AAWSAP DIRD, Technological Approaches to Controlling External Devices, March 2010

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on the cortex to implant cortical arrays is determined from previous experiments in
fMRI (References 39, 40). In this way, a map is constructed correlating electrical neural
activity with muscle movement. The goal of this exercise is to develop an algorithm that
will predict which muscles move based on reading the neural activity alone. The reading
of the neural activity for eye movement can then be used to move a device such as a
camera lens.
There are two main models of fine motor control, one where the cortical motor areas
perform all of the control functions and receive all sensory feedback, and a second
where the cortical areas direct the function and receive interpreted feedback through
sub-cortical or even peripheral networks. For open-loop invasive BMI applications, this
is an academic question since peripheral interfaces would receive and send the same
signals in both models, and cortical interfaces would blindly adapt external decoding
algorithms based on the signals present regardless of model.
There have been numerous demonstrations of nonhuman primates controlling robots or
graphical cursors in real-time through signals collected from cortical areas that employ
open loop experimentation (References 41, 42). Kim, et al. conducted experiments
where monkeys are trained on tasks prior to implantation, and then the tasks are
repeated multiple times while muscle action and cortical activity are monitored
(Reference 42). In these trials, shoulder and elbow torque were measured while the
arm itself was constrained in an exoskeleton such that the hand would only move in a
plane axial to the monkey's torso. A visual cursor was introduced and projected on a
screen above the monkeys hand to follow the 2-D motion from the center starting point
to the various task targets, which are also projected on the screen. The shoulder and
elbow position recorded the state of flexation of 6 sets of muscle groups, collectively
called the musculoskeletal arm model (MAM). Relating the spiking activity from
implanted arrays to even this simplified 2-D MAM motion proved quite complex, and no
fit correlating the observed movements and neural activity could be obtained with a
linear model when kinematic impedance was considered. 15
Even considering the six inputs, the 2-D problem is essentially a computer cursor
control and therefore a relatively simple device, fully specified by a Cartesian coordinate
system. The ultimate goal of these control systems is to manipulate something much
more complex, like an arm, which may have many more degrees of freedom organized
in a completely different coordinate system. For these tests a more elaborate "tracking
system" may be utilized in teaching a primate to feed itself16 using a directly observed
cortically controlled robotic arm.
Open-loop control systems have an inherent drawback in cases where cortical activity
controls movement directly via an adaptive algorithm. Training the algorithm is the
critical part of interface development. The adaptive algorithms use an iterative process
to create a brain-to-cursor motion decoding scheme based on how the neurons fire
when different targets are presented and therefore rely on previous normal feedback
training - the brain knows how to move an arm because it has been moving an arm for
most of its life.
15 Impedance to motion is essential for realistic operation of artificial limbs.
16 Food in this experiment is used as a reward.
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