A Multimodal EEG Framework for Cognitive Stress Detection with Retrieval-Augmented Explainability
The Core Challenge
Traditional EEG-based cognitive stress diagnostic pipelines function as black-box classifiers without clinically interpretable rationale or context grounding. Furthermore, isolated physiological telemetry fails to integrate subject metadata, environmental stressors, and authoritative medical literature needed for clinical decision support.
Methodological Contribution
Introduces a unified dual-pathway architecture: a physiological encoder (CNN + Bi-LSTM + multi-head attention) fused with a semantic context encoder (Sentence-BERT), paired with a post-prediction RAG explainer retrieving relevant neurobiological findings to generate context-grounded, peer-verifiable diagnostic rationales.
Dual-Branch Multimodal EEG Encoder + Retrieval-Augmented Generation (RAG)
⢠1D CNN Spatial Filtering
⢠Bidirectional LSTM Temporal Map
⢠Multi-Head Temporal Attention
⢠Subject cognitive baseline
⢠Environmental metadata
⢠Dense contextual vectors
⢠Key/Value = EEG states
⢠Dynamic weight projection
⢠Softmax Stress Probabilities
⢠Vector similarity retrieval
⢠Grounded explanation generation
⢠Diagnostic rationale report
Methodological Pipeline & Tensor Transformations
EEG Signal Preprocessing
Multi-channel raw EEG signals are filtered (0.5â50 Hz bandpass), normalized across channels, and segmented into fixed temporal windows with ICA eye-blink removal.
Temporal & Spatial Feature Learning
Convolutional feature maps extract localized micro-patterns, followed by Bidirectional LSTM layers capturing long-range temporal dependencies weighted by attention.
Contextual Semantic Encoding
Experimental condition metadata, subject background, and contextual variables are encoded using Sentence-BERT into semantic vectors.
Cross-Attention Multimodal Fusion
Physiological representations and contextual embeddings are fused via gated cross-attention to form an integrated cognitive stress profile.
Classification & RAG Rationalization
Softmax classifier yields stress state probabilities. The latent state queries a neuroscientific literature vector database, generating evidence-backed clinical justifications.
Reported Evaluation Results
Scientific Guardrails & Validation Boundaries
Notice: The research manuscript clearly notes that while in-dataset accuracy reaches 99.31%, cross-dataset generalization exhibits variance due to varying electrode configurations and individual neural baseline shifts. This architecture is designed for research and decision support, not standalone clinical diagnosis. Future work addresses cross-subject transfer learning.
Translation to Enterprise Production
Translating this research into enterprise AI demonstrates that high-stakes ML systems must transcend standalone statistical classifiers. By architecting a hybrid RAG layer directly tied to the latent classification states, the system yields verifiable decision support suitable for auditability and compliance in critical environments.
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