Millions of people worldwide suffer from neurological injury and disease resulting in profound movement impairment. Often the disability is so severe that it is not possible to feed oneself or readily communicate. A new class of medical system termed brain-machine interfaces (BMIs) has emerged from research labs in the past decade and is now poised to dramatically improve these patients'quality of life. BMIs """"""""read out"""""""" neural electrical activity directly from motor structures in the brain and decode these electrical impulses in order to determine the intended movement. Initial versions of BMI systems that control a computer cursor are now in FDA Phase-I clinical trials, and numerous agencies are actively engaged in clinical translation (e.g., NIH, DARPA, VA). Creating control signals to enable an amputee to feed himself with a prosthetic (robotic) arm and hand will require decoding signals from thousands of electrodes, rather than the hundred or so signals current systems read, as well as encoding thousands of sensor signals from the arm and hand into thousands of artificial neural signals to be """"""""written into"""""""" the brain, which has not yet been attempted. The lack of low-power (so that it can be implanted) electronic circuitry needed to run BMIs'encoding and decoding algorithms (termed codecs) is a fundamental barrier to successful clinical translation. The technologies available until now are too power-hungry (digital) or too algorithmically inflexible (analog) to meet the challenge. Recent advances in neuromorphic engineering make it now possible to build a fully implantable and programmable codec chip. This innovative approach combines digital's and analog's best features-programmability and efficiency-while offering far greater robustness than either. Meanwhile recent advances in neuroscience techniques make it now possible to obtain the knowledge needed to design the right algorithms to run on our codec chip. Optogenetic stimulaton can now be used to drive neurons in macaque cortex and computer vision can now be used to track freely moving monkeys while recording wirelessly. We propose to leverage these recent advances to dramatically increase prosthetic performance through the principled design of: (1) An entirely new class of encoders that can spatio-temporally pattern neural activity via optogenetic techniques. (2) An entirely new class of decoders that can operate in the real world with animals moving freely around in far less constrained settings. (3) An entirely new class of implantable programmable electronics that achieves the level of energy- efficiency required to run these complex algorithms. We will demonstrate our success by having a freely moving primate, with a 96-microelectrode recording array and a 9-channel optogenetic stimulator implanted in its premotor and somatosensory cortex, respectively, control a human-like robotic arm. Our ultimate goal is to realize the neuromorphic engineer's dream: Helping untold millions with neurological injury by replacing damaged neural tissue with chips that work like the brain.

Public Health Relevance

Millions of people worldwide suffer from neurological injury and disease resulting in profound movement impairment. Brain-machine interfaces (BMIs) read out neural electrical activity directly from motor structures in the brain and decodes these signals in order to execute the intended movement with a robotic arm. This project seeks to increase BMI performance dramatically by leveraging recent advances in systems neuroscience and neuromorphic engineering.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS076460-03
Application #
8470734
Study Section
Special Emphasis Panel (ZRG1-BCMB-A (51))
Program Officer
Ludwig, Kip A
Project Start
2011-09-01
Project End
2016-05-31
Budget Start
2013-06-01
Budget End
2014-05-31
Support Year
3
Fiscal Year
2013
Total Cost
$878,017
Indirect Cost
$304,088
Name
Stanford University
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Stavisky, Sergey D; Kao, Jonathan C; Nuyujukian, Paul et al. (2018) Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements. Sci Rep 8:16357
O'Shea, Daniel J; Shenoy, Krishna V (2018) ERAASR: an algorithm for removing electrical stimulation artifacts from multielectrode array recordings. J Neural Eng 15:026020
Pandarinath, Chethan; O'Shea, Daniel J; Collins, Jasmine et al. (2018) Inferring single-trial neural population dynamics using sequential auto-encoders. Nat Methods 15:805-815
Kao, Jonathan C; Nuyujukian, Paul; Ryu, Stephen I et al. (2017) A High-Performance Neural Prosthesis Incorporating Discrete State Selection With Hidden Markov Models. IEEE Trans Biomed Eng 64:935-945
Kao, Jonathan C; Ryu, Stephen I; Shenoy, Krishna V (2017) Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces. Sci Rep 7:7395
Stavisky, Sergey D; Kao, Jonathan C; Ryu, Stephen I et al. (2017) Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model. J Neurosci 37:1721-1732
Stavisky, Sergey D; Kao, Jonathan C; Ryu, Stephen I et al. (2017) Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions. Neuron 95:195-208.e9
Engel, Tatiana A; Steinmetz, Nicholas A; Gieselmann, Marc A et al. (2016) Selective modulation of cortical state during spatial attention. Science 354:1140-1144
Kao, Jonathan C; Ryu, Stephen I; Shenoy, Krishna V (2015) Leveraging historical knowledge of neural dynamics to rescue decoder performance as neural channels are lost: ""Decoder hysteresis"". Conf Proc IEEE Eng Med Biol Soc 2015:1061-6
Even-Chen, Nir; Stavisky, Sergey D; Kao, Jonathan C et al. (2015) Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex. Conf Proc IEEE Eng Med Biol Soc 2015:71-5

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