Figure 3. Performance remains high when daily decoder retraining is shortened (or unsupervised).

a, To account for neural activity changes that accrue over time, we retrained our handwriting decoder each day before evaluating it. Here, we simulated offline how decoding performance would have changed if less than the original 50 calibration sentences were used. Lines show the mean error rate across all data and shaded regions indicate 95% CIs. b, Copy typing data from eight sessions were used to assess whether less calibration data are required if sessions occur closer in time. All session pairs (X, Y) were considered. Decoders were first initialized using training data from session X and earlier, and then evaluated on session Y under different retraining methods (no retraining, retraining with limited calibration data, or unsupervised retraining). Lines show the average raw error rate and shaded regions indicate 95% CIs.