A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design
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{"title"=>"A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design", "type"=>"journal", "authors"=>[{"first_name"=>"Maryam M.", "last_name"=>"Shanechi", "scopus_author_id"=>"16200343400"}, {"first_name"=>"Ziv M.", "last_name"=>"Williams", "scopus_author_id"=>"6701736906"}, {"first_name"=>"Gregory W.", "last_name"=>"Wornell", "scopus_author_id"=>"7003798695"}, {"first_name"=>"Rollin C.", "last_name"=>"Hu", "scopus_author_id"=>"34872178500"}, {"first_name"=>"Marissa", "last_name"=>"Powers", "scopus_author_id"=>"55450512400"}, {"first_name"=>"Emery N.", "last_name"=>"Brown", "scopus_author_id"=>"55547126290"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84876064360", "sgr"=>"84876064360", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0059049", "pmid"=>"23593130", "isbn"=>"1932-6203", "pui"=>"368705939"}, "id"=>"68b72430-39ef-3fe0-853a-e7f228de12f0", "abstract"=>"Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.", "link"=>"http://www.mendeley.com/research/realtime-brainmachine-interface-combining-motor-target-trajectory-intent-using-optimal-feedback-cont", "reader_count"=>77, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>5, "Researcher"=>12, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>32, "Student > Postgraduate"=>3, "Student > Master"=>10, "Student > Bachelor"=>7, "Professor"=>4, "Unspecified"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>5, "Researcher"=>12, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>32, "Student > Postgraduate"=>3, "Student > Master"=>10, "Student > Bachelor"=>7, "Professor"=>4, "Unspecified"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>35, "Unspecified"=>5, "Environmental Science"=>1, "Nursing and Health Professions"=>1, "Agricultural and Biological Sciences"=>19, "Medicine and Dentistry"=>7, "Neuroscience"=>4, "Arts and Humanities"=>1, "Psychology"=>2, "Computer Science"=>1, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>35}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Neuroscience"=>{"Neuroscience"=>4}, "Psychology"=>{"Psychology"=>2}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>19}, "Computer Science"=>{"Computer Science"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Belgium"=>1, "United States"=>9, "Italy"=>1}, "group_count"=>4}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1021032", "https://ndownloader.figshare.com/files/1021033", "https://ndownloader.figshare.com/files/1021034", "https://ndownloader.figshare.com/files/1021035"], "description"=>"<div><p>Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.</p></div>", "links"=>[], "tags"=>["neuroscience", "neurophysiology", "Motor systems", "computational neuroscience", "Bioengineering", "Biomedical Engineering", "signal processing", "Statistical signal processing", "Real-time", "brain-machine", "interface", "combining", "trajectory", "optimal"], "article_id"=>679814, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Maryam M. Shanechi", "Ziv M. Williams", "Gregory W. Wornell", "Rollin C. Hu", "Marissa Powers", "Emery N. Brown"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0059049.s001", "https://dx.doi.org/10.1371/journal.pone.0059049.s002", "https://dx.doi.org/10.1371/journal.pone.0059049.s003", "https://dx.doi.org/10.1371/journal.pone.0059049.s004"], "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Real_Time_Brain_Machine_Interface_Combining_Motor_Target_and_Trajectory_Intent_Using_an_Optimal_Feedback_Control_Design_/679814", "title"=>"A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-04-10 02:43:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1021029"], "description"=>"<p>The green circle shows the instructed target and the yellow line shows the monkey's trajectory. The black line shows the trajectory estimate using the two-stage decoder, the red circle shows the predicted target from the first stage, the blue line shows the trajectory estimate of RW-PPF (i.e., the second stage of the decoder without using the target prediction), and the red line shows that of the linear ridge regression decoder. (A) Sample trials in which both the two-stage decoder and RW-PPF acquire the target correctly. (B) Sample trials in which the two-stage decoder acquires the target correctly but RW-PPF does not. (C) Sample trials in which the two-stage decoder acquires the target correctly but the target is predicted incorrectly from the first stage.</p>", "links"=>[], "tags"=>["neuroscience", "neurophysiology", "Motor systems", "computational neuroscience", "Bioengineering", "Biomedical Engineering", "signal processing", "Statistical signal processing", "offline", "trajectory"], "article_id"=>679811, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Maryam M. Shanechi", "Ziv M. Williams", "Gregory W. Wornell", "Rollin C. Hu", "Marissa Powers", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059049.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_offline_trajectory_estimates_/679811", "title"=>"Comparison of the offline trajectory estimates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-10 02:43:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1021022"], "description"=>"<p>The bars show mean quantities and the error bars show the standard deviation (s.d.) around the mean across sessions. All quantities are obtained from the training sessions using leave-one-out cross-validation. (A) Accuracy of the different models. (B) RMS error of the different models. (C) Roughness coefficient of the different models. The two-stage decoder (used in the real-time BMI) outperforms all other models in terms of accuracy, RMS error, and smoothness.</p>", "links"=>[], "tags"=>["neuroscience", "neurophysiology", "Motor systems", "computational neuroscience", "Bioengineering", "Biomedical Engineering", "signal processing", "Statistical signal processing"], "article_id"=>679805, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Maryam M. Shanechi", "Ziv M. Williams", "Gregory W. Wornell", "Rollin C. Hu", "Marissa Powers", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059049.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Offline_model_comparisons_/679805", "title"=>"Offline model comparisons.