EEG‐based emotion recognition: a state‐of‐the‐art review of current trends and opportunities
NS Suhaimi, J Mountstephens… - Computational …, 2020 - Wiley Online Library
Emotions are fundamental for human beings and play an important role in human cognition.
Emotion is commonly associated with logical decision making, perception, human …
Emotion is commonly associated with logical decision making, perception, human …
Review on psychological stress detection using biosignals
G Giannakakis, D Grigoriadis… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
This review investigates the effects of psychological stress on the human body measured
through biosignals. When a potentially threatening stimulus is perceived, a cascade of …
through biosignals. When a potentially threatening stimulus is perceived, a cascade of …
Speech emotion recognition using deep 1D & 2D CNN LSTM networks
We aimed at learning deep emotion features to recognize speech emotion. Two
convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D …
convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D …
Emotion recognition for multiple context awareness
Understanding emotion in context is a rising hotspot in the computer vision community.
Existing methods lack reliable context semantics to mitigate uncertainty in expressing …
Existing methods lack reliable context semantics to mitigate uncertainty in expressing …
Emotions don't lie: An audio-visual deepfake detection method using affective cues
We present a learning-based method for detecting real and fake deepfake multimedia
content. To maximize information for learning, we extract and analyze the similarity between …
content. To maximize information for learning, we extract and analyze the similarity between …
Survey on emotional body gesture recognition
Automatic emotion recognition has become a trending research topic in the past decade.
While works based on facial expressions or speech abound, recognizing affect from body …
While works based on facial expressions or speech abound, recognizing affect from body …
Multi-channel EEG-based emotion recognition via a multi-level features guided capsule network
In recent years, deep learning (DL) techniques, and in particular convolutional neural
networks (CNNs), have shown great potential in electroencephalograph (EEG)-based …
networks (CNNs), have shown great potential in electroencephalograph (EEG)-based …
Emotion recognition from EEG signal focusing on deep learning and shallow learning techniques
Recently, electroencephalogram-based emotion recognition has become crucial in enabling
the Human-Computer Interaction (HCI) system to become more intelligent. Due to the …
the Human-Computer Interaction (HCI) system to become more intelligent. Due to the …
M3er: Multiplicative multimodal emotion recognition using facial, textual, and speech cues
We present M3ER, a learning-based method for emotion recognition from multiple input
modalities. Our approach combines cues from multiple co-occurring modalities (such as …
modalities. Our approach combines cues from multiple co-occurring modalities (such as …
TC-Net: A Transformer Capsule Network for EEG-based emotion recognition
Deep learning has recently achieved remarkable success in emotion recognition based on
Electroencephalogram (EEG), in which convolutional neural networks (CNNs) are the mostly …
Electroencephalogram (EEG), in which convolutional neural networks (CNNs) are the mostly …