Defense Intelligence Reference Document Technological Approaches To Controlling

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EMG interfaces for limb amputees have advanced dramatically in the last several years,
with several devices controlled by remaining muscles that have been implanted with
otherwise unused nerves in an invasive process called reinnervation (References 56,
57). Twelve electrode pairs were utilized in the recording of intended arm motion. The
classifier algorithm used to determine hand control was able to provide a new prediction
10 times a second, and for controls, a motion selection from 1 of 10 possibilities could
be achieved in less than 170 milliseconds. Interesting with this procedure is that a
direct biological amplification of the neuronal signal is achieved by connecting it to a
muscle. The amplified signal is then read by a noninvasive EMG probe. Although not yet
approaching normal, the speed and dexterity of these artificial limbs is impressive.
Finally, one trial utilizing a normal, healthy human self-experimenter in 2002 showed
that sensation and control is possible through a peripheral array implant in the median
nerve of the left arm. This trial lasted 3 months before the physical connection between
the nerve and the microarray deteriorated beyond use. Subsequent examination and
testing has revealed no long-term damage at the implant site (Reference 58).
OPTICAL STIMULATION OF ACTION POTENTIALS
A final invasive technology that utilizes light to gate action potentials shows promise
(Reference 59). In this technology, the biological switch is controlled by a locally
inserted LED and possibly guided using optical fibers, but there is no contact between
the physical circuits and neurons, thus avoiding the issue of scar tissue caused by
operation of invasive probes. Application to artificial vision have already interesting pre-
clinical results in rat brain tissue (Reference 60).
Discussion
Any successful BMI hardware technology relies on at least one, and in most cases all, of
the following three essential elements: brain plasticity through user training, neural
decoding by a machine learning algorithm, and neuroscience knowledge. Advances in
the latter two will help all BMI research paths, but the former element is the most
research path dependent since different technologies have different training limitations
such as the inherent visual feedback delay of a noninvasive BOLD monitor.
One approach to the training issue is to greatly simplify the control information
bandwidth required. Research here has placed a focus on scenario-based controlling
that is not from millisecond-to-millisecond updating of some 3-D trajectory like a full-
duplex BMI specified in the introduction would perform, but rather is controlled by high
level commands from the user in a low-bandwidth supervisory mode. It is conceivably
useful to tell a wheelchair to "Go Left," "Stop," or "Veer Right," and let sensors and
actuators on the wheelchair work out the trajectory details, as opposed to constantly
setting and resetting the trajectory of the wheelchair. In this manner, much of the
feedback error-correction is done in the physical circuit. This approach can be viewed as
a series of simple asynchronous decisions, and relies on the artificial intelligence of the
execution algorithms rather than tapping the general adaptability of mammalian brain
architecture. Mention is made of this research path because it is important to the
advancement of the field as a whole, though the concentration in this treatise is on
tapping the plasticity of neural systems rather than creating a program to imitate them.
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