Fine-tuning brain foundation models on EEG data yields improved accuracy for real-time cognitive load estimation in BCIs while using SHAP to show consistent focus on prefrontal regions linked to cognitive control.
A unified approach to inter- preting model predictions
2 Pith papers cite this work. Polarity classification is still indexing.
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Machine learning approaches achieve equivalent AUROC of 1.0 and near-perfect accuracy to deep learning for OOD detection on over 60,000 medical images but with substantially lower end-to-end latency.
citing papers explorer
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Cognitive Load Estimation Using Brain Foundation Models and Interpretability for BCIs
Fine-tuning brain foundation models on EEG data yields improved accuracy for real-time cognitive load estimation in BCIs while using SHAP to show consistent focus on prefrontal regions linked to cognitive control.
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A Comparative Study of Machine Learning and Deep Learning for Out-of-Distribution Detection
Machine learning approaches achieve equivalent AUROC of 1.0 and near-perfect accuracy to deep learning for OOD detection on over 60,000 medical images but with substantially lower end-to-end latency.