Standardizing the Brain-Machine Interface

Earlier this year in a lab at Duke University, in Durham, N.C., a clever, raisin-gobbling ­monkey named Idoya made a robot move in Japan—just by thinking. And she wasn’t alone. She joined ranks with, among others, a ­paraplegic man who recently used his brain to move a cursor around a computer screen.

Researchers have endowed subjects with seemingly telekinetic powers by extracting the patterns of brain ­activity that occur when we move parts of our bodies. However those patterns are tapped electronically, algorithms are needed to interpret them and discern their salient ­features so that the appropriate ­signals can be sent to external devices. Groups working on brain-machine interfaces have designed brain decoders differently, depending on the type of neural data they collect and the purposes of their research. As a result, most algorithms have to be written from the ground up. But some in the field say it’s time to develop a generic algorithm that will incorporate the best work of the last decade and serve as a foundation for all labs ­working on neural prosthetics.

That’s just what Lakshmin­arayan Srini­vasan, a ­computer scientist at MIT, has in mind. Srinivasan—together with colleagues at MIT, Harvard, Boston University, and Massachusetts General Hospital—has pulled together elements of algorithms from all the major labs that design brain-machine interfaces and proposed a new approach that theoretically would ­support and enhance each design.

From the outset, ­researchers attacking the mind-over-matter problem of developing brain-­activated prosthetics adopted widely varying approaches. Some pasted electrodes onto the scalp; others placed them just inside the skull or directly into the brain. They eavesdropped on ­different parts of the brain and, ­having obtained ­signal ­patterns, processed them ­differently, says Srinivasan.

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