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Found 4 papers, 2 papers with code
Temporal Aware Mixed Attention-based Convolution and Transformer Network (MACTN) for EEG Emotion Recognition
In this study, we propose MACTN, a hierarchical hybrid model for jointly modeling local and global temporal information.
LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability
By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net can effectively integrate features from multiple dimensions, resulting in improved classification performance across various BCI tasks.
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework
Compared to the vanilla EEGNet and ConvNet, the proposed SDDA framework was able to boost the MI classification accuracy by 15. 2%, 10. 2% respectively in IIA dataset, and 5. 5%, 4. 2% in IIB dataset.
Dynamic Functional Connectivity and Graph Convolution Network for Alzheimer's Disease Classification
Alzheimer's disease (AD) is the most prevalent form of dementia.
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