#brain-machine interfaces

[sustainableyadayadayada]

September 2025 in Review

Most frightening and/or depressing story: We are most likely on a path to the AMOC tipping point. I distinguished between the tipping point, which is when collapse becomes inevitable, and the actual collapse itself. These are separated in time, which means the tipping point may only be called in retrospect when it is too late to prevent the collapse. This why being “on the path to the tipping…

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[scripta-elegans]

[techcocktail]

Why Brain-Machine Interfaces Are the Future of Tech Gadgets

[uncomfortablescience]

While you sleep, your brain might be helping you practice new skills

HEY SO after a totally planned and foreseen "summer hiatus," I am back to existing in the blog-world. I can hear your cries of resounding joy from here.

More reasons to get a good night's sleep after your Late Beginner Adult Ballet 1 class—it's not just a rest for your muscles, it's a chance for your brain to keep practicing.

Slow-wave sleep (SWS; the last stage of the sleep cycle before REM) is already known to be important for the consolidation of memories, including motor memories. One mechanism for this might be enhanced activity of motor control areas in the brain during SWS, which has been shown to correlate with better learning of motor skills. A new Nature Neuroscience paper suggests that this is not just a diffuse increase in motor cortex activity, but offline reactivation of the specific activity patterns involved in performing a newly learned task. The study is also notable for being one of the first (at least, the first I've seen...) to use a brain-machine interface (BMI) as a means to a scientific end other than just the development of the devices themselves.

A BMI is any of a wide variety of devices that can be stuck in the brain to read out neural activity and use it to control things like computer cursors or artificial limbs. In this study, an array of tiny electrodes recorded spikes—little electrical pulses that are the basic units of neural computation—produced by a small population of neurons in the rat primary motor cortex (M1). A computer then changed the position of a small feeding tube based on the activity of a preselected set of neurons. With some practice, the rats learned to increase the firing rate of the task-related (TR) neurons, moving the tube into the correct position and triggering the delivery of a small reward.

(I'll just pause and let you absorb this, in case you're new to the idea of BMIs. This is a study involving rats controlling machines with their brains. And that's not even the main finding. The future is now, you guys.)

There's a huge benefit to using a BMI to study motor learning—not only can you measure the activity at a very fine spatial scale, you can also define a task with respect to a particular, known, set of neurons. That makes it much easier to determine whether subsequent changes in neural activity are reflecting a general boost to the motor area, or are specific to the processing of the newly learned task (i.e., the TR neurons).

The interesting findings in this study deal with TR neuron activity during a period of SWS following the learning session, as compared to their SWS activity before. What matters is not so much the number of times neurons fire but when they fire, with respect to the local field potential (LFP). The LFP is a measure of the average electrical activity in the neuron's cellular neighborhood, which tends to oscillate in fairly regular cycles. (Probably important to note that it's still a bit of a mystery exactly what neural activity is reflected in the LFP—it's likely some aggregate including incoming and outgoing signals as well as local processing.) Phase locking analysis determines whether an individual neuron's spikes occur at the same phase (e.g., near the peak or the trough) of the LFP waves. You might think of the LFP as a kind of neural background music—neurons who're clapping with the beat are phase-locked. Neurons who are just randomly active (even if they have a high average firing rate), aren't.

Phase-locking is a common metric in memory studies. It's been shown that phase-locking of individual neurons increases during the learning portion of various different memory tasks, and that performance on these tasks correlates with the amount of phase-locking. So the alignment of individual neurons' spikes with the LFP might be one mechanism for encoding new memories—one hypothesis is that certain phases of the LFP facilitate the formation of new neural connections (synapses); or, the rhythmicity of the LFP might act like a time-keeper that helps coordinate the widespread brain activity necessary to retrieve a memory. Again, bit of a mystery, but the data are consistent: phase-locking is a key mechanism in memory formation.

But back to the rats: after they learned the task, TR neurons exhibited increased phase-locking during SWS, as compared to before the training. Phase-locking of task-unrelated (TU) neurons didn't change, nor did that of the TR neurons in rats who failed to actually learn the task. That's the first indication that task-related activity was being reactivated during sleep.

