Neural control of finger movement via intracortical brain–machine interface

Abstract

Objective Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main Results Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ = 0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits/s throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe that these results represent an important step towards full and dexterous control of neural prosthetic devices.

Cortical Decoding of Individual Finger Group Motions Using ReFIT Kalman Filter

“…All experimental tasks were performed in compliance with NIH guidelines as well as the University of Michigan's Institutional Animal Care & Use Committee and Unit for Laboratory Animal Medicine. We trained two male rhesus macaques, Monkey W and Monkey N, to use a novel manipulandum, designed to isolate finger movements (Figure 1B ), in order to match fingertip position targets in the same virtual environment described in previous work (Irwin et al, 2017 ). The manipulandum consists of two "doors" with dividers to isolate index finger movements from MRP movements.…”

“…Earlier NHP studies have established that M1 may contain enough information to distinguish between individual finger movements (Hamed et al, 2007 ; Aggarwal et al, 2008 ). In our previous NHP work, we characterized the flex-extend motion of all four fingers together as a single DOF, and used signals from M1 to provide subjects with online continuous control of a virtual hand (Irwin et al, 2017 ). Here we have developed a novel manipulandum to track and control movement of two separate finger groups.…”

Intuitive neuromyoelectric control of a dexterous bionic arm using a modified Kalman filter

“…There is a strong mathematical and theoretical justification for using a KF to estimate motor intent [8]. KFs have been used for decoding motor intent previously [9], [10]. Our group has also previously reported on the use of a KF for estimating motor intent from neural recordings offline [11], [12].…”

A bioelectric neural interface towards intuitive prosthetic control for amputees

“…In the literature, there are three locations where motor control signals can be intercepted: the brain [2,22,26,70], muscles [10,11,18,28,33], and peripheral nerves [1,8,27,46,53]. While cortical decoding techniques with implanted microelectrode arrays in the brain have pioneered the research field for many years, it remains unclear if there could be sufficient neural information harvested to meaningfully restore the lost motor function.…”