Sensors and decoding for intracortical brain computer interfaces.

Intracortical brain computer interfaces (iBCIs) are being developed to enable people to drive an output device, such as a computer cursor, directly from their neural activity. One goal of the technology is to help people with severe paralysis or limb loss. Key elements of an iBCI are the implanted sensor that records the neural signals and the software that decodes the user's intended movement from those signals. Here, we focus on recent advances in these two areas, placing special attention on contributions that are or may soon be adopted by the iBCI research community. We discuss how these innovations increase the technology's capability, accuracy, and longevity, all important steps that are expanding the range of possible future clinical applications.

[1]  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.

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

[3]  Teresa H. Y. Meng,et al.  HermesB: A Continuous Neural Recording System for Freely Behaving Primates , 2007, IEEE Transactions on Biomedical Engineering.

[4]  Michael D Joseph,et al.  Poly(3,4-ethylenedioxythiophene) (PEDOT) polymer coatings facilitate smaller neural recording electrodes , 2011, Journal of neural engineering.

[5]  Masatoshi Nakamura,et al.  Parametric modeling of somatosensory evoked potentials using discrete cosine transform , 2001, IEEE Transactions on Biomedical Engineering.

[6]  Arjun K. Bansal,et al.  Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices. , 2011, Journal of neurophysiology.

[7]  Justin Baker,et al.  Multi-scale recordings for neuroprosthetic control of finger movements , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[9]  R. J. Vetter,et al.  Silicon-substrate intracortical microelectrode arrays for long-term recording of neuronal spike activity in cerebral cortex , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Alan V. Oppenheim,et al.  Discrete-time Signal Processing. Vol.2 , 2001 .

[11]  Teresa H. Y. Meng,et al.  HermesD: A High-Rate Long-Range Wireless Transmission System for Simultaneous Multichannel Neural Recording Applications , 2010, IEEE Transactions on Biomedical Circuits and Systems.

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

[13]  C. Mehring,et al.  Encoding of Movement Direction in Different Frequency Ranges of Motor Cortical Local Field Potentials , 2005, The Journal of Neuroscience.

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

[15]  Daryl R. Kipke,et al.  Neural Electrodes: Interfacing Conducting Polymer Nanotubes with the Central Nervous System: Chronic Neural Recording using Poly(3,4‐ethylenedioxythiophene) Nanotubes (Adv. Mater. 37/2009) , 2009 .

[16]  K. E. Jones,et al.  A glass/silicon composite intracortical electrode array , 2006, Annals of Biomedical Engineering.

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

[18]  Arto Nurmikko,et al.  An implantable wireless neural interface for recording cortical circuit dynamics in moving primates , 2013, Journal of neural engineering.

[19]  Eran Stark,et al.  Predicting Movement from Multiunit Activity , 2007, The Journal of Neuroscience.

[20]  Juliana Dushanova,et al.  Neurons in primary motor cortex engaged during action observation , 2010, The European journal of neuroscience.

[21]  Ming Yin,et al.  A 100-channel hermetically sealed implantable device for wireless neurosensing applications , 2012, 2012 IEEE International Symposium on Circuits and Systems.

[22]  J. Wolpaw,et al.  Brain-Computer Interfaces: Principles and Practice , 2012 .

[23]  J. Wolpaw,et al.  Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects , 2009, IEEE Reviews in Biomedical Engineering.

[24]  R. Andersen,et al.  A neural representation of sequential states within an instructed task. , 2010, Journal of neurophysiology.

[25]  R E Kass,et al.  Recursive bayesian decoding of motor cortical signals by particle filtering. , 2004, Journal of neurophysiology.

[26]  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.

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

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

[29]  Gerhard Friehs,et al.  Intra-day signal instabilities affect decoding performance in an intracortical neural interface system , 2013, Journal of neural engineering.

[30]  N.V. Thakor,et al.  Decoding Individuated Finger Movements Using Volume-Constrained Neuronal Ensembles in the M1 Hand Area , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[32]  C. Mehring,et al.  Inference of hand movements from local field potentials in monkey motor cortex , 2003, Nature Neuroscience.

[33]  C. Koch,et al.  The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes , 2012, Nature Reviews Neuroscience.

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

[35]  Eun Jung Hwang,et al.  Brain Control of Movement Execution Onset Using Local Field Potentials in Posterior Parietal Cortex , 2009, The Journal of Neuroscience.

