Ten challenges for EEG-based affective computing
The emerging field of affective computing focuses on enhancing computers' ability to
understand and appropriately respond to people's affective states in human-computer …
understand and appropriately respond to people's affective states in human-computer …
Physiological sensors based emotion recognition while experiencing tactile enhanced multimedia
Emotion recognition has increased the potential of affective computing by getting an instant
feedback from users and thereby, have a better understanding of their behavior …
feedback from users and thereby, have a better understanding of their behavior …
A novel transferability attention neural network model for EEG emotion recognition
The existed methods for electroencephalograph (EEG) emotion recognition always train the
models based on all the EEG samples indistinguishably. However, some of the source …
models based on all the EEG samples indistinguishably. However, some of the source …
Exploiting transfer learning for emotion recognition under cloud-edge-client collaborations
Emerging virtual reality/augmented reality games and self-driving cars necessitate
accurate/responsive/private emotion recognition. Usually, traditional emotion recognition …
accurate/responsive/private emotion recognition. Usually, traditional emotion recognition …
Multi-task sequence tagging for emotion-cause pair extraction via tag distribution refinement
The task emotion-cause pair extraction deals with finding all emotions and the
corresponding causes from emotion texts. Existing joint methods solve it as multi-task …
corresponding causes from emotion texts. Existing joint methods solve it as multi-task …
Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals
S Bagherzadeh, K Maghooli, A Shalbaf… - Cognitive …, 2022 - Springer
Abstract Convolutional Neural Networks (CNN) have recently made considerable advances
in the field of biomedical signal processing. These methodologies can assist in emotion …
in the field of biomedical signal processing. These methodologies can assist in emotion …
A fuzzy ensemble-based deep learning model for EEG-based emotion recognition
Emotion recognition from EEG signals is a major field of research in cognitive computing.
The major challenges involved in the task are extracting meaningful features from the …
The major challenges involved in the task are extracting meaningful features from the …
[HTML][HTML] A review of recent approaches for emotion classification using electrocardiography and electrodermography signals
AF Bulagang, NG Weng, J Mountstephens… - Informatics in Medicine …, 2020 - Elsevier
This paper reviews emotion classification investigations, focusing on the use of the
Electrocardiogram (ECG) and Electrodermography (EDG)/Galvanic Skin Response (GSR) …
Electrocardiogram (ECG) and Electrodermography (EDG)/Galvanic Skin Response (GSR) …
Approaches, applications, and challenges in physiological emotion recognition—a tutorial overview
An automatic emotion recognition system can serve as a fundamental framework for various
applications in daily life from monitoring emotional well-being to improving the quality of life …
applications in daily life from monitoring emotional well-being to improving the quality of life …
Subject independent emotion recognition system for people with facial deformity: an EEG based approach
Emotion recognition from Electroencephalography (EEG) is a better choice for the people
with facial deformity like where facial data is not accurate or not available for example …
with facial deformity like where facial data is not accurate or not available for example …