Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review
EH Houssein, A Hammad, AA Ali - Neural Computing and Applications, 2022 - Springer
Affective computing, a subcategory of artificial intelligence, detects, processes, interprets,
and mimics human emotions. Thanks to the continued advancement of portable non …
and mimics human emotions. Thanks to the continued advancement of portable non …
EEG based emotion recognition: A tutorial and review
Emotion recognition technology through analyzing the EEG signal is currently an essential
concept in Artificial Intelligence and holds great potential in emotional health care, human …
concept in Artificial Intelligence and holds great potential in emotional health care, human …
EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM
In recent years, graph convolutional neural networks have become research focus and
inspired new ideas for emotion recognition based on EEG. Deep learning has been widely …
inspired new ideas for emotion recognition based on EEG. Deep learning has been widely …
EEG-based emotion recognition via channel-wise attention and self attention
Emotion recognition based on electroencephalography (EEG) is a significant task in the
brain-computer interface field. Recently, many deep learning-based emotion recognition …
brain-computer interface field. Recently, many deep learning-based emotion recognition …
EEG-based emotion recognition using regularized graph neural networks
Electroencephalography (EEG) measures the neuronal activities in different brain regions
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …
Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition
Multimodal signals are powerful for emotion recognition since they can represent emotions
comprehensively. In this article, we compare the recognition performance and robustness of …
comprehensively. In this article, we compare the recognition performance and robustness of …
EEG emotion recognition using dynamical graph convolutional neural networks
In this paper, a multichannel EEG emotion recognition method based on a novel dynamical
graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed …
graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed …
Emotionmeter: A multimodal framework for recognizing human emotions
In this paper, we present a multimodal emotion recognition framework called EmotionMeter
that combines brain waves and eye movements. To increase the feasibility and wearability …
that combines brain waves and eye movements. To increase the feasibility and wearability …
An efficient LSTM network for emotion recognition from multichannel EEG signals
Most previous EEG-based emotion recognition methods studied hand-crafted EEG features
extracted from different electrodes. In this article, we study the relation among different EEG …
extracted from different electrodes. In this article, we study the relation among different EEG …
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 …