NeuroStrata: An Electroencephalographic Connectivity-Aware Deep Representation Learning Framework for Dynamic Brain Network Analysis of Mental Stress

Researchers introduced NeuroStrata, a connectivity-aware deep representation learning framework for EEG-based mental stress analysis, achieving a peak accuracy of 97.3% using the LAION-CLIP-ViT-L14 backbone with a Support Vector Machine classifier on the 32-channel SAM 40 dataset. The framework models time-varying directed connectivity via Time-Varying Partial Directed Coherence (TV-PDC) and uses pretrained CNNs and Vision Transformers to extract deep connectivity embeddings, with beta-band connectivity proving most discriminative.

arXiv:2608.20354v1 Announce Type: cross Abstract: This study introduces NeuroStrata, a connectivity-aware deep representation learning framework for EEG-based mental stress analysis using Time-Varying Partial Directed Coherence TV-PDC . Unlike conventional EEG classification approaches based on static features, NeuroStrata models the temporal evolution of frequency-specific directed connectivity across distributed brain regions. EEG signals from the 32-channel SAM 40 dataset recorded during mental arithmetic tasks were used to generate TV-PDC connectivity maps. These maps were processed using pretrained Convolutional Neural Networks CNNs and Vision Transformers ViTs to extract deep connectivity embeddings, which were subsequently classified using lightweight machine learning models. Experimental results demonstrate that beta-band connectivity provides the highest discriminative capability, achieving a peak accuracy of 97.3% using the LAION-CLIP-ViT-L14 backbone with a Support Vector Machine classifier, while alpha-band connectivity exhibits consistently stable performance across model configurations. Connectivity analysis revealed prominent frontal-driven alpha influences and centrally integrated beta connectivity patterns associated with stress-related neural dynamics. Temporal evaluation further indicated that classification performance stabilizes in mid-to-late temporal windows, suggesting progressive consolidation of stress-related connectivity signatures. The proposed framework integrates time-varying effective connectivity modelling with deep representation learning to provide an interpretable and automated approach for EEG-based mental stress analysis.