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Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) enable direct interactions between the brain and external environments, with applications in medical rehabilitation, motor substitution, gaming, and entertainment. Traditional methods that model the non-Euclidean characteristics of EEG signals demonstrate robustness and high performance, but they suffer from significant computational costs and are typically restricted to a single BCI paradigm. This article addresses these limitations by utilizing a diffeomorphism from Riemannian manifolds to the Cholesky space, which simplifies the solution process and enables application across multiple BCI paradigms.
View Article and Find Full Text PDFDual-branch dynamic hierarchical U-Net with multi-layer space fusion attention for medical image segmentation.
Sci Rep
March 2025
College of Pharmacy, Qiqihar Medical University, Qiqihar, 161003, China.
Accurate segmentation of organs or lesions from medical images is essential for accurate disease diagnosis and organ morphometrics. Previously, most researchers mainly added feature extraction modules and simply aggregated the semantic features to U-Net network to improve the segmentation accuracy of medical images. However, these improved U-Net networks ignore the semantic differences of different organs in medical images and lack the fusion of high-level semantic features and low-level semantic features, which will lead to blurred or miss boundaries between similar organs and diseased areas.
View Article and Find Full Text PDFLearning reshapes the hippocampal representation hierarchy.
Proc Natl Acad Sci U S A
March 2025
Institute of Science and Technology Austria, Klosterneuburg AT-3400, Austria.
A key feature of biological and artificial neural networks is the progressive refinement of their neural representations with experience. In neuroscience, this fact has inspired several recent studies in sensory and motor systems. However, less is known about how higher associational cortical areas, such as the hippocampus, modify representations throughout the learning of complex tasks.
View Article and Find Full Text PDFCoding of egocentric distance in the macaque ventral intraparietal area.
iScience
March 2025
Applied Physics and Neurophysics, Philipps-Universität Marburg, Karl-von-Frisch Straße 8a, 35043 Marburg, Germany.
The encoding of three-dimensional visual information is of important in everyday life. Eye-movements challenge this spatial encoding: they shift the image of the outside world across the retina. In the macaque ventral intraparietal area (VIP), many neurons encode visual information irrespective of horizontal and vertical eye position.
View Article and Find Full Text PDFstAI: a deep learning-based model for missing gene imputation and cell-type annotation of spatial transcriptomics.
Nucleic Acids Res
February 2025
School of Mathematical Sciences, Fudan University, Shanghai 200433, China.
Spatial transcriptomics technology has revolutionized our understanding of cellular systems by capturing RNA transcript levels in their original spatial context. Single-cell spatial transcriptomics (scST) offers single-cell resolution expression level and precise spatial information of RNA transcripts, while it has a limited capacity for simultaneously detecting a wide range of RNA transcripts, hindering its broader applications. Characterizing the whole transcriptome level and comprehensively annotating cell types represent two significant challenges in scST applications.
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