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

[37]  Prabhat,et al.  Inferring Attentional State and Kinematics from Motor Cortical Firing Rates , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

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

[39]  Eric Leuthardt,et al.  Decoding covert spatial attention using electrocorticographic (ECoG) signals in humans , 2012, NeuroImage.

[40]  L. Miller,et al.  Accurate decoding of reaching movements from field potentials in the absence of spikes , 2012, Journal of neural engineering.

[41]  S. Baker.  Oscillatory interactions between sensorimotor cortex and the periphery , 2007, Current Opinion in Neurobiology.

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

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

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

[45]  Teresa H. Y. Meng,et al.  A high-rate long-range wireless transmission system for multichannel neural recording applications , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[46]  Uri T Eden,et al.  A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. , 2005, Journal of neurophysiology.

[47]  G. Schalk,et al.  Brain-Computer Interfaces Using Electrocorticographic Signals , 2011, IEEE Reviews in Biomedical Engineering.

[48]  H. Alkadhi,et al.  Localization of the motor hand area to a knob on the precentral gyrus. A new landmark. , 1997, Brain : a journal of neurology.

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

[50]  Kathryn Ziegler-Graham,et al.  Estimating the prevalence of limb loss in the United States: 2005 to 2050. , 2008, Archives of physical medicine and rehabilitation.

[51]  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.

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

[53]  Hamed Rahmani.  Factor-Analysis Methods for Higher-Performance Neural Prostheses , 2015 .

[54]  L R Hochberg,et al.  Efficient Decoding With Steady-State Kalman Filter in Neural Interface Systems , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

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

[57]  A. P. Georgopoulos,et al.  Neuronal population coding of movement direction. , 1986, Science.

[58]  D. Hoffman,et al.  Sensorimotor transformations in cortical motor areas , 2003, Neuroscience Research.

[59]  David J. Anderson,et al.  Solid-State Electrodes for Multichannel Multiplexed Intracortical Neuronal Recording , 1986, IEEE Transactions on Biomedical Engineering.

[60]  Naotaka Fujii,et al.  Long-Term Asynchronous Decoding of Arm Motion Using Electrocorticographic Signals in Monkeys , 2009, Front. Neuroeng..

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

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

[63]  E. Evarts,et al.  Relation of pyramidal tract activity to force exerted during voluntary movement. , 1968, Journal of neurophysiology.

[64]  David J. Anderson,et al.  Novel multi-sided, microelectrode arrays for implantable neural applications , 2011, Biomedical microdevices.

[65]  Ahmed H. Tewfik,et al.  High Accuracy Decoding of Movement Target Direction in Non-Human Primates Based on Common Spatial Patterns of Local Field Potentials , 2010, PloS one.

[66]  Uri T Eden,et al.  General-purpose filter design for neural prosthetic devices. , 2007, Journal of neurophysiology.

[67]  E N Brown,et al.  A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells , 1998, The Journal of Neuroscience.

[68]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[69]  John P. Cunningham,et al.  Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity , 2008, NIPS.

[70]  D R Humphrey,et al.  Predicting Measures of Motor Performance from Multiple Cortical Spike Trains , 1970, Science.

[71]  J. Ashe,et al.  Offline Decoding of End-Point Forces Using Neural Ensembles: Application to a Brain–Machine Interface , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[72]  J. Bouyer,et al.  Fast fronto-parietal rhythms during combined focused attentive behaviour and immobility in cat: cortical and thalamic localizations. , 1981, Electroencephalography and clinical neurophysiology.

[73]  Ming Yin,et al.  A 100-Channel Hermetically Sealed Implantable Device for Chronic Wireless Neurosensing Applications , 2013, IEEE Transactions on Biomedical Circuits and Systems.

[74]  Michael J. Black,et al.  Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex , 2001, NIPS.

[75]  J R Rüttner,et al.  Electron microscopic investigations on aging and osteoarthrotic human cartilage. Preliminary report. , 1968, Pathologia et microbiologia.

[76]  V. Gilja,et al.  Signal Processing Challenges for Neural Prostheses , 2008, IEEE Signal Processing Magazine.

