multidisciplinary

Differential Entropy Feature Signal Extraction Based on Activation Mode and Its Recognition in Convolutional Gated Recurrent Unit Network

Yongsheng Zhu et al.

Summary: A method of DE feature signal recognition based on a Convolutional Gated Recurrent Unit network was proposed in this paper, achieving 87.89% on arousal and 88.69% on valence recognition accuracy on DEAP dataset through spatial-temporal feature extraction of feature signals in different frequency bands.

FRONTIERS IN PHYSICS (2021)

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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)

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Article Computer Science, Artificial Intelligence

Differences first in asymmetric brain: A bi-hemisphere discrepancy convolutional neural network for EEG emotion recognition

Dongmin Huang et al.

Summary: Neuroscience research has found that the left and right hemispheres of the human brain respond differently to emotions, which is crucial for emotion recognition. The proposed BiDCNN model effectively learns these differences and achieves state-of-the-art performance in emotion recognition tasks. The model demonstrates high accuracy rates in both subject-dependent and subject-independent experiments.

NEUROCOMPUTING (2021)

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Article Computer Science, Information Systems

FLDNet: Frame-Level Distilling Neural Network for EEG Emotion Recognition

Zhe Wang et al.

Summary: A novel deep learning framework FLDNet is proposed in this paper, which gradually distills features through a triple-net structure to replace hand-engineered features, and experiments on public datasets demonstrate its effectiveness and robustness.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

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Article Computer Science, Artificial Intelligence

A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition

Yang Li et al.

Summary: This paper proposes a novel neural network model BiDANN for EEG emotion recognition, which learns discriminative emotional features through adversarial learning on the left and right hemispheres. Experimental results on the SEED database validate the superior performance of the model in EEG emotion recognition.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2021)

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Article Computer Science, Artificial Intelligence

Survey on Emotional Body Gesture Recognition

Fatemeh Noroozi et al.

Summary: Automatic emotion recognition, particularly from body gestures, is a trending research topic that has received less exploration compared to facial expressions or speech-based recognition. Researchers introduced a framework for recognizing emotional body gestures and discussed limitations of current methods.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2021)

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Article Computer Science, Artificial Intelligence

Novel Audio Features for Music Emotion Recognition

Renato Panda et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2020)

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Article Computer Science, Artificial Intelligence

EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks

Tengfei Song et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2020)

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Article Computer Science, Artificial Intelligence

A New Type of Fuzzy-Rule-Based System With Chaotic Swarm Intelligence for Multiclassification of Pain Perception From fMRI

Ahmed M. Anter et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2020)

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Article Computer Science, Hardware & Architecture

Generative Adversarial Networks

Ian Goodfellow et al.

COMMUNICATIONS OF THE ACM (2020)

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Article Computer Science, Artificial Intelligence

EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network

Heng Cui et al.

KNOWLEDGE-BASED SYSTEMS (2020)

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Article Computer Science, Artificial Intelligence

Discriminative Spatiotemporal Local Binary Pattern with Revisited Integral Projection for Spontaneous Facial Micro-Expression Recognition

Xiaohua Huang et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2019)

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Article Computer Science, Artificial Intelligence

Emotions Recognition Using EEG Signals: A Survey

Soraia M. Alarcao et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2019)

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Article Computer Science, Artificial Intelligence

Identifying Stable Patterns over Time for Emotion Recognition from EEG

Wei-Long Zheng et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2019)

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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)

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Proceedings Paper Computer Science, Interdisciplinary Applications

Emotion Recognition using Multimodal Residual LSTM Network

Jiaxin Ma et al.

PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19) (2019)

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Article Computer Science, Artificial Intelligence

A Combined Rule-Based & Machine Learning Audio-Visual Emotion Recognition Approach

Kah Phooi Seng et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2018)

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Proceedings Paper Telecommunications

Dynamic Switch-Controller Association and Control Devolution for SDN Systems

Xi Huang et al.

2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) (2017)

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Article Computer Science, Artificial Intelligence

A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition

Yong-Jin Liu et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2016)

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Review Engineering, Biomedical

EEG artifact removal-state-of-the-art and guidelines

Jose Antonio Urigueen et al.

JOURNAL OF NEURAL ENGINEERING (2015)

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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)

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Article Computer Science, Artificial Intelligence

Feature Extraction and Selection for Emotion Recognition from EEG

Robert Jenke et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2014)

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Article Engineering, Electrical & Electronic

The Emerging Field of Signal Processing on Graphs

David I. Shuman et al.

IEEE SIGNAL PROCESSING MAGAZINE (2013)

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Article Computer Science, Artificial Intelligence

DEAP: A Database for Emotion Analysis Using Physiological Signals

Sander Koelstra et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2012)

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Article Computer Science, Artificial Intelligence

Toward a Minimal Representation of Affective Gestures

Donald Glowinski et al.

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2011)

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Article Computer Science, Information Systems

Emotion Recognition From EEG Using Higher Order Crossings

Panagiotis C. Petrantonakis et al.

IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE (2010)

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Article Computer Science, Artificial Intelligence

Toward machine emotional intelligence: Analysis of affective physiological state

RW Picard et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2001)

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A Domain Generative Graph Network for EEG-Based Emotion Recognition

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