Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering
Fig 5
Spike-event-based adaptation enables faster convergence.
(A) Performance over time for adaptive OFC-PPF (solid) and SmoothBatch OFC-PPF (dashed) run on two sets (red and blue) of two consecutive days that started from the same initial parameters. Vertical lines show the time point where assistance stopped as the subject’s non-assisted success rate in the test period at that point exceeded the desired minimum threshold of 5 trials/min. Success rate is calculated in sliding 2 min windows. (B, C) Average success rate across sessions as a function of time into the adaptive session for SmoothBatch OFC-PPF in (B) and Adaptive OFC-PPF in (C). Blue curves show the mean success rate over 12 days of experiments for each decoder and shading reflects the standard deviation across these days. The red bar shows the time range in which the BMI architecture stopped the assisted training across days. Spike-event-based adaptation resulted in faster convergence and less variability compared with SmoothBatch adaptation that updated the decoder parameters on a slower adaptation time-scale, i.e., once every 90 seconds.