CogAdapt adapts clinical ECG foundation models to 3-lead wearable signals for cognitive load assessment via a LeadBridge adapter and ProFine progressive fine-tuning, outperforming scratch-trained models with macro-F1 of 0.626 and 0.768 on public datasets under leave-one-subject-out validation.
This baseline uses a shallow CNN inspired by VGG networks with multiple convolutional and max-pooling layers, followed by fully connected layers for binary classi- fication
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2representative citing papers
MambaGaze claims better eye-tracking cognitive-load classification using XMD masks/deltas plus bidirectional Mamba-2, but the abstract's numbers contradict the body's Table 1.
citing papers explorer
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MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data
MambaGaze claims better eye-tracking cognitive-load classification using XMD masks/deltas plus bidirectional Mamba-2, but the abstract's numbers contradict the body's Table 1.