At the population level, correlations and interactions among the studied neurons were also different during the SWS periods after learning. First, there was an increase in spike-spike coherence between pairs of TR neurons, meaning that when one TR neuron fired, others tended to fire as well. They also used principle component analysis (PCA) to probe more complex interactions (“components”) within the population activity. PCA involves a lot of fancy math (read: REALLY FUN MATH but too tangential for this post), but as a quick example, imagine that neuron A is a TR neuron that fires a bunch of spikes whenever the animal performs the task. Imagine neuron A is linked to neuron B (as many neurons within a small cortical region might be) such that A can only fire more if B also calms down and fires less than its average baseline rate. PCA would find this interaction—the combined decrease in B's firing rate with the increase in A's—and other possibly more complicated “components,” and highlight them as important aspects of task-related activity at the population level. You might have already guessed, but these components, too, were both reactivated during SWS and phase-locked to the LFP, just like the individual TR neurons' spikes.

Those results are really cool—they show that in the period of SWS after rats learn a motor task, the increased activity in motor cortex is not simply a diffuse boost in the spiking rates throughout M1, but the specific rehearsal of (approximately) the actual population activity required to do the task.

But to show that these neural changes are actually the mechanism behind sleep-related performance improvements, you also have to show that performance actually improves after sleep, and that this improvement depends on the neural changes. The researchers find exactly that—they tested the animals in two sessions, before and after a period of sleep, and found that rats whose TR neurons became more phase-locked during SWS improved more between the end of session 1 and beginning of session 2. And, the more time a rat spent in SWS during his nap, the more he improved in session 2.

All together, this evidence suggests the reactivation of task-related neural activity during SWS as an important mechanism for sleep-dependent improvements in motor learning. It also helps define the roles of sleep and plasticity with respect to brain-machine interfaces, which can be used to replace lost sensation (for example, cochlear implants) or even (someday) amputated limbs.

(The full text of the paper, by Gulati, Ramanathan, Wong, and Ganguly, is behind a paywall, but if you have access through a university or library subscription, it's here: http://www.nature.com/neuro/journal/v17/n8/full/nn.3759.html)

[txchnologist]

Prosthetic Hand Lets User Feel Again

by Marsha Lewis, Inside Science TV

Three years ago Igor Spetic was working at his manufacturing job when his life changed forever.

"When I looked down and saw red everywhere, I knew something had happened," said Spetic.

A huge metal hammer had crushed Igor's right hand.

"Thirty minutes later, I was in surgery, and two hours after that, I was without a hand," said Spetic.

Since his accident, Spetic has had to learn how to navigate the world left-handed, which has not always been easy. "Learning to do stuff left-handed has been challenging," he said. 

Now, Spetic is helping biomedical engineers at Case Western University in Cleveland test a newly-developed prosthetic hand that allows its users to feel physical sensations.

"The idea with sensation is it's sort of our way of interacting with the world. It's a very personal thing," said Dustin Tyler, a biomedical engineer at Case Western.

In the new prosthetic, electrodes are placed on nerves inside the arm that used to go to the hand. When they are activated, pulses travel to the brain. "The subject can feel 20 different isolated spots," Tyler explained.

The researchers have performed tests with Spetic where he is blindfolded and is wearing earphones. In these tests, he is able to pick up blocks on a board and averages about eight in two minutes.

As the researchers place the electrodes on to Spetic's prosthetic, "It's like 'Oh, I can feel each finger. I can feel the back of my hand. I can feel my palm. I can feel the bottom of my hand,'" Spetic said.

It's a breakthrough that could change the way amputees live.

Spetic said, "It was a little overwhelming at first, emotional, because I didn't feel anything for a year and a half." 

Spetic is one of two people who have been testing the new hand for about 19 months. Tyler said that the research team wants to make the device more portable for at-home use. He envisions a prototype where the sensors are housed inside the hand and communicate wirelessly to a device in the chest.

Top Gif: Peripheral nerve interface for sensory restoration. Courtesy Dustin Tyler/Case Western Reserve University.<

Inside Science is supported by the American Institute of Physics, a nonprofit publisher of scientific journals, and a group of underwriters that include multiple science, technology, engineering, and mathematics organizations.

