Model averaging with sequential weight selection, based on mean square error, KL-divergence, and linear correlation, is evaluated for all three performance metrics and compared to the performance of individual models.

Values are bolded to designate the model that performs best under each criterion for each participant. While MSE-based averaging consistently outperformed the individual models, the ideal scheme varies across patients.