A novel framework for EEG-based emotion recognition is proposed. The framework consists of two modules. The first module is deep convolutional neural network ( ...
A novel spatial-temporal feature extraction framework is used to aggregate spatial-temporal characteristic of EEG signals.
Oct 22, 2024 · In these works, different feature extraction techniques along with deep neural networks and RNNs are presented to detect emotions based on the ...
Spatial–temporal features-based EEG emotion recognition using graph convolution network and long short-term memory · Computer Science. Physiological measurement.
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Aug 16, 2022 · Abstract: The dynamic uncertain relationship among each brain region is a necessary factor that limits EEG-based emotion recognition.
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In this study, we present a novel EEG emotion recognition model, named GraphEmotionNet, designed to enhance the accuracy of EEG-based emotion recognition.
We propose a new deep learning model named three-dimension convolution attention neural network (3DCANN) for EEG emotion recognition in this paper.
Dec 9, 2024 · In this paper, temporal, spatial and connective features are extracted from EEG signals gotten around the head, and used for emotion recognition ...
Apr 7, 2022 · Abstract: The temporal and spatial information of electroencephalogram (EEG) are essential for the emotion recognition model to learn the ...
We develop spatial–temporal features-based EEG emotion recognition using a graph convolution network (GCN) and long short-term memory (LSTM), named ERGL.