Combining Decoder Design and Neural Adaptation in Brain-Machine Interfaces

[1]  Richard A Andersen,et al.  Decoding Trajectories from Posterior Parietal Cortex Ensembles , 2008, The Journal of Neuroscience.

[2]  John P. Cunningham,et al.  A High-Performance Neural Prosthesis Enabled by Control Algorithm Design , 2012, Nature Neuroscience.

[3]  Gytis Baranauskas,et al.  What limits the performance of current invasive brain machine interfaces? , 2014, Front. Syst. Neurosci..

[4]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[5]  Michael Gastpar,et al.  Redundant information encoding in primary motor cortex during natural and prosthetic motor control , 2011, Journal of Computational Neuroscience.

[6]  Afsheen Afshar,et al.  Free-paced high-performance brain-computer interfaces. , 2007, Journal of neural engineering.

[7]  Miguel A. L. Nicolelis,et al.  Adaptive Decoding for Brain-Machine Interfaces Through Bayesian Parameter Updates , 2011, Neural Computation.

[8]  Robert E. Kass,et al.  2009 Special Issue: Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain-computer interface algorithms , 2009 .

[9]  K. Ganguly,et al.  Reactivation of emergent task-related ensembles during slow-wave sleep after neuroprosthetic learning , 2014, Nature Neuroscience.

[10]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[11]  Krishna V. Shenoy,et al.  Challenges and Opportunities for Next-Generation Intracortically Based Neural Prostheses , 2011, IEEE Transactions on Biomedical Engineering.

[12]  Karunesh Ganguly,et al.  Neural Correlates of Skill Acquisition with a Cortical Brain–Machine Interface , 2010, Journal of motor behavior.

[13]  E. Fetz.  Volitional control of neural activity: implications for brain–computer interfaces , 2007, The Journal of physiology.

[14]  Dawn M. Taylor,et al.  Direct Cortical Control of 3D Neuroprosthetic Devices , 2002, Science.

[15]  Jose M. Carmena,et al.  Continuous Closed-Loop Decoder Adaptation with a Recursive Maximum Likelihood Algorithm Allows for Rapid Performance Acquisition in Brain-Machine Interfaces , 2014, Neural Computation.

[16]  Jerald D. Kralik,et al.  Real-time prediction of hand trajectory by ensembles of cortical neurons in primates , 2000, Nature.

[17]  P. Kennedy,et al.  Restoration of neural output from a paralyzed patient by a direct brain connection , 1998, Neuroreport.

[18]  A. Schwartz,et al.  Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex. , 2012, Journal of neurophysiology.

[19]  Jiping He,et al.  Selection and parameterization of cortical neurons for neuroprosthetic control , 2006, Journal of neural engineering.

[20]  Jose M. Carmena,et al.  Designing Dynamical Properties of Brain–Machine Interfaces to Optimize Task-Specific Performance , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[21]  Jose M Carmena,et al.  Invasive or noninvasive: understanding brain-machine interface technology. , 2010, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[22]  Krishna V Shenoy,et al.  Human cortical prostheses: lost in translation? , 2009, Neurosurgical focus.

[23]  Vikash Gilja,et al.  A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces. , 2011, Journal of neurophysiology.

[24]  Rajesh P. N. Rao,et al.  Distributed cortical adaptation during learning of a brain–computer interface task , 2013, Proceedings of the National Academy of Sciences.

[25]  S. Solla,et al.  Toward the Restoration of Hand Use to a Paralyzed Monkey: Brain-Controlled Functional Electrical Stimulation of Forearm Muscles , 2009, PloS one.

[26]  J D Simeral,et al.  Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia , 2011, Journal of neural engineering.

[27]  J. M. Carmena,et al.  Closed-Loop Decoder Adaptation on Intermediate Time-Scales Facilitates Rapid BMI Performance Improvements Independent of Decoder Initialization Conditions , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  A. Schwartz,et al.  High-performance neuroprosthetic control by an individual with tetraplegia , 2013, The Lancet.

[29]  Jose M. Carmena,et al.  Learning in Closed-Loop Brain–Machine Interfaces: Modeling and Experimental Validation , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  M. Sahani,et al.  Cortical control of arm movements: a dynamical systems perspective. , 2013, Annual review of neuroscience.

[31]  Krishna V. Shenoy,et al.  Monkey models for brain-machine interfaces: The need for maintaining diversity , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Paul Nuyujukian,et al.  Intention estimation in brain–machine interfaces , 2014, Journal of neural engineering.

[33]  Jose M. Carmena,et al.  Creating new functional circuits for action via brain-machine interfaces , 2013, Front. Comput. Neurosci..

[34]  Kip A Ludwig,et al.  Naïve coadaptive cortical control , 2005, Journal of neural engineering.

