Bin He: Brain Computer Interface: Sending Neurological Signals to an External Device

I’m developing a technology called a brain-computer
interface. We have an array of sensors put on our hat,
and then we pick up an electric signal that’s generated by the firing of our neurons, which
is our brain tissue. And we use machine learning technology to
clean up the signals and pick up extremely weak signals. That reflects what we intend to do. And then we send that signal to an external
device—such as a computer, a robotic device—to control a computer or robotics or even consumer
electronics. So, the impact of this work essentially is
two-fold: the first is direct application to patients who are paralyzed or have any
kind of motor function disorder. Or another important application, in my opinion,
is it could help the general population: in the sense that our hands are always tied up with
something. If we could have extra capability… You think about something, you control your
environment. So that would one day really make science
fiction into reality. Brain-computer interfaces integrate engineering,
machine learning, and neural science. The Department of Biomedical Engineering at
Carnegie Mellon University is the ideal place to conduct such research and make innovation
to change the future of medicine and healthcare through engineering innovation. We have world-class expertise in both non-invasive
and invasive brain-computer interfaces. We have the expertise, not only to read out
and control a device, but also we have the expertise to write-in, to modulate a neural
system, so we can treat lots of neurological and mental diseases. One of the unique features of our work is
really to push the limit on non-invasive brain computer interface. That is an exact example about the many innovations
that Carnegie Mellon University faculty and students are doing every day.

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