IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
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2026
star Destacado
Dynamic Cognitive Load Estimation via Multi-Modal Eye Tracking and Electroencephalography Transformers
Understanding real-time cognitive workload is critical for designing adaptive educational interfaces and high-stakes decision-support systems. We propose CogFormer, a novel multimodal cross-attention transformer architecture that synchronizes non-invasive 64-channel EEG streams with high-frequency eye gaze fixations and pupillometry. Across a cohort of 140 participants performing complex algorithmic tasks, CogFormer achieved a 94.2% F1-score in detecting cognitive overload states with a sub-18ms inference latency on consumer-grade hardware.