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Emotion Recognition from Multi-channel EEG Signals by Exploiting the Deep Belief-Conditional Random Field Framework

Hao Chao, Yongli Liu
2020 IEEE Access  
of EEG signals for emotion recognition.  ...  In the framework, the raw feature vector sequence is firstly extracted from the multi-channel EEG signals by a sliding window.  ...  There are three emotional labels (−1 for negative, 0 for neutral and +1 for positive). Among the physiological signals, only EEG signals are used to perform emotion recognition task.  ... 
doi:10.1109/access.2020.2974009 fatcat:nzmdsuw7nfetflhiuehqyz5vza

Systematic Analysis of a Military Wearable Device Based on a Multi-Level Fusion Framework: Research Directions

Han Shi, Hai Zhao, Yang Liu, Wei Gao, Sheng-Chang Dou
2019 Sensors  
The proposed framework covers multiple types of information at a single node, including behaviors, physiology, emotions, fatigue, environments, and locations, so as to enable Soldier-BSNs to obtain sufficient  ...  Therefore, this paper proposes a multi-level fusion framework (MLFF) based on Body Sensor Networks (BSNs) of soldiers, and describes a model of the deployment of heterogeneous sensor networks.  ...  The correlation between physiological signal characteristics and three negative emotions.Figure 3. The correlation between physiological signal characteristics and three negative emotions.  ... 
doi:10.3390/s19122651 fatcat:f4kxqgyopvgfbjefn7mkduaasi

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances [article]

Yan Wang, Wei Song, Wei Tao, Antonio Liotta, Dawei Yang, Xinlei Li, Shuyong Gao, Yixuan Sun, Weifeng Ge, Wei Zhang, Wenqiang Zhang
2022 arXiv   pre-print
Thus, the fusion of physical information and physiological signals can provide useful features of emotional states and lead to higher accuracy.  ...  Firstly, we introduce two typical emotion models followed by commonly used databases for affective computing.  ...  [336] first constructed a music-induced ECG emotion database, and then developed a nine-stage framework for automatic ECGbased emotion recognition, including 1) Signal preprocessing; 2) R-wave detection  ... 
arXiv:2203.06935v3 fatcat:h4t3omkzjvcejn2kpvxns7n2qe

Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

Değer Ayata, Yusuf Yaslan, Mustafa E. Kamasak
2020 Journal of Medical and Biological Engineering  
Conclusion This study demonstrated a framework for emotion recognition using multimodal physiological signals from respiratory belt, photo plethysmography and fingertip temperature.  ...  Purpose The purpose of this paper is to propose a novel emotion recognition algorithm from multimodal physiological signals for emotion aware healthcare systems.  ...  Conclusion This study demonstrated a framework for emotion recognition using multimodal physiological signals from respiratory belt, photo plethysmography and fingertip temperature.  ... 
doi:10.1007/s40846-019-00505-7 fatcat:zyvnt2ajhzh7rfreobdkd4yfau

A Multimodal Facial Emotion Recognition Framework through the Fusion of Speech with Visible and Infrared Images

Mohammad Faridul Haque Siddiqui, Ahmad Y. Javaid
2020 Multimodal Technologies and Interaction  
This work presents a multimodal automatic emotion recognition (AER) framework capable of differentiating between expressed emotions with high accuracy.  ...  The exigency of emotion recognition is pushing the envelope for meticulous strategies of discerning actual emotions through the use of superior multimodal techniques.  ...  For example, using the images and voice samples from the same subjects and using other modalities such as physiological signals might further enhance the framework for detecting the emotional intensity  ... 
doi:10.3390/mti4030046 fatcat:uiafzatnfjhw5mby3aauavis2e

Multimodal emotion recognition using a hierarchical fusion convolutional neural network

Yong Zhang, Cheng Cheng, Yidie Zhang
2021 IEEE Access  
Considering the complexity of recording electroencephalogram signals, some researchers have applied deep learning to find new features for emotion recognition.  ...  However, the extraction of hierarchical features with convolutional neural network for multimodal emotion recognition remains unexplored.  ...  [5] proposed a three-emotion recognition model based on physiological signals.  ... 
doi:10.1109/access.2021.3049516 fatcat:rnynnapfnjdldi54rolcspglxi

Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

Dawei Song, Peng Zhang, Yuexian Hou, Bin Hu, Xiang Li
2017 International Journal of Data Mining and Bioinformatics  
How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology.  ...  neurophysiological signal for emotion recognition and monitoring', Int.  ...  The brief process of our framework in dealing with multi-channel neurophysiological signal based emotion recognition is illustrated in Figure 1 .  ... 
doi:10.1504/ijdmb.2017.10007183 fatcat:rstpq4apo5ep5ltsfxtbajs2z4