[mnxmnkmnd]

Getting detailed neural activity data is pretty hard. Present methods generally involve putting many electrodes into the skull to measure LFPs. These electrodes degrade, etc. This paper develops a possible system for "chronic" recordings - a system that can be inserted once and used indefinitely, or at least for long periods.

A major hurdle in brain-machine interfaces (BMI) is the lack of an implantable neural interface system that remains viable for a lifetime. This paper explores the fundamental system design trade-offs and ultimate size, power, and bandwidth scaling limits of neural recording systems built from low-power CMOS circuitry coupled with ultrasonic power delivery and backscatter communication. In particular, we propose an ultra-miniature as well as extremely compliant system that enables massive scaling in the number of neural recordings from the brain while providing a path towards truly chronic BMI. These goals are achieved via two fundamental technology innovations: 1) thousands of 10 - 100 µm scale, free-floating, independent sensor nodes, or neural dust, that detect and report local extracellular electrophysiological data, and 2) a sub-cranial interrogator that establishes power and communication links with the neural dust.

Basically it works like this: On the outside of your head (back of your neck), you have a control unit. Inside of your head, under your dura mater, you have some "interrogators", that communicate with many "dust" motes in the cortex - small mostly-cubic integrated circuits with piezoelectric equipment etc., less than 100 µm to a side - through ultrasound, which also powers the motes (through the piezo).

I'm no good at electronics and probably misunderstanding some things, but this is sci-fi and pretty cool, and developed in great detail (though nowhere near ready for real use).

[txchnologist]

Could Broadband Radio Beaming from Your Brain Control Robots Remotely?

by Charles Q. Choi

Researchers have developed a novel brain radio implant that can wirelessly broadcast signals from up to 100 brain cells for well over a year. They say the apparatus could one day help lead to improved robotic limbs and perhaps even remote control of devices.

A Brown University research team developed the rechargeable implant that can record neuron activity and wirelessly broadcast that data like a cell phone for the brain.

Herculean effort from idea to prototype

The device uses a pill-sized chip of electrodes implanted on the cortex to record signals from up to 100 neurons. These electrodes then relay this data into a laser-welded, hermetically sealed titanium can about the size of a box of Tic Tacs. This box holds the battery, infrared transmitters, a copper coil for recharging, and microchips that convert neural signals into digital data. The wireless and charging signals pass through an electromagnetically transparent sapphire window.

"In an absolutely herculean effort, we went from drawing board to device ready for in vivo testing in exactly one year," Borton says. "It integrated many individual innovations into a complete system with potential for neuroscientific gain greater than the sum of its parts."

Neurosurgeons implanted the device in three pigs and three rhesus macaque monkeys. The implants have helped scientists observe complex brain signals in real time for as long as 16 months so far, "and we fully expect that the device will last even longer without failure," Borton says.

Neuroprostheses rising

Increasingly, brain implants are enabling people to control robotics using only their minds. The hope is that soon people will be able to overcome disabilities using bionic limbs or mechanical exoskeletons.

Currently, scientists are recording what brain cells do when experimental animals move their natural limbs. They hope this data will guide strategies that will help patients with disabilities mentally control robotic limbs. However, brain implants usually tether subjects to computers via wires, significantly constraining the actions that investigators can record.

Past research has devised wireless methods of recording brain activity, but these have proven relatively limited in scope or duration. For instance, electronic tattoos that one can stick on the skin can read brain waves, but control of limbs requires far more details about brain activity that extends down to the level of neurons.

Others have invented wireless brain sensors that gather comparable amounts of data from roughly as many brain cells. However, these were only tested for a few hours, and not for more than a year in the corrosive environment of the body like the implant from Borton and his colleagues.

"Our device certainly represents the longest implanted broadband wireless neural recordings," Borton says. "Just like a sailboat needs to keep saltwater out of the cockpit, we must protect the nested active electronics inside the device from the saline environment in the body. We worked tirelessly with industry leaders in hermetic sealing technologies to design custom titanium packaging that not only allowed 100 individual wires from the neurosensor into the device, but also allowed zero saline ingress."

