A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
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
2021
Sensors
Preserved Fulltext
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
2021
International Journal of Artificial Intelligence and Machine Learning
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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)
2019
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2020
Mental Disorders [Working Title]
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2018
Information Fusion
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk 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
2019
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2021
Brain Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2021
The Science of Emotional Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2020
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2019
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2021
North atlantic university union: International Journal of Circuits, Systems and Signal Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2017
International Journal of Data Mining and Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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
EMOTION RECOGNITION BASED ON VARIOUS PHYSIOLOGICAL SIGNALS - A REVIEW
2018
ICTACT Journal on Communication Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2020
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 353 results