Zihao Yan
Biographic Data
| ID | 7964034 |
|---|---|
| NAME | Zihao Yan |
| GIVEN NAMES | Zihao |
| FAMILY NAME | Yan |
| SIGNATURE | YAN Z |
| AFFILIATIONS | Zhejiang Normal University |
| ORCID | 0009-0005-9447-4913 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2026 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Multidimensional EEG features integration with feature selection strategy for precision diagnosis of depressive disorders
Depressive disorder (DD), a leading global cause of disability, lacks objective diagnostic biomarkers due to reliance on subjective clinical criteria. This study introduces an algorithm-driven framework integrating multidimensional EEG features, dynamic time-window optimization, feature selection and machine learning to address this gap. Resting-state EEG signals were acquired from 70 DD patients and 30 healthy controls (HC). Three-dimensional ne…
A novel deep learning model for objective quantification of generalized anxiety disorder severity using EEG functional connectivity
Generalized anxiety disorder (GAD) is a prevalent and disabling psychiatric condition, yet its severity is still assessed mainly through clinical interviews and self-report scales, which lack objective neurobiological markers. This study aimed to develop an electroencephalography (EEG)-based deep learning (DL) model for objective quantification of GAD severity based on functional connectivity (FC) features. Resting-state EEG was recorded for 10 m…
No prominent works on this page.
Multidimensional EEG features integration with feature selection strategy for precision diagnosis of depressive disorders
Depressive disorder (DD), a leading global cause of disability, lacks objective diagnostic biomarkers due to reliance on subjective clinical criteria. This study introduces an algorithm-driven framework integrating multidimensional EEG features, dynamic time-window optimization, feature selection and machine learning to address this gap. Resting-state EEG signals were acquired from 70 DD patients and 30 healthy controls (HC). Three-dimensional ne…
A novel deep learning model for objective quantification of generalized anxiety disorder severity using EEG functional connectivity
Generalized anxiety disorder (GAD) is a prevalent and disabling psychiatric condition, yet its severity is still assessed mainly through clinical interviews and self-report scales, which lack objective neurobiological markers. This study aimed to develop an electroencephalography (EEG)-based deep learning (DL) model for objective quantification of GAD severity based on functional connectivity (FC) features. Resting-state EEG was recorded for 10 m…
Electroencephalography (2 works) · Functional Brain Connectivity Studies (2 works) · Pattern recognition (psychology (2 works) · Anxiety (1 work) · Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes (1 work) · BETA (programming language (1 work) · Convolutional neural network (1 work) · Deep learning (1 work) · Discriminative model (1 work) · EEG and Brain-Computer Interfaces (1 work)