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1.
Figure 2

Figure 2. System identification.. From: A Brain-Machine Interface for Control of Medically-Induced Coma.

(a) and (b) show two sample fitted system responses. The measured BSP trace in response to a preliminary bolus of propofol is shown in grey and the response of the second-order system model in (2) fitted using nonlinear least-squares is shown in red.

Maryam M. Shanechi, et al. PLoS Comput Biol. 2013 October;9(10):e1003284.
2.
Figure 6

Figure 6. In vivo real-time BMI control of burst suppression in individual rodents.. From: A Brain-Machine Interface for Control of Medically-Induced Coma.

In each subfigure, the top panel shows the estimated closed-loop controlled BSP trace (black) and the time-varying target level (green), and the bottom panel shows the corresponding BMI drug infusion rate using the bounded LQR strategy (a–e) and the MPC strategy (f).

Maryam M. Shanechi, et al. PLoS Comput Biol. 2013 October;9(10):e1003284.
3.
Figure 3

Figure 3. Simulated closed-loop controlled BSP traces.. From: A Brain-Machine Interface for Control of Medically-Induced Coma.

In each subfigure, the top panel shows the BSP traces and the bottom panel shows the drug infusion rate. In the top panels, sample trials of the closed-loop controlled BSP traces are shown in black and the corresponding estimated BSP traces are shown in grey. The time-varying target BSP level is shown in green. The bottom panel shows the corresponding controller infusion rates. Each subfigure (a–f) corresponds to one possible permutation of the 3 BSP target levels.

Maryam M. Shanechi, et al. PLoS Comput Biol. 2013 October;9(10):e1003284.
4.
Figure 4

Figure 4. Comparison of the bounded LQR and MPC strategies.. From: A Brain-Machine Interface for Control of Medically-Induced Coma.

In each subfigure, the top panel shows the closed-loop controlled BSP traces using the bounded LQR control strategy and using the MPC strategy with various time horizons, time samples (seconds). The bottom panel shows the corresponding drug infusion rates. The only constraint imposed here is non-negativity of the drug infusion rate. Each subfigure (a–f) corresponds to one possible permutation of the 3 BSP target levels.

Maryam M. Shanechi, et al. PLoS Comput Biol. 2013 October;9(10):e1003284.
5.
Figure 7

Figure 7. Reliability of the real-time BMI.. From: A Brain-Machine Interface for Control of Medically-Induced Coma.

Each subfigure (a–f) corresponds to one of the six real-time BMI experiments (Figure 6) and shows the modified boxplot summaries for the absolute error distribution at each of the levels used in that experiment. The lower and upper end of the boxes represent the 25th and 75th percentiles of the absolute error distribution and the middle line in each box represents the median. Whiskers represent the 95th percentile of the absolute error distribution at each level. The BMI is reliable (95th percentile of the absolute error <0.15) at all 20 levels. Additionally, the BMI is highly reliable (95th percentile of the absolute error <0.1) at 17 of the 20 levels.

Maryam M. Shanechi, et al. PLoS Comput Biol. 2013 October;9(10):e1003284.
6.
Figure 5

Figure 5. Comparison of the bounded LQR and MPC strategies with upper-bound constraints on the drug infusion rates.. From: A Brain-Machine Interface for Control of Medically-Induced Coma.

In each subfigure, the top panel shows the closed-loop controlled BSP traces using the bounded LQR control strategy and using the MPC strategy with various time horizons, time samples (seconds). The bottom panel shows the corresponding drug infusion rates. In addition to being non-negative, here the drug infusion rate is required to be less than 2.4 mg/min. Here we have shown two example permutations of the target levels but the bounded LQR and the MPC drug infusion rates converge with increasing in all cases.

Maryam M. Shanechi, et al. PLoS Comput Biol. 2013 October;9(10):e1003284.
7.
Figure 1

Figure 1. The BMI system.. From: A Brain-Machine Interface for Control of Medically-Induced Coma.

(a) The BMI records the EEG, segments the EEG into a binary time-series by filtering and thresholding, estimates the BSP or equivalently the effect-site concentration level based on the binary-time series, and then uses this estimate as feedback to control the drug infusion rate. (b) A sample burst suppression EEG trace. Top panel shows the EEG signal, middle panel shows the corresponding filtered EEG magnitude signal (orange) and the threshold (blue) used to detect the burst suppression events, and bottom panel shows the corresponding binary time-series with black indicating the suppression and white indicating the burst events. (c) The two-compartmental model used by the BMI to characterize the effect of propofol on the EEG.

Maryam M. Shanechi, et al. PLoS Comput Biol. 2013 October;9(10):e1003284.

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