# Development of an EMG-controlled BCI for biomimetic control of hand movement in humans

> **NIH NIH R01** · NORTHWESTERN UNIVERSITY · 2023 · $677,982

## Abstract

Abstract
When asked, most persons with high-level spinal cord injury (SCI) would elect brain surgery to improve their
hand control, yet even the state-of-the-art intracortical brain computer interfaces (iBCI) have only limited control
of finger motion and no direct control of applied forces whatsoever. The exclusive use of kinematics in iBCI
control is despite the rich representation of kinetic information in primary motor cortex (M1). We propose to
address these fundamental limitations by mimicking the mammalian neuromuscular system, which controls both
digit motion and the forces they exert through the modulation of muscle activity. We will develop an iBCI that
predicts intended muscle activity (EMG) from M1 recordings in humans, then use these EMG signals to control
joint kinematics, their stiffness, and grasp forces, through a forward musculoskeletal model of the hand. We
hypothesize that this EMG-based iBCI will be an intuitive means for humans with high-level SCI to achieve more
generalizable control of their hand movements than with existing kinematic iBCIs.
Decoders for use by paralyzed humans are typically built by recording spiking activity from M1 as the user
attempts to imitate the observed motion of a cursor or a robotic arm. The decoder is computed by correlating
measured M1 activity with the observed kinematics. Though similar in concept, our approach to decoder
development is more challenging, as the high-dimensional motor output signals it requires – the EMGs – cannot
be directly visualized or imitated. To circumvent this problem, we will record EMGs (as well as hand posture and
contact forces) as able-bodied people perform a broad range of motor actions. We will also record M1 spiking
activity as paralyzed individuals observe and attempt to imitate the same actions. The able-bodied EMG data
will provide the output signals for decoder calculation, analogous to the use of observed the trajectory in
kinematic decoders. This real-time, EMG-based iBCI will allow participants to control a hand, using it to apply
forces to grasped objects in a way that mimics natural motor control. Initial development will be done in virtual
reality (VR). Subsequently, participants will use the same biomimetic iBCI to control a robotic hand in tasks
designed to replicate activities of daily life. We will compare the users' performance with this biomimetic iBCI to
that of a state-of-the-art kinematic iBCI. When successful, these methods will have application to the control of
robotic limbs for patients with limb loss, and as a means to restore movement of the user's own limbs through
Functional Electrical Stimulation. They could also be applied to the legs, where control of interaction forces and
limb impedance through muscle cocontraction is also critical.

## Key facts

- **NIH application ID:** 10651404
- **Project number:** 1R01NS131953-01
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Jennifer L. Collinger
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $677,982
- **Award type:** 1
- **Project period:** 2023-09-18 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10651404

## Citation

> US National Institutes of Health, RePORTER application 10651404, Development of an EMG-controlled BCI for biomimetic control of hand movement in humans (1R01NS131953-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10651404. Licensed CC0.

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