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

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

[79]  Kianoush Nazarpour,et al.  EMG Prediction From Motor Cortical Recordings via a Nonnegative Point-Process Filter , 2012, IEEE Transactions on Biomedical Engineering.

[80]  N. Hatsopoulos,et al.  Periodicity and Evoked Responses in Motor Cortex , 2010, The Journal of Neuroscience.

[81]  J. Donoghue,et al.  Oscillations in local field potentials of the primate motor cortex during voluntary movement. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[82]  A. Jackson,et al.  A demographic profile of new traumatic spinal cord injuries: change and stability over 30 years. , 2004, Archives of physical medicine and rehabilitation.

[83]  Michael J. Black,et al.  A switching Kalman filter model for the motor cortical coding of hand motion , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[84]  Rajmohan Bhandari,et al.  A novel masking method for high aspect ratio penetrating microelectrode arrays , 2009 .

[85]  Liam Paninski,et al.  Population decoding of motor cortical activity using a generalized linear model with hidden states , 2010, Journal of Neuroscience Methods.

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

[87]  N. Hatsopoulos,et al.  Propagating waves mediate information transfer in the motor cortex , 2006, Nature Neuroscience.

[88]  L. Paninski,et al.  Superlinear Population Encoding of Dynamic Hand Trajectory in Primary Motor Cortex , 2004, The Journal of Neuroscience.

[89]  R. Andersen,et al.  Cortical Local Field Potential Encodes Movement Intentions in the Posterior Parietal Cortex , 2005, Neuron.

[90]  Jonathan R. Wolpaw,et al.  Brain–Computer InterfacesPrinciples and Practice , 2012 .

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

[92]  S. Giszter,et al.  Adaptation to a Cortex-Controlled Robot Attached at the Pelvis and Engaged during Locomotion in Rats , 2011, The Journal of Neuroscience.

[93]  Joseph E. O’Doherty,et al.  Unscented Kalman Filter for Brain-Machine Interfaces , 2009, PloS one.

[94]  N.G. Hatsopoulos,et al.  Kinetic Trajectory Decoding Using Motor Cortical Ensembles , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[95]  Wei Wu,et al.  Real-Time Decoding of Nonstationary Neural Activity in Motor Cortex , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[96]  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 .

[97]  D. Robinson,et al.  The electrical properties of metal microelectrodes , 1968 .

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

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

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

[101]  Michael J. Black,et al.  Decoding Complete Reach and Grasp Actions from Local Primary Motor Cortex Populations , 2010, The Journal of Neuroscience.

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

[103]  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 .

[104]  R A Normann,et al.  The Utah intracortical Electrode Array: a recording structure for potential brain-computer interfaces. , 1997, Electroencephalography and clinical neurophysiology.

[105]  Arjun K. Bansal,et al.  Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials. , 2012, Journal of neurophysiology.

[106]  Byron M. Yu,et al.  Detecting neural-state transitions using hidden Markov models for motor cortical prostheses. , 2008, Journal of neurophysiology.

[107]  D. Humphrey,et al.  CORTICAL CONTROL OF A ROBOT USING A TIME-DELAY NEURAL NETWORK , 1997 .

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

[109]  M.J. Black,et al.  Motor Cortical Decoding Using an Autoregressive Moving Average Model , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[110]  J. Kalaska,et al.  Cerebral cortical mechanisms of reaching movements. , 1992, Science.

[111]  John P. Donoghue,et al.  Decoding 3-D Reach and Grasp Kinematics From High-Frequency Local Field Potentials in Primate Primary Motor Cortex , 2010, IEEE Transactions on Biomedical Engineering.

[112]  R.R. Harrison,et al.  HermesC: Low-Power Wireless Neural Recording System for Freely Moving Primates , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[113]  Michael J. Black,et al.  Statistical Analysis of the Non-stationarity of Neural Population Codes , 2006, The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006..

[114]  Qing Bai,et al.  Single-unit neural recording with active microelectrode arrays , 2001, IEEE Transactions on Biomedical Engineering.

[115]  R.R. Harrison,et al.  Wireless Neural Recording With Single Low-Power Integrated Circuit , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[116]  Miguel A. L. Nicolelis,et al.  Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex , 1999, Nature Neuroscience.

[117]  S.I. Ryu,et al.  Cortical Neural Prosthesis Performance Improves When Eye Position Is Monitored , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.