The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) derived from electroencephalographic (EEG) data. Traditional methods involve averaging the EEG signa...
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jan 27, 2025
INTRODUCTION: Current models of Alzheimer's disease (AD) progression assume a common pattern and pathology, oversimplifying the heterogeneity of clinical AD.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 26, 2025
Contrastive learning is a type of deep learning technique trying to classify data or examples without requiring data labeling. Instead, it learns about the most representative features that contrast positive and negative pairs of examples. In literat...
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by repetitive stereotypical behavior and social impairment. Early diagnosis is essential for developing a treatment plan for autism. Although multi-site data ca...
Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sM...
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monito...
In the context of China's ongoing industrial revolution and technological transformation, there is a growing demand for advanced energy management solutions and the increasing role of artificial intelligence in various industries. This paper aims to ...
Magnetic resonance electrical properties tomography can extract the electrical properties of in-vivo tissue. To estimate tissue electrical properties, various reconstruction algorithms have been proposed. However, physics-based reconstructions are pr...
Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of these data complicates analysis of spatial gene expression patterns. We address this issue by deriving a to...
. Machine learning has enhanced the performance of decoding signals indicating human behaviour. Electroencephalography (EEG) brainwave decoding, as an exemplar indicating neural activity and human thoughts non-invasively, has been helpful in neural a...
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