AIMC Topic: Brain
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Automatically Extracting and Utilizing EEG Channel Importance Based on Graph Convolutional Network for Emotion Recognition.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Graph convolutional network (GCN) based on the brain network has been widely used for EEG emotion recognition. However, most studies train their models directly without considering network dimensionality reduction beforehand. In fact, some nodes and ...
Riemannian Locality Preserving Method for Transfer Learning With Applications on Brain-Computer Interface.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Brain-computer interfaces (BCIs) have been widely focused and extensively studied in recent years for their huge prospect of medical rehabilitation and commercial applications. Transfer learning exploits the information in the source domain and appli...
Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMR...
DCNet: A Self-Supervised EEG Classification Framework for Improving Cognitive Computing-Enabled Smart Healthcare.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Cognitive computing endeavors to construct models that emulate brain functions, which can be explored through electroencephalography (EEG). Developing precise and robust EEG classification models is crucial for advancing cognitive computing. Despite ...
LightNet: A Novel Lightweight Convolutional Network for Brain Tumor Segmentation in Healthcare.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results i...
EEG based depression detection by machine learning: Does inner or overt speech condition provide better biomarkers when using emotion words as experimental cues?
Journal of psychiatric research
Aug 5, 2024
BACKGROUND: Objective diagnostic approaches need to be tested to enhance the efficacy of depression detection. Non-invasive EEG-based identification represents a promising area.
Brain-GCN-Net: Graph-Convolutional Neural Network for brain tumor identification.
Computers in biology and medicine
Aug 5, 2024
BACKGROUND: The intersection of artificial intelligence and medical image analysis has ushered in a new era of innovation and changed the landscape of brain tumor detection and diagnosis. Correct detection and classification of brain tumors based on ...
Machine learning of brain-specific biomarkers from EEG.
EBioMedicine
Aug 5, 2024
BACKGROUND: Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnes...
3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images.
IEEE transactions on neural networks and learning systems
Aug 5, 2024
This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D qua...
Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks.
IEEE transactions on neural networks and learning systems
Aug 5, 2024
Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and i...
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