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
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MambaGaze augments gaze features with observation masks and time-deltas then applies bidirectional Mamba-2 to reach 76.8 % and 73.1 % accuracy on CLARE and CL-Drive under leave-one-subject-out evaluation.
citing papers explorer
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CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation
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.
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MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data
MambaGaze augments gaze features with observation masks and time-deltas then applies bidirectional Mamba-2 to reach 76.8 % and 73.1 % accuracy on CLARE and CL-Drive under leave-one-subject-out evaluation.