- Leveraging spatial|temporal convolutional features for EEG|based ...🔍
- Spatial|Temporal Feature Fusion Neural Network for EEG|Based ...🔍
- Leveraging Graphic and Convolutional Neural Networks for Auditory ...🔍
- Leveraging spatial|temporal convolutional features for EEG ...🔍
- Spatial–temporal features|based EEG emotion recognition using ...🔍
- Adaptive Spatial–Temporal Aware Graph Learning for EEG|Based ...🔍
- Exploring Convolutional Neural Network Architectures for EEG ...🔍
- Spatial|Temporal Mamba Network for EEG|based Motor Imagery ...🔍
Leveraging spatial|temporal convolutional features for EEG|based ...
Leveraging spatial-temporal convolutional features for EEG-based ...
The framework consists of two modules. The first module is deep convolutional neural network (DCNN) architecture, which can represent the inter-channel ...
Leveraging spatial-temporal convolutional features for EEG-based ...
Highlights. •. A framework for emotion recognition from EEG signals is proposed. •. A novel spatial-temporal feature extraction framework is used to aggregate ...
Leveraging spatial-temporal convolutional features for EEG-based ...
Semantic Scholar extracted view of "Leveraging spatial-temporal convolutional features for EEG-based emotion recognition" by Yicheng An et al.
Leveraging spatial-temporal convolutional features for EEG-based ...
Besides, a 3D representation of ... [Show full abstract] EEG segment was proposed to combine features of signals from different frequency bands while preserving ...
Leveraging spatial-temporal convolutional features for EEG-based ...
Leveraging spatial-temporal convolutional features for EEG-based emotion recognition. https://doi.org/10.1016/j.bspc.2021.102743 ·. Journal: Biomedical Signal ...
Spatial-Temporal Feature Fusion Neural Network for EEG-Based ...
Abstract: The temporal and spatial information of electroencephalogram (EEG) are essential for the emotion recognition model to learn the ...
Leveraging Graphic and Convolutional Neural Networks for Auditory ...
Both networks first extract spatial features of the EEG, captur- ing interactions between channels, followed by temporal fea- ture ...
Leveraging spatial-temporal convolutional features for EEG ... - dblp
Bibliographic details on Leveraging spatial-temporal convolutional features for EEG-based emotion recognition.
Spatial–temporal features-based EEG emotion recognition using ...
... features-based EEG emotion recognition using graph convolution ... An Y, Xu N and Qu Z 2021 Leveraging spatial-temporal convolutional features for EEG-based ...
Adaptive Spatial–Temporal Aware Graph Learning for EEG-Based ...
Spatial graph convolution updates node feature representations by aggregating the features of neighboring nodes. Unlike traditional CNNs that are primarily used ...
MASA-TCN: Multi-Anchor Space-Aware Temporal Convolutional ...
The key innovation lies in the introduction of a space-aware temporal layer, which empowers TCN to capture spatial relationships among EEG ...
Exploring Convolutional Neural Network Architectures for EEG ...
CNNs can collect temporal information, automatically extract features, scale large datasets, and have the flexibility to adapt to various EEG applications. At ...
Spatial-Temporal Mamba Network for EEG-based Motor Imagery ...
However, these models have shown limitations in areas such as generalizability, contextuality and scalability when it comes to effectively ...
EEG-based Intention Recognition from Spatio-Temporal ...
Both cascade and parallel convolutional recurrent neural network models for precisely identifying human intended movements by effectively learning ...
Bidirectional feature pyramid attention-based temporal convolutional ...
However, the current performance of EEG signal decoding is not sufficient for real-world applications based on Motor Imagery EEG (MI-EEG). To ...
Leveraging Spatio-Temporal Dependency for Skeleton-Based ...
gal) features, unlike convolutional neural networks (CNNs), which aggregate left, identity, and right pixels features. To apply the dilated kernel to such ...
CTNet: a convolutional transformer network for EEG-based motor ...
We introduce a novel high-performance CTNet model, which effectively leverages CNN for local feature extraction and employs the Transformer ...
MASA-TCN: Multi-anchor Space-aware Temporal Convolutional ...
However, those two methods all utilized flattened feature vectors. Hence, the spatial information of EEG signals were not capably learned by the ...
Temporal-frequency-phase feature classification using 3D ... - Frontiers
Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to ...
Spatial–temporal graph convolutional network for Alzheimer ...
Different from existing studies that are based on either topological brain function characteristics or temporal features of EEG, the proposed ST ...