Graph-based deep learning for medical diagnosis and analysis: past, present and future
With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …
problems have been tackled. It has become critical to explore how machine learning and …
Research review for broad learning system: Algorithms, theory, and applications
In recent years, the appearance of the broad learning system (BLS) is poised to
revolutionize conventional artificial intelligence methods. It represents a step toward building …
revolutionize conventional artificial intelligence methods. It represents a step toward building …
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 …
Automated emotion recognition: Current trends and future perspectives
Background Human emotions greatly affect the actions of a person. The automated emotion
recognition has applications in multiple domains such as health care, e-learning …
recognition has applications in multiple domains such as health care, e-learning …
EEG-based BCI emotion recognition: A survey
Affecting computing is an artificial intelligence area of study that recognizes, interprets,
processes, and simulates human affects. The user's emotional states can be sensed through …
processes, and simulates human affects. The user's emotional states can be sensed through …
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 …
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 …
GLFANet: A global to local feature aggregation network for EEG emotion recognition
Recently, emotion recognition technology based on electroencephalogram (EEG) signals is
widely used in areas such as human–computer interaction and disease diagnosis …
widely used in areas such as human–computer interaction and disease diagnosis …
Dynamic domain adaptation for class-aware cross-subject and cross-session EEG emotion recognition
It is vital to develop general models that can be shared across subjects and sessions in the
real-world deployment of electroencephalogram (EEG) emotion recognition systems. Many …
real-world deployment of electroencephalogram (EEG) emotion recognition systems. Many …
Hierarchical dynamic graph convolutional network with interpretability for EEG-based emotion recognition
Graph convolutional networks (GCNs) have shown great prowess in learning topological
relationships among electroencephalogram (EEG) channels for EEG-based emotion …
relationships among electroencephalogram (EEG) channels for EEG-based emotion …