Abstract

Recent technological advances have made it possible to move a robotic device in real time, using signals obtained directly from the brain. This field of Brain Machine Interface (BMI) has the means to provide movement for paralyzed patients, communication for locked-in patients, and a better understanding of the brain for all of society. In order to control movement effectively, the brain must be able to activate muscles appropriately, and monitor the evolving movement quickly and precisely. Existing BMIs for, while remarkable, do each of these tasks in poor imitation of the intact nervous system. Our proposed work addresses these limitations by developing a bidirectional interface that produces movement in a more natural way, and provides feedback about the movement by direct, electrical stimulation of the brain. Our partnership includes members at Northwestern Univ (NU), Univ of Chicago (UC), Univ of Mass, Amherst (UMass), and the autonomous Univ of Mexico (UNAM). Partners have advanced degrees in a range of biological science, computer science, physics, mathematics, and engineering disciplines. Miller (NU) will coordinate the partnership. He has extensive experience with a wide range of recording, stimulation and behavioral protocols in behaving monkeys. Hatsopoulos (UC) is at the forefront of the field of multi-electrode recordings. He was a leading member of the first group to demonstrate visually guided BMI control by a primate. Barto (U Mass) has done pioneering research in neural networks, machine learning and stochastic optimization. Fagg (UMass) is an authority in the control of reaching and grasping robots that learn to interact with the environment. Together they will develop the decoders of activity from the brain used to cause movement. Romo (UNAM), is a world leader in studies of the perceptual and decision making processes induced by electrical stimulation of the brain. Solla (NU) is an expert in neural networks and information theory. With Romo, she will develop optimal routines to encode information in stimulus trains to provide feedback to the brain. Mussa-Ivaldi (NU) will focus on the overall design and evaluation of the interfaces. He created the first ever bidirectional interface between neural tissue and a robotic device.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
1R01NS048845-01A1
Application #
6943266
Study Section
Special Emphasis Panel (ZRG1-IFCN-K (50))
Program Officer
Chen, Daofen
Project Start
2005-05-01
Project End
2009-04-30
Budget Start
2005-05-01
Budget End
2006-04-30
Support Year
1
Fiscal Year
2005
Total Cost
$768,711
Indirect Cost

  Institution

Name
Northwestern University at Chicago
Department
Physiology
Type
Schools of Medicine
DUNS #
005436803
City
Chicago
State
IL
Country
United States
Zip Code
60611

  Publications

Benjamin, Ari S; Fernandes, Hugo L; Tomlinson, Tucker et al. (2018) Modern Machine Learning as a Benchmark for Fitting Neural Responses. Front Comput Neurosci 12:56
Perich, Matthew G; Miller, Lee E (2017) Altered tuning in primary motor cortex does not account for behavioral adaptation during force field learning. Exp Brain Res 235:2689-2704
Sachs, Nicholas A; Ruiz-Torres, Ricardo; Perreault, Eric J et al. (2016) Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface. J Neural Eng 13:016009
Tomlinson, Tucker; Miller, Lee E (2016) Toward a Proprioceptive Neural Interface that Mimics Natural Cortical Activity. Adv Exp Med Biol 957:367-388
Suminski, Aaron J; Mardoum, Philip; Lillicrap, Timothy P et al. (2015) Temporal evolution of both premotor and motor cortical tuning properties reflect changes in limb biomechanics. J Neurophysiol 113:2812-23
Takahashi, Kazutaka; Kim, Sanggyun; Coleman, Todd P et al. (2015) Large-scale spatiotemporal spike patterning consistent with wave propagation in motor cortex. Nat Commun 6:7169
Best, Matthew D; Suminski, Aaron J; Takahashi, Kazutaka et al. (2014) Consideration of the functional relationship between cortex and motor periphery improves offline decoding performance. Conf Proc IEEE Eng Med Biol Soc 2014:4868-71
Willett, Francis R; Suminski, Aaron J; Fagg, Andrew H et al. (2014) Differences in motor cortical representations of kinematic variables between action observation and action execution and implications for brain-machine interfaces. Conf Proc IEEE Eng Med Biol Soc 2014:1334-7
Bensmaia, Sliman J; Miller, Lee E (2014) Restoring sensorimotor function through intracortical interfaces: progress and looming challenges. Nat Rev Neurosci 15:313-25
Zaaimi, Boubker; Ruiz-Torres, Ricardo; Solla, Sara A et al. (2013) Multi-electrode stimulation in somatosensory cortex increases probability of detection. J Neural Eng 10:056013

Showing the most recent 10 out of 39 publications