Brain–Computer Interfaces - Index of

214 D.M. Taylor and M.E. Stetner
person wouldn’t be able to tell the difference. The artificial limb would respond just
as they intend their own limb to move.
It is unclear how many individual neurons are needed to accurately decode full
arm and hand movements over a continuous range of possible joint configurations
and muscle forces. Most studies so far try to predict some subset of movement
characteristics, such as predicting two or three dimensional hand position in space
and ignoring other details of the hand and arm configuration. Figure 4 compares
actual hand trajectories and brain-controlled movement trajectories from a study by
Taylor et al. where a monkey used intracortical signals to move a virtual cursor to
targets in 3D space [15]. This figure illustrates the importance of visual feedback
and the brain’s ability to adapt to the BCI. Figure 4 also illustrates the benefit of
concurrently adapting the decoding function to track and encourage learning and
beneficial changes in the brain signals.
Part(a)ofFig.4 shows the monkey’s actual hand trajectories as it made centerout
movements in 3D space (split into two 2D plots for easier viewing on the printed
page). Part (b) plots trajectories decoded from the intracortical signals recorded
while the animal made the hand movements in part (a). These trajectories were
generated after the animal completed the experiment, so the animal did not have any
real-time visual feedback of where the neural trajectories were going. Part (c) shows
what happened when the animal did have visual feedback of the brain-controlled trajectories
in real time. Here the animal used its intracortical signals to directly control
the 3D movements of the cursor to the targets. In this case, the monkey modified
its brain signals as needed to correct for errors and steer the cursor to the targets.
Part (d) shows what happened when the experimenters also adapted the decoding
function to track and make use of learning induced changes in the animal’s brain
signals. Adapting the BCI decoder to the brain as the brain adapts to the decoder
resulted in substantial improvements in movement control. Many neural signals that
did not convey much movement information during normal arm movements became
very good at conveying intended movement with practice. Regularly updating the
decoding function enabled the decoder to make use of these improvements in the
animal’s firing patterns.
Dr. Schwartz and colleagues have moved further toward generating whole arm
and hand control with intracortical signals. They trained a monkey to use a braincontrolled
robotic arm to retrieve food and bring it back to its mouth. This
self-feeding included 3D control of the robot’s hand position as well as opening
and closing of the hand itself for control of four independent dimensions or
actions [16].
The United States government’s Department of Defense is currently sponsoring
research investigating use of intracortical signals to control very sophisticated prosthetic
limbs to benefit soldiers who have lost limbs in the war efforts in the Middle
East. The goal of this funding is to develop realistic whole-arm-and-hand prosthetic
limbs with brain signals controlling 22 independent actions or dimensions including
control of individual finger movements. Researchers involved in this effort so far
have been able to decode individual finger movements from the intracortical signals
recorded in monkeys [17]. This government funding effort is intended to fast-track