Domain adaptation spatial feature perception neural network for cross-subject EEG emotion recognition.

Emotion recognition is a critical research topic within affective computing, with potential applications across various domains. Currently, EEG-based emotion recognition, utilizing deep learning frameworks, has been effectively applied and achieved commendable performance. However, existing deep learning-based models face challenges in capturing both the spatial activity features and spatial topology features of EEG signals simultaneously.

Multiscale dilated convolutional neural network for Atrial Fibrillation detection.

Atrial Fibrillation (AF), a type of heart arrhythmia, becomes more common with aging and is associated with an increased risk of stroke and mortality. In light of the urgent need for effective automated AF monitoring, existing methods often fall short in balancing accuracy and computational efficiency. To address this issue, we introduce a framework based on Multi-Scale Dilated Convolution (AF-MSDC), aimed at achieving precise predictions with low cost and high efficiency.

Hybrid transfer learning strategy for cross-subject EEG emotion recognition.

Emotion recognition constitutes a pivotal research topic within affective computing, owing to its potential applications across various domains. Currently, emotion recognition methods based on deep learning frameworks utilizing electroencephalogram (EEG) signals have demonstrated effective application and achieved impressive performance. However, in EEG-based emotion recognition, there exists a significant performance drop in cross-subject EEG Emotion recognition due to inter-individual differences among subjects.