Machine learning analysis of preparatory EEG activity shows subject-specific patterns that distinguish self-initiated from externally cued attention shifts, with strong contributions from higher-frequency bands and frontal regions.
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A hybrid deep learning model with physics regularization and SHAP analysis achieves 1.18% MAPE on ERCOT load data and up to 40.5% better performance on extreme events than its individual branches.
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Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions
Machine learning analysis of preparatory EEG activity shows subject-specific patterns that distinguish self-initiated from externally cued attention shifts, with strong contributions from higher-frequency bands and frontal regions.
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Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather
A hybrid deep learning model with physics regularization and SHAP analysis achieves 1.18% MAPE on ERCOT load data and up to 40.5% better performance on extreme events than its individual branches.