[35]  R. Andersen,et al.  Cognitive Control Signals for Neural Prosthetics , 2004, Science.

[36]  Jose M. Carmena,et al.  Closed-Loop Decoder Adaptation Shapes Neural Plasticity for Skillful Neuroprosthetic Control , 2014, Neuron.

[37]  Krishna V. Shenoy,et al.  Information Systems Opportunities in Brain–Machine Interface Decoders , 2014, Proceedings of the IEEE.

[38]  Wolfgang Maass,et al.  A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task , 2010, The Journal of Neuroscience.

[39]  Aaron C. Koralek,et al.  Volitional modulation of optically recorded calcium signals during neuroprosthetic learning , 2014, Nature Neuroscience.

[40]  Michael J. Black,et al.  Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[41]  Nicolas Y. Masse,et al.  Advantages of closed-loop calibration in intracortical brain–computer interfaces for people with tetraplegia , 2013, Journal of neural engineering.

[42]  R. Andersen,et al.  Neural prosthetic control signals from plan activity , 2003, Neuroreport.

[43]  Dragan F. Dimitrov,et al.  Cortical Representation of Ipsilateral Arm Movements in Monkey and Man , 2009, The Journal of Neuroscience.

[44]  D. Wilkin,et al.  Neuron , 2001, Brain Research.

[45]  Michael J. Black,et al.  Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array , 2011 .

[46]  D. Lovinger,et al.  Dynamic reorganization of striatal circuits during the acquisition and consolidation of a skill , 2009, Nature Neuroscience.

[47]  P.R. Kennedy,et al.  Computer control using human intracortical local field potentials , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[48]  Stephen I. Ryu,et al.  A High-Performance Keyboard Neural Prosthesis Enabled by Task Optimization , 2015, IEEE Transactions on Biomedical Engineering.

[49]  E. E. Fetz,et al.  Interfacing With the Computational Brain , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[50]  Shamim Nemati,et al.  Biomimetic Brain Machine Interfaces for the Control of Movement , 2007, The Journal of Neuroscience.

[51]  Monica A. Perez,et al.  Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity. , 2010, Physical medicine and rehabilitation clinics of North America.

[52]  E. Fetz,et al.  Direct control of paralyzed muscles by cortical neurons , 2008, Nature.

[53]  J. Carmena,et al.  Proficient BMI Control Enabled by Closed-Loop Adaptation of an Optimal Feedback-Controlled Point Process Decoder , 2013 .

[54]  Michael J. Black,et al.  Closed-loop neural control of cursor motion using a Kalman filter , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[55]  Solaiman Shokur,et al.  A Brain-Machine Interface Enables Bimanual Arm Movements in Monkeys , 2013, Science Translational Medicine.

[56]  Roy Fox,et al.  A multi-agent control framework for co-adaptation in brain-computer interfaces , 2013, NIPS.

[57]  R. Andersen,et al.  Volitional Control of Neural Activity Relies on the Natural Motor Repertoire , 2013, Current Biology.

[58]  Jose M. Carmena,et al.  Decoder remapping to counteract neuron loss in brain-machine interfaces , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[59]  L. Miller,et al.  Restoring sensorimotor function through intracortical interfaces: progress and looming challenges , 2014, Nature Reviews Neuroscience.

[60]  J. Kalaska,et al.  Learning to Move Machines with the Mind , 2022 .

[61]  J. W. Judy.  Neural Interfaces for Upper-Limb Prosthesis Control: Opportunities to Improve Long-Term Reliability , 2012, IEEE Pulse.

[62]  Robert E. Kass,et al.  Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control , 2010, Journal of Computational Neuroscience.

[63]  David Sussillo,et al.  A recurrent neural network for closed-loop intracortical brain–machine interface decoders , 2012, Journal of neural engineering.

[64]  Eilon Vaadia,et al.  Kernel-ARMA for Hand Tracking and Brain-Machine interfacing During 3D Motor Control , 2008, NIPS.

[65]  Mark L. Homer,et al.  Sensors and decoding for intracortical brain computer interfaces. , 2013, Annual review of biomedical engineering.

[66]  Paul Nuyujukian,et al.  Performance sustaining intracortical neural prostheses , 2014, Journal of neural engineering.

[67]  Byron M. Yu,et al.  Factor-analysis methods for higher-performance neural prostheses. , 2009, Journal of neurophysiology.

[68]  Dragan F. Dimitrov,et al.  Reversible large-scale modification of cortical networks during neuroprosthetic control , 2011, Nature Neuroscience.

[69]  Dennis C. Tkach,et al.  Observation-based learning for brain–machine interfaces , 2008, Current Opinion in Neurobiology.

[70]  Andrew Jackson,et al.  Learning a Novel Myoelectric-Controlled Interface Task , 2008, Journal of neurophysiology.

