Brain machine interfaces (BMIs) is a field that holds out the hope of allowing severely paralyzed people to communicate with the world, move their limbs, and even walk.
Of course, making that happen is far from simple. Nevertheless, researchers are working to develop solutions to the many practical problems that have prevented the idea from becoming a clinical reality.

Original article published in IEEE’s “The Institute”

One problem is signal acquisition, specifically the design of the actual physical interface that taps into the brain’s neural signals. The ideal would be to sense the signals noninvasively, through electrodes placed on the scalp.
On the other hand, poking electrodes into the brain is a surgical procedure that risks infection as well as injury. As in many engineering situations, the name of the game is trade-off. Proponents of the noninvasive approach are constantly improving their signal-processing software to better extract every bit of information from the signals they collect. At the same time, those who favor microelectrodes are trying to lessen their impact by improving the electrode-tissue interface.
Another method of accessing the brain’s neural signals falls between those two. Sanchez’s group is experimenting with an electrocorticographic (ECoG) technique that places an array of small electrodes on the cortex, each of which aggregates signals from a large number of neurons—many more than a microelectrode does but significantly fewer than an external electrode.
Minimizing power consumption is another major issue with BMI. Any permanently implantable device needs amplifiers, signal-processing circuitry, and a wireless transmitter. Therefore, using as little power as possible to minimize the heating of tissue and to prolong battery life is another important goal.
Finally, a team at Stanford University came up with a scheme that combines a variable-precision analog-to-digital converter with a spike-sorting subsystem that samples the neurological signal only when a spike is present and varies its resolution from 3 to 8 bits, depending on the quality of the signal. IEEE Student Member Michael D. Linderman, who is part of the Stanford team, says the subsystem can be trained by the signal shapes to identify individual neurons whose signals are picked up by the same electrode. That information is enough for the complex decoding algorithms to analyze and determine what action the person intends to take.

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