Neural Broadband Radio

The device records all frequencies of neural activity and broadcasts at 24 megabytes per second. After a two-hour charge, it can operate for about seven hours.

In the future, the implant could monitor the activity of even more neurons. The researchers hope to reduce the size of their device and prolong its battery life. "We have already made significant innovations on the more power-hungry components in the system," Borton says.

The device was designed to get approval for use in humans one day. It could be used to monitor the brains of mobile volunteers to help scientists better understand how brains work normally and in those with disorders. "We must still understand a great deal more about how the brain encodes and decodes information" before these signals can be used to control robotics, Borton says.

"We are becoming laser-focused on following the regulatory pathway to bring devices like the one discussed in this paper into clinical use," he says. "This is a long and extremely expensive road, but one that leads to a potential change in a patient’s life. So, obviously, worth it."

The scientists detailed their findings online Feb. 21 in the Journal of Neural Engineering.

Top Image: Engineers Arto Nurmikko and Ming Yin examine their prototype wireless, broadband neural sensing device. Courtesy Fred Field/Brown University.

Charles Q. Choi has written for Scientific American, The New York Times, Wired, Science and Nature, among others. In his spare time, he has traveled to all seven continents, including scaling the side of an iceberg in Antarctica, investigating mummies from Siberia, snorkeling in the Galapagos, climbing Mt. Kilimanjaro, camping in the Outback, avoiding thieves near Shaolin Temple and hunting for mammoth DNA in Yukon.

[txchnologist]

Neuroprosthesis First Step to Star Trek "Vision"-Enabling Visor?

by Michael Keller

Another scientific development is bringing modern life closer to the future world envisioned in Star Trek.

Researchers have taken a significant step toward making an implantable machine that could restore and augment vision by bypassing the optic system and training another of the brain's sensory processing centers to "see."

A Duke University team has successfully fitted rats with an infrared detector whose electrodes were wired into the part of their brains that sense touch. When the neuroprosthetic device was powered up, the rats felt infrared light as if they were touching it.

The scientists who performed the study say the experiment represents the first time that a brain-machine interface has augmented an adult animal’s senses.

  New possibilities

Neurobiologist Miguel Nicolelis, a visionary in the field of brain-machine interfaces who leads the group, says their success is a first step to giving animals or people the machine-enabled ability to see in any region of the electromagnetic spectrum. It could also help those whose region of the brain that processes visual information is damaged learn to “see” again through a different information-processing region.

If such technology becomes reality, the world could see devices like the VISOR worn by Star Trek character Geordi La Forge, which allowed the congenitally blind man to see elements of the electromagnetic spectrum.

"We could create devices sensitive to any physical energy," Nicolelis says in an announcement about their success. "It could be magnetic fields, radio waves, or ultrasound. We chose infrared initially because it didn't interfere with our electrophysiological recordings."

Such a brain-machine interface could also work to replace other lost senses, like rewiring the sensation of touch for those who have lost it through spinal cord injury.

Training rats to see with their whiskers

In the study, the researchers trained rats to respond to a single visible light that was turned on from a bank of three LEDs. Then microelectrodes, each about a tenth the width of a human hair, were implanted into the cortical region that interprets touch information from the rat’s whiskers. These electrodes were connected to an infrared detector and the LEDs were replaced with ones that emitted infrared light, which mammals cannot see.

The animals eventually learned how to interpret information from the detector that was placed on their heads through the electrodes that stimulated their touch-processing cortical region. They began sweeping their heads back and forth to search for an active infrared signal through the detector and were able to successfully navigate to the active infrared light to receive rewards.

Nicolelis says even though researchers co-opted the part of the rat brain that processed tactile sensations from their whiskers to “feel” infrared light, the animals were still able to sense touch through the whiskers.

"When we recorded signals from the touch cortex of these animals, we found that although the cells had begun responding to infrared light, they continued to respond to whisker touch,” he says. “It was almost like the cortex was dividing itself evenly so that the neurons could process both types of information.”

Photo Credit: Just Us 3 via Compfight cc