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Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

Saad Awadh Alanazi, Madallah Alruwaili, Fahad Ahmad, Alaa Alaerjan, Nasser Alshammari
2021 Sensors  
In this study, the one-dimensional convolutional neural network classification model is initially employed to interpret and evaluate shifts in emotion over a period by categorizing emotional states that  ...  However, a significant mean difference was observed for the blood volume pulse, galvanic skin response, skin temperature, valence, and arousal values, indicating the effectiveness of the chosen physiological  ...  Emotional State Detection 3.1.1. One-Dimensional Convolutional Neural Network Table 4 . 4 One-dimensional convolutional neural network characterization.  ... 
doi:10.3390/s21113760 pmid:34071556 fatcat:kagcicrqnjgnrdtpxf6gorwnye

Automatic Multiface Expression Recognition Using Convolutional Neural Network

Padmapriya K.C., Leelavathy V., Angelin Gladston
2021 International Journal of Artificial Intelligence and Machine Learning  
Most of the methods utilized in the literature for the automatic facial expression recognition systems are based on geometry and appearance.  ...  In this paper we applied various deep learning methods to classify the seven key human emotions: anger, disgust, fear, happiness, sadness, surprise and neutrality.  ...  ., (2019) recognized emotions for a text-independent and speaker-independent emotion recognition system based on a novel classifier, which is a hybrid of a cascaded Gaussian mixture model and deep neural  ... 
doi:10.4018/ijaiml.20210701.oa8 fatcat:tbzf3jvbwnahhkr6ynuxn4bpza

Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS)

Luz Santamaria-Granados, Mario Munoz-Organero, Gustavo Ramirez-Gonzalez, Enas Abdulhay, N. Arunkumar
2019 IEEE Access  
This paper applies the deep learning approach using a deep convolutional neural network on a dataset of physiological signals (electrocardiogram and galvanic skin response), in this case, the AMIGOS dataset  ...  INDEX TERMS Emotion recognition, deep convolutional neural network, physiological signals, machine learning, AMIGOS dataset.  ...  DEEP CONVOLUTIONAL NEURAL NETWORK Deep learning is an area of machine learning based on algorithms and techniques for modeling high-level abstractions in datasets [47] , such as the patterns recognition  ... 
doi:10.1109/access.2018.2883213 fatcat:vz4wpuoi4rhxfbwn2bc43rcw3m

Cross-Subject Multimodal Emotion Recognition based on Hybrid Fusion

Yucel Cimtay, Erhan Ekmekcioglu, Seyma Caglar Ozhan
2020 IEEE Access  
to thank all volunteering staff and students in Loughborough University London and Hacettepe University for participating in the recording sessions to generate the LUMED-2 dataset and Perihan TEKELI from  ...  GALVANIC SKIN RESPONSE (GSR) ANALYSIS Second modality used in the proposed hybrid multimodal architecture is the GSR data.  ...  When one is emotionally aroused, the electrical conductivity of the skin changes.  ... 
doi:10.1109/access.2020.3023871 fatcat:6pv6ftzbxzb57jejybadv43x6u

Advances in Emotion Recognition: Link to Depressive Disorder [chapter]

Xiaotong Cheng, Xiaoxia Wang, Tante Ouyang, Zhengzhi Feng
2020 Mental Disorders [Working Title]  
text on social network platform.  ...  The emotion recognition algorithms using emotion representation based on emotional labels are intuitive which are ambiguous for computer processing.  ...  [32] proposed a joint model of microblog emotion recognition and emotion incentive extraction based on neural network.  ... 
doi:10.5772/intechopen.92019 fatcat:jmss4llbpnfrxcue6bzebsgmby

Deep Learning Analysis of Mobile Physiological, Environmental and Location Sensor Data for Emotion Detection

Eiman Kanjo, Eman M.G. Younis, Chee Siang Ang
2018 Information Fusion  
The version in the Kent Academic Repository may differ from the final published version. Users are advised to check for the status of the paper.  ...  We follow a hybrid approach based on Convolutional Neural Network and Long Short-term Memory Recurrent Neural Network (CNN-LSTM) inspired by previous state of the art [19, 21] which have been applied  ...  Various experiments carried out to compare different architectures of deep neural networks, including hybrid models using hybrid multi-channel sensor data (beyond human activity recognition). 4.  ... 
doi:10.1016/j.inffus.2018.09.001 fatcat:di56h2kpcjcdhondjlyghm2gai

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

Heekyung Yang, Jongdae Han, Kyungha Min
2019 Sensors  
Recently, a deep neural network is widely employed for extracting features and recognizing emotions from various biosignals including EEG signals.  ...  We present a multi-column CNN-based model for emotion recognition from EEG signals.  ...  (ECG), electromyogram (EMG), and photoplethysmogram (PPG), respiration pattern (RSP), and galvanic skin response (GSR) are employed.  ... 
doi:10.3390/s19214736 pmid:31683608 pmcid:PMC6865186 fatcat:5z647sxn7raixkhmzjaa45jtcu

