Lee Miller
Northwestern University, IL·Biomedical Engineering
Brain-computer interfaces can read signals from the brain's movement areas and turn them into commands for a robotic or virtual hand. This offers people paralyzed by high-level spinal cord injury (SCI) a way to regain hand use.
- Fields
Biomedical Engineering
Neuroscience
Engineering
- Topics
brain-computer interface
iBCI
spinal cord injury
- Grants
- 1 known grant, through Aug 2028
What they work on
- Build decoders that predict intended muscle activity (EMG) from primary motor cortex spiking recorded in humans with high-level spinal cord injury, trained with EMG, hand posture and force data from able-bodied people doing the same actions.
- Drive a forward musculoskeletal model of the hand with the decoded EMG so users can control joint motion, stiffness and grasp forces in real time.
- Develop and test the EMG-based intracortical brain-computer interface first in virtual reality, then for controlling a robotic hand in tasks that mimic activities of daily life.
- Test whether the biomimetic EMG-based iBCI gives more intuitive, generalizable hand control than a state-of-the-art kinematic iBCI by comparing user performance on both.
More about the research
Today's implanted versions, called intracortical brain-computer interfaces (iBCIs), decode only intended motion. They give limited finger control, no control of grip force, and no control of how stiff the hand is. Yet the primary motor cortex (M1), the brain region that drives voluntary movement, also carries rich information about force. This project asks whether copying the body's own strategy can give users more natural hand control. The body moves the hand by controlling muscle activity.
Why it matters
People with high-level spinal cord injury could gain more natural and useful hand control, including the ability to set grip force and hand stiffness when holding objects. The same muscle-based approach could improve control of robotic limbs for people with limb loss. It could also restore movement of a person's own paralyzed limbs through functional electrical stimulation of muscles or nerves, and it might later be applied to leg control.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Karunesh Ganguly
University of California-San Francisco, CA·Neuroscience
Compare two neural decoding methods for their ability to give paralyzed patients stable, plug-and-play control of a robotic arm and hand with ECoG for at least 8 weeks without recalibration.
Sergey Stavisky
University of California-Davis, CA·Neuroscience·Early-career
Record hundreds of individual neurons with implanted microelectrode arrays in the ventral precentral gyrus and superior temporal gyrus to learn which features of speech these neurons encode and when during speaking.
Jonathan Kao
University of California-Los Angeles, CA·Biomedical Engineering·Early-career
Train new recurrent neural network architectures on the decision-making and movement tasks animals perform, constrained by anatomy and neural recordings, then analyze them to propose mechanisms of cortical computation.
The school
Northwestern University: 71 labs on PI Pages, $1.3B a year on research, 536 research doctorates a year.
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