Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering
Fig 3
Performance over the process of adaptive OFC-PPF.
(A) Success rate as a function of time from the start of the experiment in one session. Success rate is calculated in sliding 2 min windows. The decoder was initialized using a visual feedback seed. Adaptive OFC-PPF was then run as described in the flow-chart in Fig 2. The architecture started by providing assisted training and adaptation. After the first assist period, which consisted of 3 discrete assist levels, performance in the test period exceeded the desired threshold of 5 trials/min and hence the architecture stopped the assisted training (vertical dashed black line). We stopped the adaptation at the vertical dashed blue line, after which the trained point process model was used in a random-walk PPF to control the cursor. Green lines show the 99% upper bound on the chance level performance during the assisted training. Assisted performance is above the 99% chance level. The horizontal dashed line shows the mean manual task performance with the arm on that day. (B) Randomly selected center-out trials on this day after adaptation stopped.