A5100425719- Functional Brain Connectivity Studies
- EEG and Brain-Computer Interfaces
- Neural dynamics and brain function
- Mental Health Research Topics
- Emotion and Mood Recognition
- Advanced Neuroimaging Techniques and Applications
- Neuroscience and Neural Engineering
- Neural and Behavioral Psychology Studies
- Advanced MRI Techniques and Applications
- Heart Rate Variability and Autonomic Control
- Bipolar Disorder and Treatment
- Gaze Tracking and Assistive Technology
- Bioinformatics and Genomic Networks
- Health, Environment, Cognitive Aging
- Gene expression and cancer classification
- ECG Monitoring and Analysis
- Pain Mechanisms and Treatments
- Blind Source Separation Techniques
- Fault Detection and Control Systems
- Traumatic Brain Injury Research
- Gene Regulatory Network Analysis
- Stroke Rehabilitation and Recovery
- Neurological disorders and treatments
- Neuroscience and Music Perception
- Speech Recognition and Synthesis
First Affiliated Hospital of Hebei Medical University
2025
West China Second University Hospital of Sichuan University
2025
Sichuan University
2023-2025
Chengdu Women's and Children's Central Hospital
2025
Hebei Medical University
2025
First Affiliated Hospital of Chengdu Medical College
2025
Fujian Medical University
2024
Shenzhen University
2020-2024
Northwestern Polytechnical University
2022-2024
Shenzhen University Health Science Center
2018-2024
How to effectively and efficiently extract valid reliable features from high-dimensional electroencephalography (EEG), particularly how fuse the spatial temporal dynamic brain information into a better feature representation, is critical issue in data analysis. Most current EEG studies work task driven manner explore with supervised model, which would be limited by given labels great extent. In this paper, we propose practical hybrid unsupervised deep convolutional recurrent generative...
Affective brain-computer interface based on electroencephalography (EEG) is an important branch in the field of affective computing. However, individual differences EEG emotional data and noisy labeling problem subjective feedback seriously limit effectiveness generalizability existing models. To tackle these two critical issues, we propose a novel transfer learning framework with Prototypical Representation Pairwise Learning ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML"...
Determining and decoding emotional brain processes under ecologically valid conditions remains a key challenge in affective neuroscience. The current functional Magnetic Resonance Imaging (fMRI) based emotion studies are mainly on brief isolated episodes of induction, while sustained experience naturalistic environments that mirror daily life experiences scarce. Here we used 12 different 10-minute movie clips as emotion-evoking procedures n = 52 individuals to explore emotion-specific fMRI...
Psychological resilience is an important personality trait whose decrease associated with many common psychiatric disorders, but the neural mechanisms underlying it remain largely unclear. In this study, we aimed to explore correlates of psychological in healthy adults by investigating its relationship functional brain network flexibility, a fundamental dynamic feature defined switching frequency modular community structures.Resting-state magnetic resonance imaging (fMRI) scans were acquired...
Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking advantages electroencephalogram (EEG) signals (i.e., high time resolution) video‐based evoking rich media information), video‐triggered EEG been proven useful tool to conduct...
EEG microstates have been widely adopted to understand the complex and dynamic-changing process in dynamic brain systems, but how are temporally modulated by emotion dynamics is still unclear. An investigation of under video-evoking modulation would provide a novel insight into understanding temporal functional networks.In present study, we postulate that emotional states dynamically modulate microstate patterns, perform an in-depth between video-watching task. By mapping from...
Objective: The brain functional alterations at regional and network levels in post-traumatic stress disorder patients are still unclear. This study explored with resting-state magnetic resonance imaging evaluated the relationship between function clinical indices disorder. Methods: Amplitude of low-frequency fluctuation seed-based connectivity analyses were conducted among typhoon survivors ( n = 27) without 33) healthy controls 30) to assess spontaneous activity network-level function....
Machine learning has been increasingly used in decoding brain states from functional magnetic resonance imaging (fMRI). One important application is to classify the levels of pain perception patients' fMRI for clinical assessment. However, huge number features and complex relationships between affect performance classification models heavily. In this article, we introduce a new fuzzy-rule-based hybrid optimization approach dimension reduction multiclassification problems using chaotic map,...
Recognizing human emotions from complex, multivariate, and non-stationary electroencephalography (EEG) time series is essential in affective brain-computer interface. However, because continuous labeling of ever-changing emotional states not feasible practice, existing methods can only assign a fixed label to all EEG timepoints emotion-evoking trial, which overlooks the highly dynamic signals. To solve problems high reliance on labels ignorance time-changing information, this paper we...
To examine the knowledge, attitudes and willingness of caregivers preterm infants toward autism spectrum disorder (ASD).
The scarcity of speaker-annotated far-field speech presents a significant challenge in developing high-performance speaker verification (SV) systems. While data augmentation using large-scale near-field has been common strategy to address this limitation, the mismatch acoustic environments between and significantly hinders improvement SV effectiveness. In paper, we propose an adaptive approach leveraging NaturalSpeech3, pre-trained foundation text-to-speech (TTS) model, convert into by...
Non-suicidal self-injury (NSSI) is a serious threat to the physical and mental health of adolescents, significantly increasing risk suicide attracting widespread public concern. Electroencephalography (EEG), as an objective tool for identifying brain disorders, holds great promise. However, extracting meaningful reliable features from high-dimensional EEG data, especially by integrating spatiotemporal dynamics into informative representations, remains major challenge. In this study, we...
EEG signals exhibit commonality and variability across subjects, sessions, tasks. But most existing studies focus on mean group effects (commonality) by averaging over trials subjects. The substantial intra- inter-subject of have often been overlooked. recently significant technological advances in machine learning, especially deep brought innovations to signal application many aspects, but there are still great challenges cross-session, cross-task, cross-subject decoding. In this work, an...
Unlike most conventional techniques with static model assumption, this paper aims to estimate the time-varying parameters and identify significant genes involved at different timepoints from time course gene microarray data. We first formulate parameter identification problem as a new maximum posteriori probability estimation so that prior information can be incorporated regularization terms reduce large variance of high dimensional problem. Under framework, sparsity temporal consistency are...