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-10 02:43:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1021021"], "description"=>"<p>(A) Experimental task. The experiment consisted of an instructed-delay center-out task with four targets (<i>left</i>). To be rewarded, the monkey not only had to acquire the correct target, but also had to avoid touching any of the incorrect targets first (example successful and unsuccessful paths are shown on the <i>right</i>). After target presentation, there was 1 s of delay before the “go” cue, signaling that the monkey could begin moving the joystick. During the training sessions, the monkey controlled the position of the cursor using a joystick. During the BMI sessions, the joystick was disconnected and the real-time decoder controlled the cursor. (B) Optimal feedback control framework to model the BMI. An optimal feedback control framework is used to model the BMI motor task. In this framework, each task is performed to accomplish a goal during which there is real-time sensory feedback (e.g., visual feedback), , about the state of the system to be controlled (e.g., BMI), . Based on the intended goal, the internal forward model about the dynamics of the system, and the sensory feedback about the state of the system, the brain (controller) decides on a control command, which is reflected in its neural activity, , and controls the system (see Materials and Methods). Here we have assumed that the brain receives perfect sensory feedback of the state, i.e., </p>", "links"=>[], "tags"=>["neuroscience", "neurophysiology", "Motor systems", "computational neuroscience", "Bioengineering", "Biomedical Engineering", "signal processing", "Statistical signal processing", "optimal"], "article_id"=>679804, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Maryam M. Shanechi", "Ziv M. Williams", "Gregory W. Wornell", "Rollin C. Hu", "Marissa Powers", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059049.g001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experimental_task_and_the_optimal_feedback_control_model_/679804", "title"=>"Experimental task and the optimal feedback control model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-10 02:43:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1021031"], "description"=>"<p>(A) Comparison of the real-time BMI accuracy with the real-time target prediction accuracy from the first stage and also with the accuracy of RW-PPF (i.e., the second stage without using the target prediction) obtained offline using the same real-time data set. The bars show mean quantities and the error bars show the standard deviation (s.d.) around the mean across sessions. The two-stage BMI outperforms either stage alone. (B–C) Sample decoded trajectories. In all panels, the green circle shows the instructed target, the red circle shows the predicted target from the first stage, the black line shows the trajectory estimate of the two-stage BMI, and the blue line shows that of RW-PPF using only the peri-movement activity. Sample trials where both the two-stage BMI and RW-PPF correctly acquire the target are shown in B. The complementary property of the two-stage BMI is illustrated in C and D. C shows sample trials in which the correct target prediction of the first stage compensates for the inaccurate estimation of the kinematic decoder in the second stage. D shows sample trials in which the kinematic decoder in the second stage, using ongoing peri-movement activity, compensates for the incorrect target prediction of the first stage.</p>", "links"=>[], "tags"=>["neuroscience", "neurophysiology", "Motor systems", "computational neuroscience", "Bioengineering", "Biomedical Engineering", "signal processing", "Statistical signal processing", "trajectories", "complementary", "two-stage"], "article_id"=>679813, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Maryam M. Shanechi", "Ziv M. Williams", "Gregory W. Wornell", "Rollin C. Hu", "Marissa Powers", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059049.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Real_time_trajectories_and_the_complementary_property_of_the_two_stage_BMI_/679813", "title"=>"Real-time trajectories and the complementary property of the two-stage BMI.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-10 02:43:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/1021030"], "description"=>"<p>The green circle shows the instructed target. The black lines show the trajectories estimated using the two-stage decoder in multiple trials in which the decoder is correct. The blue lines show the trajectories estimated by RW-PPF (i.e., the second stage of the decoder without using the target prediction) in multiple trials in which RW-PPF is correct.</p>", "links"=>[], "tags"=>["neuroscience", "neurophysiology", "Motor systems", "computational neuroscience", "Bioengineering", "Biomedical Engineering", "signal processing", "Statistical signal processing", "estimates"], "article_id"=>679812, "categories"=>["Engineering", "Biological Sciences"], "users"=>["Maryam M. Shanechi", "Ziv M. Williams", "Gregory W. Wornell", "Rollin C. Hu", "Marissa Powers", "Emery N. Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059049.g004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Trajectory_estimates_and_their_variations_/679812", "title"=>"Trajectory estimates and their variations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-10 02:43:32"}

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  • {"unique-ip"=>"6", "full-text"=>"1", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"3", "full-text"=>"1", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"11", "full-text"=>"14", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"3", "full-text"=>"6", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"10", "full-text"=>"8", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Cell biology", "average_usage"=>[272, 472, 600, 713, 815, 911, 1004, 1094, 1185, 1273, 1358, 1441]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[297, 488, 616, 724, 828, 939, 1038, 1127, 1223, 1311, 1393, 1479, 1556]}, {"subject_area"=>"/Computer and information sciences/Systems science", "average_usage"=>[271, 427, 520, 640, 714, 773, 841, 899, 961, 1057, 1131, 1229, 1280, 1364]}, {"subject_area"=>"/Engineering and technology", "average_usage"=>[254, 440, 558, 675, 785, 883, 972, 1061, 1154, 1248, 1336, 1414, 1489]}, {"subject_area"=>"/Engineering and technology/Control engineering", "average_usage"=>[311, 514, 645, 772, 878, 974, 1050, 1131, 1232, 1313, 1384, 1487, 1565, 1657]}, {"subject_area"=>"/Engineering and technology/Electronics", "average_usage"=>[280, 490, 639, 786, 922, 1024, 1153, 1257, 1382, 1485, 1638, 1733, 1805, 1871]}, {"subject_area"=>"/Physical sciences", "average_usage"=>[254, 431, 547, 651, 748, 842, 932, 1017, 1098, 1178, 1259, 1336, 1404]}]}
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