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Current Developments in a Cortically Controlled Brain-Machine Interface

Nicho Hatsopoulos, University of Chicago


DR. HATSOPOULOS: My background is in neuroscience. I started out in physics, went to psychology, and now I’m in neuroscience. Today I am going to talk about some work we have been doing starting 10 years ago with my collaborators at Brown University, which is trying to make sense out of large data sets collected from the brain, particularly in the cortex of behaving monkeys, and then where we are taking that to the next level.

It is going to sound a little bit engineering in flavor, but I hope to convince you that it has not just applied applications but also has scientific interest. Let me start off by telling you what people in my line of business do and have done historically. For about 40 years, my field might properly be called behavioral electrophysiology. What we are trying to understand is electrical and physiological signals in the brain and how they correlate with behavior, whether it is sensory signals coming in, visual or auditory signals, cognitive process, or motor outputs. Since the late 1960s people have been able to record these electrical signals in behaving animals and, for the most part, what people have done is address this encoding problem. In our case, we have worked with a monkey that we trained for several months to play a video game by using a joy stick to move a curser to particular targets. Historically, what people have done is insert an electrode into the an animal’s brain to record the extracellular action potentials from individual neurons while the animal performs a task. Particularly in cortex, signals are believed to be highly noisy so you have to do some sort of multi-trial averaging, requiring the animal to perform the task over and over again.

Figure 1 shows an example of five trials. These are raster plots showing you the time occurrence of the spikes, which are action potentials. These are extracellular recordings. By averaging many of these rasters you can get an average response which is shown in the yellow graph, and this is a typical average response for a neuron in the motor cortex. What we are plotting on the y axis is the firing rate of the neurons versus time. This vertical bar is the onset of movement when the monkey first begins to move. Typical of the motor cortex, it starts firing maybe 200 or 300 milliseconds before the moving begins, and it is believed to be intimately involved in driving the arm, driving the motor neurons in the spinal cord that ultimately activate the muscles and move the arm. This same approach has been used in the sensory domain as well. You present a sensory stimulus multiple times, so as to wash out noise, and you get this sort of response.



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