Clinical translation of a high-performance neural prosthesis

Neural prostheses have the potential to improve the quality of life of individuals with paralysis by directly mapping neural activity to limb- and computer-control signals. We translated a neural prosthetic system previously developed in animal model studies for use by two individuals with amyotrophic lateral sclerosis who had intracortical microelectrode arrays placed in motor cortex. Measured more than 1 year after implant, the neural cursor-control system showed the highest published performance achieved by a person to date, more than double that of previous pilot clinical trial participants.

[1]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[2]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[3]  Nicholas G. Hatsopoulos,et al.  Brain-machine interface: Instant neural control of a movement signal , 2002, Nature.

[4]  David J. Ward,et al.  Artificial intelligence: Fast hands-free writing by gaze direction , 2002, Nature.

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

[6]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

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

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

[9]  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.

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

[11]  Andrea Gaggioli,et al.  Neurorehabil Neural Repair , 2008 .

[12]  Steven M Chase,et al.  Control of a brain–computer interface without spike sorting , 2009, Journal of neural engineering.

[13]  R. Quian Quiroga What is the real shape of extracellular spikes? , 2009, Journal of Neuroscience Methods.

[14]  R. Rosenfeld Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

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

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

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

[18]  Stefan Mihalas,et al.  Does Afferent Heterogeneity Matter in Conveying Tactile Feedback Through Peripheral Nerve Stimulation? , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  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.

[20]  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 .

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

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

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

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

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

[26]  Josef Parvizi,et al.  Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas , 2013, Journal of neural engineering.

[27]  Robin C. Ashmore,et al.  An Electrocorticographic Brain Interface in an Individual with Tetraplegia , 2013, PloS one.

[28]  Nicolas Y. Masse,et al.  Non-causal spike filtering improves decoding of movement intention for intracortical BCIs , 2014, Journal of Neuroscience Methods.

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

[30]  Shaomin Zhang,et al.  Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex , 2014, Journal of neural engineering.

[31]  Bill Bynum,et al.  Lancet , 2015, The Lancet.

[32]  R. Andersen,et al.  Decoding motor imagery from the posterior parietal cortex of a tetraplegic human , 2015, Science.

[33]  Vikash Gilja,et al.  Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain–machine interface performance , 2015, Journal of neural engineering.

[34]  Nicolas Y. Masse,et al.  Neural Point-and-Click Communication by a Person With Incomplete Locked-In Syndrome , 2015, Neurorehabilitation and neural repair.