Huiguang He
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View article: Development of a machine learning-based predictive model for long-term adverse outcomes in neonatal bacterial meningitis
Development of a machine learning-based predictive model for long-term adverse outcomes in neonatal bacterial meningitis Open
Machine learning models, particularly the superior-performing Random Forest, are proven to reliably predict long-term adverse outcomes in NBM patients, aiding in the identification of high-risk individuals. Further validation in broader co…
View article: Urban Sprawl in the Yangtze River Delta: Spatio-Temporal Characteristics and Impacts on PM2.5
Urban Sprawl in the Yangtze River Delta: Spatio-Temporal Characteristics and Impacts on PM2.5 Open
Over the past three decades, the Yangtze River Delta has undergone a rapid urbanization phenomenon, resulting in pronounced urban sprawl that has significantly impacted regional sustainable development and air quality. This study construct…
View article: Bridging the behavior-neural gap: A multimodal AI reveals the brain's geometry of emotion more accurately than human self-reports
Bridging the behavior-neural gap: A multimodal AI reveals the brain's geometry of emotion more accurately than human self-reports Open
The ability to represent emotion plays a significant role in human cognition and social interaction, yet the high-dimensional geometry of this affective space and its neural underpinnings remain debated. A key challenge, the ‘behavior-neur…
View article: Relationships between multimodal ocular imaging and white matter hyperintensity volume
Relationships between multimodal ocular imaging and white matter hyperintensity volume Open
It is suggested that parameters from OCT and fundus photography are independently associated with WMH volume. These parameters may serve as potential biomarkers for the early detection and longitudinal monitoring of WMH, enabling precise i…
View article: NSSI contagion in adolescent friendships: exploring the impact of peer influence
NSSI contagion in adolescent friendships: exploring the impact of peer influence Open
Objective Although empirical evidence of NSSI contagion within adolescent friendships has been documented, the specific mechanisms remain poorly understood. The current study employed a longitudinal design to investigate the influence of N…
View article: Establishment of a Machine Learning‐Based Prediction Model for Short‐Term Adverse Prognosis in Neonatal Bacterial Meningitis
Establishment of a Machine Learning‐Based Prediction Model for Short‐Term Adverse Prognosis in Neonatal Bacterial Meningitis Open
Background Neonatal bacterial meningitis (NBM) is an extremely severe disease in the neonatal period. Early identification of high‐risk infants is critical for timely intervention, yet prognostic assessment remains challenging due to nonsp…
View article: Neural correlates of rumination in remitted depressive episodes: Brain network connectivity and topology analyses
Neural correlates of rumination in remitted depressive episodes: Brain network connectivity and topology analyses Open
BACKGROUND Rumination is a critical psychological factor contributing to the relapse of major depressive episodes (MDEs) and a core residual symptom in remitted MDEs. Investigating its neural correlations is essential for developing strate…
View article: NSSI Contagion in Adolescent Friendships: Explore the process of peer influence
NSSI Contagion in Adolescent Friendships: Explore the process of peer influence Open
Objective Although empirical evidence of NSSI contagion within adolescent friendships has been documented, the specific mechanisms remain poorly understood. The current study employed a longitudinal design to investigate the influence of N…
View article: Altered brain network dynamics during rumination in remitted depression
Altered brain network dynamics during rumination in remitted depression Open
Rumination is a known risk factor for depression relapse. Understanding its neurobiological mechanisms during depression remission can inform strategies to prevent relapse, yet the temporal dynamics of brain networks during rumination in r…
View article: Exploring EEG and Eye Movement Fusion for Multi-Class Target RSVP-BCI
Exploring EEG and Eye Movement Fusion for Multi-Class Target RSVP-BCI Open
Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interfaces (BCIs) facilitate high-throughput target image detection by identifying event-related potentials (ERPs) evoked in EEG signals. The RSVP-BCI systems effectively detect …
View article: Integrating Language-Image Prior into EEG Decoding for Cross-Task Zero-Calibration RSVP-BCI
Integrating Language-Image Prior into EEG Decoding for Cross-Task Zero-Calibration RSVP-BCI Open
Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an effective technology used for information detection by detecting Event-Related Potentials (ERPs). The current RSVP decoding methods can perform well in deco…
View article: A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability
A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability Open
A brain-computer interface (BCI) based on motor imagery (MI) can translate users' subjective movement-related mental state without external stimulus, which has been successfully used for replacing and repairing motor function. In contrast …
View article: Enhancing visual brain-computer interface through V1-targeted RTMS by modulating visual attention
Enhancing visual brain-computer interface through V1-targeted RTMS by modulating visual attention Open
