NeuroStrata: An Electroencephalographic Connectivity-Aware Deep Representation Learning Framework for Dynamic Brain Network Analysis of Mental Stress
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 cl...
| Authors | Sayantan Acharya, Hamzeh Asgharnezhad, Abbas Khosravi et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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