Caltech researchers are working on developing a MEMS-based (Micro-Electro-Mechanical System) brain-computer interface, with initial designs proving promising, and they already claim that the software side is essentially complete.
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.
For all this to work, the program must be able not only to correctly discriminate between spikes from different neurons in the same recording but also to retain this information and track a neuron’s spiking patterns over time. The neuron-tracking algorithm was inspired by software the U.S. military uses to track planes, and Wolf [Michael Wolf, engineer at Caltech] expects that his formulas may be useful to other applications in robot and computer vision.
Yu-Chong Tai, a MEMS researcher at Caltech, is designing the hardware that would move the electrodes on a scale of microns. Each electrode in an array would have to connect to a tiny motor on the surface of the brain that would control all its movements. And there’s quite a bit of work left to be done on that. “The idea of actually putting this in the [human] brain is far off,” says Wolf.