But what if a more direct form of
brain-machine interface could widen the information bottleneck by
sending commands not through nerves and muscles made of meat, but wires
and semi-conductors made of metal? Well, then you’d have one big future
path for medicine — and very likely personal computing as well.
There are two basic types of interaction between the brain and a machine: info in, and info out.
Info in generally takes the form of an augmented or artificial sensory
organ sending its signals directly into the nervous system, like a
cochlear or ocular implant. Info out, for instance controlling a bionic
arm or a mouse pointer with pure thought, involves reading signals in
the nervous system and ferrying them out to a computer system. The most
advanced devices, like sensing bionic limbs, incorporate paths running
in both directions.
It’s important to draw a distinction between devices that read and/or create neural signals in the brain,
and those that create neural signals in the nervous system and then
allow the nervous system to naturally ferry those signals to the brain
on its own. There are advantages and disadvantages to both approaches.
The
understand the difference, take the issue of a mind-controlled
prosthetic arm. Early bionic control rigs almost all involved surgically
implanting electrodes on the surface of the brain, and using these
electrodes to read and record brain activity. By recording the activity
associated with all sorts of different thoughts (“Think about moving the
mouse pointer up and to the left!”), scientists can teach a computer to
recognize different wishes and execute the corresponding command. This
can be extremely challenging for neural control technology, since of
course our computer command of interest is only a tiny fraction of the
overall storm of neural activity ongoing in a whole brain at any given
instant.
This computer-identification process is also basically an
attempt at reinventing something far, far older than the wheel.
Evolution created neural structures that naturally sift through complex,
chaotic brain-born instructions and produce relatively simple commands
to be ferried on by motor neurons; conversely, we also have structures
that naturally turn the signals produced by our sensory organs into our
nuanced, subjective experience.
Asking a computer to re-learn this
brain-sifting process, it turns out, isn’t always the most efficient
way of doing things. Often, we can get the body to keep doing its most
difficult jobs for us, making real neural control both easier and more
precise.
In
neural prosthetics, there’s an idea called targeted muscle
reinnervation. This allows scientists, in some situations, to preserve a
fragment of damaged muscle near the site of amputation and to use this
muscle to keep otherwise useless nerves alive. In an amputee these
nerves are bound for nowhere, of course, but if kept healthy they will
continue to receive signals meant for the missing phantom limb. These
signals, as mentioned, have already been distilled out of the larger
storm of brain activity, and nicely separated in the motor neuron of the
arm, this signal can be read much more easily. And since the user is
sending a motor command down precisely the same neural paths as before
their amputation, the interaction can be immediately natural and without any meaningful learning curve.
Of course, the strategy of using the nervous system to
our benefit is limited by what nature has decided we ought to be able to
do. It will probably always be easier and more effective to use
pre-separated muscular signals to control muscle-replacement
prosthetics, but we have no built-in mouse pointer control nucleus in
our brain — at least, not yet. Eventually, if we want to pull from the
brain whole complex thoughts or totally novel forms of control, we’re
going to have to go to the source.
Direct brain reading and control has made incredible steps forward, from a super-advanced, injectable neuro-mesh to genetically-induced optogenic solutions
that can force neurons to fire in response to stimulation with light.
Solutions are getting both more invasive and less, diverging into one
group with super-high-fidelity by ultimately impractical designs, and
one with lower fidelity but more realistic, over-the-scalp solutions.
Skullcaps studded with electrodes might not look cool — but you might
still pull one on, not too far into the future.
Long term, there’s
almost no telling where these trends might take us. Will we end up with
enlarged new portions of the motor cortex due to constant use of new
pure-software appendages? Will we dictate to our computer in full
thoughts? If you’re in a store and spy a sweater your friend might like,
could you run it by them simply by remotely sending them the
sensory feeling you get as you run your fingers over the fabric? Would
this vicarious living be inherently any less worthwhile than having felt
the fabric yourself?
12/11/2015
What are brain-machine interfaces, and how do they work?
The simplest brain-machine
interface, or at least the one we can use the most readily, is the human
hand. We’ve structured pretty much the entirely of computing around the
input it’s possible to produce with our hands, and now to a lesser
extent with our voices. But hands and voices are limited. Words, whether
spoken or typed, are just representations of our real intentions, and
the practice of a moving the image of a mouse-pointer within a simulated
physical space creates even more abstraction between user and program.
Translating our thoughts to computer-style commands, and then physically
inputting them, is a slow process that takes time and attention away
from the task at hand.
Zac Vawter climbs some stairs for science.
This
idea, that we interact with the brain not through the brain itself but
through a contact point somewhere else in the nervous system, works just
as well for input technology. Most vision prosthetics work by sending
signals into the optic nerve, and from there the artificial
signals enter the brain just like regular ones. They avoid the
difficulty of reliably stimulating only certain neurons in the brain,
and again use the brain’s own signal-transduction processes to achieve
this aim.
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