With the advancement of multimodal sensing and AI technologies, intelligent diagnosis and treatment of mental health disorders using EEG, facial video, speech, and text data have gained increasing attention. This workshop focuses on major disorders such as depression, Alzheimer's disease, and epilepsy, exploring key topics including data fusion, feature extraction, emotion recognition, and clinical application. It aims to foster interdisciplinary collaboration and promote the development of interpretable and deployable intelligent healthcare systems for mental well-being.

Track Committee
• Track Chair
Prof. Chengyu Liu, Southeast University
Assoc. Prof. Chunfeng Yang, Southeast University
• Technical Committee
Assoc. Prof. Wentao Xiang, Nanjing Medical University
Assoc. Prof. Zhuhong Shao , Capital Normal University
Asst. Prof. Miao Cao, Swinburne University of Technology
Dr. Lulu Zhao, Southeast University
Assoc. Prof. Zongya Zhao, Xinxiang Medical University

Topics
• Multimodal Data Fusion for Mental Disorder Detection
EEG-Speech-Facial Cross-Modality Feature Fusion Techniques
Fusion Learning for Early Detection of Depression and Alzheimer' s Disease
Consistency and Heterogeneity Modeling of Multisource Data
• EEG Signal Processing and Decoding for Mental Health Monitoring
EEG Feature Extraction for Emotional State Recognition
Frequency-Band Based Cognitive Impairment Detection and Classification
Epileptic Seizure Prediction and Abnormal EEG Detection Algorithms)
• Role of Facial Video and Speech in Cognitive Impairment Assessment
Micro-expression Analysis for Early Mental Disorder Detection
Speech Emotion Recognition for Mental State Assessment
Non-contact Behavioral Analysis using Combined Video and Speech
• Modeling of Textual and Language Data for Psychological Assessment
Depression and Anxiety Detection via Natural Language Processing (NLP)
Analysis of Linguistic Style and Semantic Changes in Cognitive Decline
Multimodal Sentiment Analysis and Joint Text Modeling
• Design and Clinical Validation of Multimodal Intelligent Diagnostic Systems
Design Architecture of Personalized Mental Health Assessment Systems
Interpretability and Clinical Usability of Multimodal Diagnostic Platforms
Model Generalization and Stability Evaluation on Real-World Datasets

Publication