A review of emotion recognition using physiological signals
Emotion recognition based on physiological signals has been a hot topic and applied in
many areas such as safe driving, health care and social security. In this paper, we present a …
many areas such as safe driving, health care and social security. In this paper, we present a …
A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals
K Kamble, J Sengupta - Multimedia Tools and Applications, 2023 - Springer
Emotion recognition using electroencephalography (EEG) is becoming an interesting topic
among researchers. It has made a remarkable entry in the domain of biomedical, smart …
among researchers. It has made a remarkable entry in the domain of biomedical, smart …
A review, current challenges, and future possibilities on emotion recognition using machine learning and physiological signals
The seminal work on Affective Computing in 1995 by Picard set the base for computing that
relates to, arises from, or influences emotions. Affective computing is a multidisciplinary field …
relates to, arises from, or influences emotions. Affective computing is a multidisciplinary field …
EEG-based emotion classification using a deep neural network and sparse autoencoder
J Liu, G Wu, Y Luo, S Qiu, S Yang, W Li… - Frontiers in Systems …, 2020 - frontiersin.org
Emotion classification based on brain–computer interface (BCI) systems is an appealing
research topic. Recently, deep learning has been employed for the emotion classifications of …
research topic. Recently, deep learning has been employed for the emotion classifications of …
A bayesian deep learning framework for end-to-end prediction of emotion from heartbeat
R Harper, J Southern - IEEE transactions on affective …, 2020 - ieeexplore.ieee.org
Automatic prediction of emotion promises to revolutionise human-computer interaction.
Recent trends involve fusion of multiple data modalities audio, visual, and physiological to …
Recent trends involve fusion of multiple data modalities audio, visual, and physiological to …
A systematic review of time series classification techniques used in biomedical applications
Background: Digital clinical measures collected via various digital sensing technologies
such as smartphones, smartwatches, wearables, and ingestible and implantable sensors …
such as smartphones, smartwatches, wearables, and ingestible and implantable sensors …
Recognizing developers' emotions while programming
Developers experience a wide range of emotions during programming tasks, which may
have an impact on job performance. In this paper, we present an empirical study aimed at (i) …
have an impact on job performance. In this paper, we present an empirical study aimed at (i) …
The design of CNN architectures for optimal six basic emotion classification using multiple physiological signals
SJ Oh, JY Lee, DK Kim - Sensors, 2020 - mdpi.com
This study aimed to design an optimal emotion recognition method using multiple
physiological signal parameters acquired by bio-signal sensors for improving the accuracy …
physiological signal parameters acquired by bio-signal sensors for improving the accuracy …
Electroencephalogram emotion recognition based on 3D feature fusion and convolutional autoencoder
Y An, S Hu, X Duan, L Zhao, C Xie… - Frontiers in Computational …, 2021 - frontiersin.org
As one of the key technologies of emotion computing, emotion recognition has received
great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to …
great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to …
Multimodal physiological signal emotion recognition based on convolutional recurrent neural network
J Liao, Q Zhong, Y Zhu, D Cai - IOP conference series: materials …, 2020 - iopscience.iop.org
In order to solve the problem that the emotion recognition rate of single-mode physiological
signals is not high in the physiological signals based emotion recognition, in this paper, we …
signals is not high in the physiological signals based emotion recognition, in this paper, we …