IEEE Transactions on Biomedical Engineering

Featured Articles
C2MA-Net: Cross-modal Cross-Attention Network for Acute Ischemic Stroke Lesion Segmentation based on CT Perfusion Scans
This work adds a cross-modal and cross-attention (C2MA) mechanism into a deep learning network aiming to improve accuracy and efficacy of acute ischemic stroke (AIS) lesion segmentation from CT perfusion maps. The proposed network uses a C2MA module directly to establish a spatial-wise relationship by using the multigroup non-local attention operation between two modal features and performs dynamic group-wise recalibration through group attention block. This study demonstrates the advantages of applying C2MA-network to segment AIS lesions, which yields promising segmentation accuracy and proves the potential of applying cross-modal interactions in attention to assist in identifying new imaging biomarkers for more accurately predicting AIS prognosis in future studies... Read more
Articles
Learning Invariant Patterns Based on a Convolutional Neural Network and Big Electroencephalography Data for Subject-Independent P300 Brain-Computer Interfaces
A brain-computer interface (BCI) measures and analyzes brain activity and converts this activity into computer commands to control external devices. In contrast to traditional BCIs that require a subject-specific calibration process before being operated, a subject-independent BCI learns a subject-independent... Read more
Featured Articles
Learning Invariant Patterns Based on a Convolutional Neural Network and Big Electroencephalography Data for Subject-Independent P300 Brain-Computer Interfaces
A brain-computer interface (BCI) measures and analyzes brain activity and converts this activity into computer commands to control external devices. In contrast to traditional BCIs that require a subject-specific calibration process before being operated, a subject-independent BCI learns a subject-independent... Read more