Brain-computer interfaces (BCIs) enable users to control devices directly through brain activity. Despite recent advancements in machine-learning algorithms, the signal-to-noise ratio (SNR) of the brain’s responses still limits decoding pe…
View article: Enhanced Temporal Processing in Spiking Neural Networks for Static Object Detection Using 3D Convolutions
Enhanced Temporal Processing in Spiking Neural Networks for Static Object Detection Using 3D Convolutions Open
Spiking Neural Networks (SNNs) are a class of network models capable of processing spatiotemporal information, with event-driven characteristics and energy efficiency advantages. Recently, directly trained SNNs have shown potential to matc…
View article: Risk assessment of belt conveyor belt in thermal power plant based on Bow-tie model and uncertainty Fuzzy Dynamic Bayesian Network
Risk assessment of belt conveyor belt in thermal power plant based on Bow-tie model and uncertainty Fuzzy Dynamic Bayesian Network Open
In thermal power plants, coal conveyor belts pose significant risks that jeopardize the stability of the energy supply, underscoring the need for effective risk management. To address the complexity, uncertainty, and polymorphism issues in…
View article: NeuralOOD: Improving Out-of-Distribution Generalization Performance with Brain-machine Fusion Learning Framework
NeuralOOD: Improving Out-of-Distribution Generalization Performance with Brain-machine Fusion Learning Framework Open
Deep Neural Networks (DNNs) have demonstrated exceptional recognition capabilities in traditional computer vision (CV) tasks. However, existing CV models often suffer a significant decrease in accuracy when confronted with out-of-distribut…
View article: Optimizing Spatio-Temporal Information Processing in Spiking Neural Networks via Unconstrained Leaky Integrate-and-Fire Neurons and Hybrid Coding
Optimizing Spatio-Temporal Information Processing in Spiking Neural Networks via Unconstrained Leaky Integrate-and-Fire Neurons and Hybrid Coding Open
Spiking Neural Networks (SNN) exhibit higher energy efficiency compared to Artificial Neural Networks (ANN) due to their unique spike-driven mechanism. Additionally, SNN possess a crucial characteristic, namely the ability to process spati…
View article: Human-like object concept representations emerge naturally in multimodal large language models
Human-like object concept representations emerge naturally in multimodal large language models Open
The conceptualization and categorization of natural objects in the human mind have long intrigued cognitive scientists and neuroscientists, offering crucial insights into human perception and cognition. Recently, the rapid development of L…
View article: Identifying the Hierarchical Emotional Areas in the Human Brain Through Information Fusion
Identifying the Hierarchical Emotional Areas in the Human Brain Through Information Fusion Open
The brain basis of emotion has consistently received widespread attention, attracting a large number of studies to explore this cutting-edge topic. However, the methods employed in these studies typically only model the pairwise relationsh…
View article: Multi-label Class Incremental Emotion Decoding with Augmented Emotional Semantics Learning
Multi-label Class Incremental Emotion Decoding with Augmented Emotional Semantics Learning Open
Emotion decoding plays an important role in affective human-computer interaction. However, previous studies ignored the dynamic real-world scenario, where human experience a blend of multiple emotions which are incrementally integrated int…
View article: Reverse the auditory processing pathway: Coarse-to-fine audio reconstruction from fMRI
Reverse the auditory processing pathway: Coarse-to-fine audio reconstruction from fMRI Open
Drawing inspiration from the hierarchical processing of the human auditory system, which transforms sound from low-level acoustic features to high-level semantic understanding, we introduce a novel coarse-to-fine audio reconstruction metho…
View article: Open-vocabulary Auditory Neural Decoding Using fMRI-prompted LLM
Open-vocabulary Auditory Neural Decoding Using fMRI-prompted LLM Open
Decoding language information from brain signals represents a vital research area within brain-computer interfaces, particularly in the context of deciphering the semantic information from the fMRI signal. However, many existing efforts co…
View article: Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity
Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity Open
Reconstructing human dynamic vision from brain activity is a challenging task with great scientific significance. Although prior video reconstruction methods have made substantial progress, they still suffer from several limitations, inclu…
View article: CLIP-MUSED: CLIP-Guided Multi-Subject Visual Neural Information Semantic Decoding
CLIP-MUSED: CLIP-Guided Multi-Subject Visual Neural Information Semantic Decoding Open
The study of decoding visual neural information faces challenges in generalizing single-subject decoding models to multiple subjects, due to individual differences. Moreover, the limited availability of data from a single subject has a con…
View article: A Temporal-Spectral Fusion Transformer with Subject-Specific Adapter for Enhancing RSVP-BCI Decoding
A Temporal-Spectral Fusion Transformer with Subject-Specific Adapter for Enhancing RSVP-BCI Decoding Open
The Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient technology for target retrieval using electroencephalography (EEG) signals. The performance improvement of traditional decoding methods relies…