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

Nibras Abo Alzahab, Luca Apollonio, Angelo Di Iorio, Muaaz Alshalak, Sabrina Iarlori, Francesco Ferracuti, Andrea Monteriù, Camillo Porcaro
2021 Brain Sciences  
In this work, we proposed a review on hDL-based BCI starting from the seminal studies in 2015.  ...  The most used architecture has been Convolutional Neural Network-Recurrent Neural Network CNN-RNN with 47% of the cases.  ...  Acknowledgments: The authors thanks Mamoon Abou Alzahab for the advice on statistics.  ... 
doi:10.3390/brainsci11010075 pmid:33429938 fatcat:mh6naeofzjahjbi4bikub65i2i

Machine Learning and EEG for Emotional State Estimation [chapter]

Krzysztof Kotowski, Katarzyna Stapor
2021 The Science of Emotional Intelligence  
Based on the EEG signal, trained deep neural networks are then used together with mappings between emotion models to predict the emotions perceived by the participant.  ...  In this chapter, we present a computer system that can automatically recognize an emotional state of a human, based on EEG signals induced by a standardized affective picture database.  ...  for the kNN, 67% for the SVM, 70% for deep convolutional neural network and 75% for the deep hybrid neural network.  ... 
doi:10.5772/intechopen.97133 fatcat:suqe6ipt7zdptjojymepf2euaq

CNN and LSTM-Based Emotion Charting Using Physiological Signals

Muhammad Najam Dar, Muhammad Usman Akram, Sajid Gul Khawaja, Amit N. Pujari
2020 Sensors  
Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR).  ...  To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term  ...  (ECG), and Galvanic Skin Response (GSR) due to their significance in human-computer interaction (HCI).  ... 
doi:10.3390/s20164551 pmid:32823807 fatcat:3wfbsnsrh5fkxakz3ks7pdawiu

Internal Emotion Classification Using EEG Signal with Sparse Discriminative Ensemble

Habib Ullah, Muhammad Uzair, Arif Mahmood, Mohib Ullah, Sultan Daud Khan, Faouzi Alaya Cheikh
2019 IEEE Access  
Our method describes an EEG channel using kernel-based representations computed from the training EEG recordings.  ...  INDEX TERMS Multiple channel EEG, emotion recognition, linear discriminant analysis, sparse PCA. 40144 2169-3536  ...  [33] proposed deep and convolutional neural networks for emotion recognition from multi-channel EEG signals.  ... 
doi:10.1109/access.2019.2904400 fatcat:xk7cesplabef5cvsg2uiqfr57i

Convolutional Neural Networks Model for Emotion Recognition Using EEG Signal

Isah Salim Ahmad, Zhang Shuai, Wang Lingyue, Sani Saminu, Abd El Kader Isselmou, Zilian Cai, Imran Javaid, Souha Kamhi, Ummay Kulsum
2021 North atlantic university union: International Journal of Circuits, Systems and Signal Processing  
This study aims to reduce the manual effort on features extraction and improve the EEG signal single model's emotion recognition using convolutional neural network (CNN) architecture with residue block  ...  In recent years, deep learning (DL) techniques, specifically convolutional neural networks (CNNs) have made excellent progress in many applications.  ...  Convolutional neural network (CNN) Based works. The Deep Learning (DL) approach was proposed for emotion recognition from non-stationary EEG signals.  ... 
doi:10.46300/9106.2021.15.46 fatcat:e263lu6unbhejgl3azjai3yime

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  
neurophysiological signal for emotion recognition and monitoring', Int.  ...  In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency  ...  In this paper, we have proposed a hybrid deep learning model, C-RNN, which integrates CNN and RNN, for emotion recognition and monitoring based on multi-channel EEG signals.  ... 
doi:10.1504/ijdmb.2017.10007183 fatcat:rstpq4apo5ep5ltsfxtbajs2z4


Supriya Londhe, Rushikesh Borse
2018 ICTACT Journal on Communication Technology  
In this paper, we discuss the research done on emotion recognition using skin conductance, skin temperature, electrocardiogram (ECG), electromyography (EMG), and electroencephalogram (EEG) signals.  ...  Physiological signals play vital role in emotion recognition as they are not controllable and are of immediate response type.  ...  Section 3 describes emotion recognition based on skin conductance and EMG. Section 4 talks about emotion recognition based on ECG.  ... 
doi:10.21917/ijct.2018.0265 fatcat:nhncuul23feb3lyajc4toglram

Emotion Variation from Controlling Contrast of Visual Contents through EEG-Based Deep Emotion Recognition

Heekyung Yang, Jongdae Han, Kyungha Min
2020 Sensors  
We manipulate the contrast of the scenes and measure the change of valence and arousal from human participants who watch the contents using a deep emotion recognition module based on electroencephalography  ...  (EEG) signals.  ...  [23] proposed hybrid neural network-based model by combining RNN and CNN for recognizing emotion.  ... 
doi:10.3390/s20164543 pmid:32823741 fatcat:2imp3mvmzbgd5nkaklplwcxhlu
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