Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates

Several groups have developed brain-machine-interfaces (BMIs) that allow primates to use cortical activity to control artificial limbs. Yet, it remains unknown whether cortical ensembles could represent the kinematics of whole-body navigation and be used to operate a BMI that moves a wheelchair continuously in space. Here we show that rhesus monkeys can learn to navigate a robotic wheelchair, using their cortical activity as the main control signal. Two monkeys were chronically implanted with multichannel microelectrode arrays that allowed wireless recordings from ensembles of premotor and sensorimotor cortical neurons. Initially, while monkeys remained seated in the robotic wheelchair, passive navigation was employed to train a linear decoder to extract 2D wheelchair kinematics from cortical activity. Next, monkeys employed the wireless BMI to translate their cortical activity into the robotic wheelchair’s translational and rotational velocities. Over time, monkeys improved their ability to navigate the wheelchair toward the location of a grape reward. The navigation was enacted by populations of cortical neurons tuned to whole-body displacement. During practice with the apparatus, we also noticed the presence of a cortical representation of the distance to reward location. These results demonstrate that intracranial BMIs could restore whole-body mobility to severely paralyzed patients in the future.

[1]  José del R. Millán,et al.  Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving , 2007, Comput. Intell. Neurosci..

[2]  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.

[3]  Miguel A. L. Nicolelis,et al.  Expanding the primate body schema in sensorimotor cortex by virtual touches of an avatar , 2013, Proceedings of the National Academy of Sciences.

[4]  J. Kalaska,et al.  Modulation of preparatory neuronal activity in dorsal premotor cortex due to stimulus-response compatibility. , 1994, Journal of neurophysiology.

[5]  Peter R. Giacobbi,et al.  Methodological challenges confronting researchers of wheeled mobility aids and other assistive technologies , 2007, Disability and rehabilitation. Assistive technology.

[6]  Emilio Kropff,et al.  Place cells, grid cells, and the brain's spatial representation system. , 2008, Annual review of neuroscience.

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

[8]  Huosheng Hu,et al.  A Self-Paced Motor Imagery Based Brain-Computer Interface for Robotic Wheelchair Control , 2011, Clinical EEG and neuroscience.

[9]  Miguel A. L. Nicolelis,et al.  Extracting Kinematic Parameters for Monkey Bipedal Walking from Cortical Neuronal Ensemble Activity , 2009, Front. Integr. Neurosci..

[10]  R. Nelson,et al.  Interactions between motor commands and somatic perception in sensorimotor cortex , 1996, Current Opinion in Neurobiology.

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

[12]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[13]  E T Rolls,et al.  Information about spatial view in an ensemble of primate hippocampal cells. , 1998, Journal of neurophysiology.

[14]  José del R. Millán,et al.  Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.

[15]  Jerald D. Kralik,et al.  Representation of Attended Versus Remembered Locations in Prefrontal Cortex , 2004, PLoS biology.

[16]  Cuntai Guan,et al.  On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

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

[19]  A. Kübler,et al.  Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials , 2014, Journal of NeuroEngineering and Rehabilitation.

[20]  C. Duffy,et al.  Cortical neuronal responses to optic flow are shaped by visual strategies for steering. , 2008, Cerebral cortex.

[21]  S. Wise,et al.  Tuning for the orientation of spatial attention in dorsal premotor cortex , 2001, The European journal of neuroscience.

[22]  M. Nicolelis,et al.  Optimizing a Linear Algorithm for Real-Time Robotic Control using Chronic Cortical Ensemble Recordings in Monkeys , 2004, Journal of Cognitive Neuroscience.

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

[24]  S. Wise,et al.  Oscillations in the premotor cortex: single-unit activity from awake, behaving monkeys , 2000, Experimental Brain Research.

[25]  Miguel A. L. Nicolelis,et al.  Reprogramming movements: extraction of motor intentions from cortical ensemble activity when movement goals change , 2012, Front. Neuroeng..

[26]  J. Huggins,et al.  What would brain-computer interface users want? Opinions and priorities of potential users with amyotrophic lateral sclerosis , 2011, Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases.

[27]  A. Berthoz,et al.  Neurons responding to whole-body motion in the primate hippocampus , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[28]  Michael L Boninger,et al.  Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury. , 2013, Journal of rehabilitation research and development.

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

[30]  G. Rizzolatti,et al.  Mirror neurons and mirror systems in monkeys and humans. , 2008, Physiology.

[31]  Mikhail A. Lebedev,et al.  Chronic, Wireless Recordings of Large Scale Brain Activity in Freely Moving Rhesus Monkeys , 2014, Nature Methods.

[32]  Olaf Blanke,et al.  The wheelchair as a full-body tool extending the peripersonal space , 2015, Front. Psychol..

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

[34]  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.

[35]  M. Nuttin,et al.  A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots , 2008, Clinical Neurophysiology.

[36]  Miguel A. L. Nicolelis,et al.  Principles of neural ensemble physiology underlying the operation of brain–machine interfaces , 2009, Nature Reviews Neuroscience.

[37]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .

[38]  Matthew A. Wilson,et al.  Neural Representation of Spatial Topology in the Rodent Hippocampus , 2013, Neural Computation.

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

[40]  Marco Molinari,et al.  A Functionally Relevant Tool for the Body following Spinal Cord Injury , 2013, PloS one.

[41]  Christina Papadimitriou,et al.  Becoming en‐wheeled: the situated accomplishment of re‐embodiment as a wheelchair user after spinal cord injury , 2008 .

[42]  Brice Rebsamen,et al.  A brain controlled wheelchair to navigate in familiar environments. , 2010, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[43]  Joseph T. Francis,et al.  Toward an Autonomous Brain Machine Interface: Integrating Sensorimotor Reward Modulation and Reinforcement Learning , 2015, The Journal of Neuroscience.

[44]  J. Kalaska.  From intention to action: motor cortex and the control of reaching movements. , 2009, Advances in experimental medicine and biology.

[45]  Kazuo Tanaka,et al.  Electroencephalogram-based control of an electric wheelchair , 2005, IEEE Transactions on Robotics.

[46]  Øyvind F Standal,et al.  Re-embodiment: incorporation through embodied learning of wheelchair skills , 2011, Medicine, health care, and philosophy.

[47]  J. Donoghue,et al.  Primary Motor Cortex Tuning to Intended Movement Kinematics in Humans with Tetraplegia , 2008, The Journal of Neuroscience.

[48]  Hisao Nishijo,et al.  The relationship between monkey hippocampus place-related neural activity and action in space , 1997, Neuroscience Letters.

[49]  Hamzah Arof,et al.  HMM based automated wheelchair navigation using EOG traces in EEG , 2014, Journal of neural engineering.

[50]  Javier Moya,et al.  Widespread Vestibular Activation of the Rodent Cortex , 2015, The Journal of Neuroscience.