A Brain-Machine Interface for Control of Medically-Induced Coma
Publication Date
October 31, 2013
Journal
PLOS Computational Biology
Authors
Maryam M. Shanechi, Jessica J. Chemali, Max Liberman, Ken Solt, et al
Volume
9
Issue
10
Pages
e1003284
DOI
https://dx.plos.org/10.1371/journal.pcbi.1003284
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1003284
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/24204231
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3814408
Europe PMC
http://europepmc.org/abstract/MED/24204231
Web of Science
000330355300041
Scopus
84885457392
Mendeley
http://www.mendeley.com/research/brainmachine-interface-control-medicallyinduced-coma
Events
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1263415"], "description"=>"<p>(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.</p>", "links"=>[], "tags"=>["bmi"], "article_id"=>838753, "categories"=>["Biological Sciences"], "users"=>["Maryam M. Shanechi", "Jessica J. Chemali", "Max Liberman", "Ken Solt", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003284.g001", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_BMI_system_/838753", "title"=>"The BMI system.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-31 03:27:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/1263416"], "description"=>"<p>(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.</p>", "links"=>[], "tags"=>[], "article_id"=>838754, "categories"=>["Biological Sciences"], "users"=>["Maryam M. Shanechi", "Jessica J. Chemali", "Max Liberman", "Ken Solt", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003284.g002", "stats"=>{"downloads"=>3, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_System_identification_/838754", "title"=>"System identification.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-31 03:27:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/1263417"], "description"=>"<p>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.</p>", "links"=>[], "tags"=>["closed-loop", "controlled", "bsp"], "article_id"=>838755, "categories"=>["Biological Sciences"], "users"=>["Maryam M. Shanechi", "Jessica J. Chemali", "Max Liberman", "Ken Solt", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003284.g003", "stats"=>{"downloads"=>1, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulated_closed_loop_controlled_BSP_traces_/838755", "title"=>"Simulated closed-loop controlled BSP traces.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-31 03:27:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/1263418"], "description"=>"<p>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.</p>", "links"=>[], "tags"=>["bounded", "lqr", "mpc"], "article_id"=>838756, "categories"=>["Biological Sciences"], "users"=>["Maryam M. Shanechi", "Jessica J. Chemali", "Max Liberman", "Ken Solt", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003284.g004", "stats"=>{"downloads"=>5, "page_views"=>37, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_bounded_LQR_and_MPC_strategies_/838756", "title"=>"Comparison of the bounded LQR and MPC strategies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-31 03:27:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/1263419"], "description"=>"<p>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.</p>", "links"=>[], "tags"=>["bounded", "lqr", "mpc", "strategies", "upper-bound", "constraints", "infusion"], "article_id"=>838758, "categories"=>["Biological Sciences"], "users"=>["Maryam M. Shanechi", "Jessica J. Chemali", "Max Liberman", "Ken Solt", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003284.g005", "stats"=>{"downloads"=>0, "page_views"=>28, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_bounded_LQR_and_MPC_strategies_with_upper_bound_constraints_on_the_drug_infusion_rates_/838758", "title"=>"Comparison of the bounded LQR and MPC strategies with upper-bound constraints on the drug infusion rates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-31 03:27:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/1263420"], "description"=>"<p>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).</p>", "links"=>[], "tags"=>["Real-time", "bmi", "suppression"], "article_id"=>838759, "categories"=>["Biological Sciences"], "users"=>["Maryam M. Shanechi", "Jessica J. Chemali", "Max Liberman", "Ken Solt", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003284.g006", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_In_vivo_real_time_BMI_control_of_burst_suppression_in_individual_rodents_/838759", "title"=>"<i>In vivo</i> real-time BMI control of burst suppression in individual rodents.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-31 03:27:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/1263421"], "description"=>"<p>Each subfigure (a–f) corresponds to one of the six real-time BMI experiments (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003284#pcbi-1003284-g006\" target=\"_blank\">Figure 6</a>) 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 <i>highly</i> reliable (95th percentile of the absolute error <0.1) at 17 of the 20 levels.</p>", "links"=>[], "tags"=>["Real-time"], "article_id"=>838760, "categories"=>["Biological Sciences"], "users"=>["Maryam M. Shanechi", "Jessica J. Chemali", "Max Liberman", "Ken Solt", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003284.g007", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reliability_of_the_real_time_BMI_/838760", "title"=>"Reliability of the real-time BMI.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-31 03:27:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/1263422"], "description"=>"<p>Performance metrics across experiments.</p>", "links"=>[], "tags"=>["metrics"], "article_id"=>838761, "categories"=>["Biological Sciences"], "users"=>["Maryam M. Shanechi", "Jessica J. Chemali", "Max Liberman", "Ken Solt", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003284.t001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_metrics_across_experiments_/838761", "title"=>"Performance metrics across experiments.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-10-31 03:27:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/1263423", "https://ndownloader.figshare.com/files/1263424"], "description"=>"<div><p>Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care.</p></div>", "links"=>[], "tags"=>["brain-machine", "interface", "medically-induced"], "article_id"=>838762, "categories"=>["Biological Sciences"], "users"=>["Maryam M. Shanechi", "Jessica J. Chemali", "Max Liberman", "Ken Solt", "Emery N. Brown"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003284.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003284.s002"], "stats"=>{"downloads"=>0, "page_views"=>27, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Brain_Machine_Interface_for_Control_of_Medically_Induced_Coma_/838762", "title"=>"A Brain-Machine Interface for Control of Medically-Induced Coma", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-10-31 03:27:36"}

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Relative Metric

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