Emotion Recognition from Multiband EEG Signals Using CapsNet

Hao Chao, Liang Dong, Yongli Liu, Baoyun Lu
2019 Sensors  
Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive.  ...  Experiments conducted on the dataset for emotion analysis using EEG, physiological, and video signals (DEAP) indicate that the proposed method outperforms most of the common models.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/s19092212 fatcat:bw5txv3urbdfdarao5jjl534li

Expression-EEG Based Collaborative Multimodal Emotion Recognition Using Deep AutoEncoder

Hongli Zhang
2020 IEEE Access  
To alleviate this problem, we proposed an expression-EEG interaction multi-modal emotion recognition method using a deep automatic encoder.  ...  Finally, LIBSVM classifier is used to complete classification task. We carried out experiments on a constructed video library to verify the proposed emotion recognition method.  ...  Therefore, the use of human physiological signals for emotion recognition is currently a novel international research trend in emotion computing.  ... 
doi:10.1109/access.2020.3021994 fatcat:5vvqz4okt5a37o66cldhtpmkje

FusionSense: Emotion Classification Using Feature Fusion of Multimodal Data and Deep Learning in a Brain-Inspired Spiking Neural Network

Clarence Tan, Gerardo Ceballos, Nikola Kasabov, Narayan Puthanmadam Subramaniyam
2020 Sensors  
Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing.  ...  expressions, voice, posture to name a few, in order to classify emotions.  ...  Several deep learning methodologies have been utilized for emotion recognition using physiological signals [70] [71] [72] [73] .  ... 
doi:10.3390/s20185328 pmid:32957655 pmcid:PMC7571195 fatcat:hasiuoax3fb5hhyu5nriad3gve

Multimodal Fused Emotion Recognition about Expression-EEG Interaction and Collaboration Using Deep Learning

Di Wu, Jianpei Zhang, Qingchao Zhao
2020 IEEE Access  
[21] proposed a decision-level fusion framework of both EEG and facial expression in continuous emotions recognition.  ...  Emotions includes three parts: subjective experience (personal feelings), physiological arousal (physiological signal changes in body), and outer performance (quantified response to actions for each body  ... 
doi:10.1109/access.2020.3010311 fatcat:jhtmqsqwgfdcph6fparr5ls3tu

A Review of Emotion Recognition Using Physiological Signals

Lin Shu, Jinyan Xie, Mingyue Yang, Ziyi Li, Zhenqi Li, Dan Liao, Xiangmin Xu, Xinyi Yang
2018 Sensors  
, features, classifiers, and the whole framework for emotion recognition based on the physiological signals.  ...  In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets  ...  For emotion elicitation, subjects are given a series of emotionally-evocative materials to induce a certain emotion.  ... 
doi:10.3390/s18072074 pmid:29958457 pmcid:PMC6069143 fatcat:einxw5uc7fdxherrwenpggdruq

Advances in Multimodal Emotion Recognition Based on Brain–Computer Interfaces

Zhipeng He, Zina Li, Fuzhou Yang, Lei Wang, Jingcong Li, Chengju Zhou, Jiahui Pan
2020 Brain Sciences  
This paper primarily discusses the progress of research into multimodal emotion recognition based on BCI and reviews three types of multimodal affective BCI (aBCI): aBCI based on a combination of behavior  ...  Finally, we identify several important issues and research directions for multimodal emotion recognition based on BCI.  ...  The workflow generally includes three stages: multimodal signal acquisition, signal processing (including basic modality data processing, signal fusion and decision-making) and emotion reflection control  ... 
doi:10.3390/brainsci10100687 pmid:33003397 pmcid:PMC7600724 fatcat:juzx77asgrh2zpl3s2jvw6tdcq

Combining Facial Expressions and Electroencephalography to Enhance Emotion Recognition

Yongrui Huang, Jianhao Yang, Siyu Liu, Jiahui Pan
2019 Future Internet  
We used two emotion datasets—a Database for Emotion Analysis using Physiological Signals (DEAP) and MAHNOB-human computer interface (MAHNOB-HCI)—to evaluate our method.  ...  In this paper, we adopted a multimodal emotion recognition framework by combining facial expression and EEG, based on a valence-arousal emotional model.  ...  Jinyan Xie el al. proposed a new emotion recognition framework based on multi-channel physiological signals, including electrocardiogram (ECG), electromyogram (EMG), and serial clock line (SCL), using  ... 
doi:10.3390/fi11050105 fatcat:ngushp2ec5euxge3kdov5jobru

A Survey on Physiological Signal Based Emotion Recognition [article]

Zeeshan Ahmad, Naimul Khan
2022 arXiv   pre-print
Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject.  ...  Emotion recognition poses its own set of challenges that are very important to address for a robust system.  ...  , features, classifiers, and the frameworks for emotion recognition based on the physiological signal.  ... 
arXiv:2205.10466v1 fatcat:llfz5bjml5gctjuj6tnbjx2rwe
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