[71]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[72]  Peter J. Ifft,et al.  Active tactile exploration enabled by a brain-machine-brain interface , 2011, Nature.

[73]  Maysam Ghovanloo,et al.  Time to address the problems at the neural interface , 2014, Journal of neural engineering.

[74]  Stuart K. Card,et al.  Evaluation of mouse, rate-controlled isometric joystick, step keys, and text keys, for text selection on a CRT , 1987 .

[75]  P R Kennedy,et al.  Direct control of a computer from the human central nervous system. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[76]  Robert E Kass,et al.  Functional network reorganization during learning in a brain-computer interface paradigm , 2008, Proceedings of the National Academy of Sciences.

[77]  Byron M. Yu,et al.  Self-recalibrating classifiers for intracortical brain–computer interfaces , 2014, Journal of neural engineering.

[78]  E. Fetz,et al.  Long-term motor cortex plasticity induced by an electronic neural implant , 2006, Nature.

[79]  Maneesh Sahani,et al.  A dynamical systems view of motor preparation: implications for neural prosthetic system design. , 2011, Progress in brain research.

[80]  J. Krakauer,et al.  How is a motor skill learned? Change and invariance at the levels of task success and trajectory control. , 2012, Journal of neurophysiology.

[81]  Hannes Bleuler,et al.  Active tactile exploration enabled by a brain-machine-brain interface , 2011, Nature.

[82]  L. Miller,et al.  Restoration of grasp following paralysis through brain-controlled stimulation of muscles , 2012, Nature.

[83]  Michael J. Black,et al.  Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia , 2008, Journal of neural engineering.

[84]  J. Carmena,et al.  Active Sensing of Target Location Encoded by Cortical Microstimulation , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[85]  Miriam Zacksenhouse,et al.  Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface , 2005, The Journal of Neuroscience.

[86]  Justin C. Sanchez,et al.  A Symbiotic Brain-Machine Interface through Value-Based Decision Making , 2011, PloS one.

[87]  Gerald E Loeb,et al.  Cognitive signals for brain–machine interfaces in posterior parietal cortex include continuous 3D trajectory commands , 2012, Proceedings of the National Academy of Sciences.

[88]  Aaron C. Koralek,et al.  Temporally Precise Cell-Specific Coherence Develops in Corticostriatal Networks during Learning , 2013, Neuron.

[89]  J. Millán,et al.  Personalized Neuroprosthetics , 2013, Science Translational Medicine.

[90]  José Carlos Príncipe,et al.  Coadaptive Brain–Machine Interface via Reinforcement Learning , 2009, IEEE Transactions on Biomedical Engineering.

[91]  J. Carmena,et al.  Emergence of a Stable Cortical Map for Neuroprosthetic Control , 2009, PLoS biology.

[92]  Matthew T. Kaufman,et al.  Supplementary materials for : Cortical activity in the null space : permitting preparation without movement , 2014 .

[93]  K. Anderson.  Targeting recovery: priorities of the spinal cord-injured population. , 2004, Journal of neurotrauma.

[94]  Byron M. Yu,et al.  A high-performance brain–computer interface , 2006, Nature.

[95]  J. Carmena.  Advances in Neuroprosthetic Learning and Control , 2013, PLoS biology.

[96]  S. Meagher.  Instant neural control of a movement signal , 2002 .

[97]  Andrew S. Whitford,et al.  Cortical control of a prosthetic arm for self-feeding , 2008, Nature.

[98]  Aaron C. Koralek,et al.  Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills , 2012, Nature.

[99]  Jose M. Carmena,et al.  Design and Analysis of Closed-Loop Decoder Adaptation Algorithms for Brain-Machine Interfaces , 2013, Neural Computation.

[100]  Vikash Gilja,et al.  Long-term Stability of Neural Prosthetic Control Signals from Silicon Cortical Arrays in Rhesus Macaque Motor Cortex , 2010 .

[101]  Daniel Moran,et al.  Evolution of brain–computer interface: action potentials, local field potentials and electrocorticograms , 2010, Current Opinion in Neurobiology.

[102]  R. Ivry,et al.  Annals of the New York Academy of Sciences the Role of Strategies in Motor Learning , 2022 .

[103]  E. Fetz.  Operant Conditioning of Cortical Unit Activity , 1969, Science.

[104]  J. Krakauer,et al.  Motor skill depends on knowledge of facts , 2013, Front. Hum. Neurosci..

[105]  Nicholas G Hatsopoulos,et al.  Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control , 2010, The Journal of Neuroscience.

[106]  Byron M. Yu,et al.  Neural constraints on learning , 2014, Nature.

[107]  Matthew T. Kaufman,et al.  Neural population dynamics during reaching , 2012, Nature.