20 May 2008—A group of mechanical engineers at Caltech have come up with a way to guide miniature robots in the task of inserting and positioning electrode arrays in brain tissue. What they propose would be the first robotic approach to establishing an interface between computers and the brain by positioning electrodes in neural tissue. Researchers say that this could enhance the performance and longevity of emerging neural prosthetics, which allow paralyzed people to operate computers and robots with their minds.
In the brain-machine interface, the brain is undeniably the more chaotic and unpredictable of the pair. Electrical impulses flow between supple bodies in an aqueous pulp whose architecture is constantly changing. Neurons grow, shrink, and die, as do the synapses that link them. In the best marriage, a neural prosthetic would create a permanent one-to-one connection between electrode and neuron, ensuring that recordings of electrical spike activity—the language of information transfer in the brain—represents the chatter of a single cell per electrode. But this doesn’t always happen.
With today’s experimental neuroprostheses, ”whether the tip of the electrode ends up next to a neuron that’s helping you is still dependent on luck,” says Michael Wolf, an engineer at Caltech who, with his colleagues, will present his robotic approach to the procedure today at the IEEE International Conference on Robotics and Automation, in Pasadena, Calif.
As it is now, in single-cell recording, researchers determine where in the brain the electrodes should go by analyzing the signals they get as they bore deeper into the brain tissue. The technique is a way to figure out how many neurons you’re listening to at a given time. But since all neurons relay information in the same way, with an all-or-none electrical event called an action potential, it can be very difficult to tell what signals are coming from where.
”The uncertainty is the key issue,” says Wolf. The Caltech team has designed a system that would make the procedure more predictable by attaching a tiny MEMS-based motor to each electrode on a multichannel electrode array and using an algorithm to direct the electrodes to individual neurons. The MEMS part is still a work in progress, but the software algorithm has been worked out and tested in Caltech neuroscience labs.
Here’s how the algorithm makes a neural connection: As the electrodes are driven into the tissue, the software starts taking sample recordings to detect spikes of electrical activity at the electrode tip. When the software detects spikes, it moves forward in small increments and tracks how the signals change. After determining whether the signal has improved or gotten worse, it the algorithm moves the electrode to a new position and does more recording and comparing, driving the electrode in further if necessary until it finds the best signal. If the signal wanes, the algorithm will automatically adjust the electrode position to improve the signal.