Multimodal approach to public health interventions using EGG and mobile health technologies

Public health interventions increasingly integrate multimodal data sources, such as Electroencephalogram (EEG) data, to enhance monitoring and predictive capabilities for mental health conditions. However, traditional models often face challenges with the complexity and high dimensionality of EEG signals. While recent advancements like Contrastive Language-lmage Pre-training(CLIP) models excel in cross-modal understanding, their application to EEG-based tasks remains limited due to the unique characteristics of EEG data.

In response, we introduce PH-CLIP (Public Health Contrastive Language-lmage Pretraining), a novel framework that combines CLIP's representational power with a multi-scale fusion mechanism designed specifically for EEG data within mobile health technologies. PH-CLIP employs hierarchical feature extraction to capture the temporal dynamics of EEG signals, aligning them with contextually relevant textual descriptions for improved public health insights. Through a multi-scale fusion layer, PH-CLIP enhances interpretability and robustness in EEG embeddings, thereby supporting more accurate and scalable interventions across diverse public health applications.

Experimental results indicate that PH-CLIP achieves significant improvements in EEG classification accuracy and mental health prediction efficiency compared to leading EEG analysis models. This framework positions PH-CLIP as a transformative tool in public health monitoring, with the potential to advance large-scale mental health interventions through integrative mobile health technologies.