Multi-layer transfer learning algorithm based on improved common spatial pattern for brain-computer interfaces.

In the application of brain-computer interface, the differences in imaging methods and brain structure between subjects hinder the effectiveness of decoding algorithms when applied on different subjects. Transfer learning has been designed to solve this problem. There have been many applications of transfer learning in motor imagery (MI), however the effectiveness is still limited due to the inconsistent domain alignment, lack of prominent data features and allocation of weights in trails.

Recognition of regions of stroke injury using multi-modal frequency features of electroencephalogram.

Objective: Nowadays, increasingly studies are attempting to analyze strokes in advance. The identification of brain damage areas is essential for stroke rehabilitation.

Approach: We proposed Electroencephalogram (EEG) multi-modal frequency features to classify the regions of stroke injury.

Predictive Model for Pulmonary Embolism in Pregnant and Postpartum Women: A 10-Year Retrospective Study.

Pulmonary embolism (PE) in pregnant and postpartum women is fatal, and risk assessment is crucial for effective and safe management, the aim of this retrospective study was to establish a nomogram for predicting the risk of PE in pregnant and postpartum women. Totally 343 subjects suspected of PE at the Obstetrics Department of Affiliated Dongyang Hospital of Wenzhou Medical University from January 2012 to December 2021 were retrospective analyzed in our study. Pregnant women suspected of PE and who underwent computed tomographic pulmonary angiography examination were included in the study.