Brain-Brain Communication  

Researchers have successfully replicated a direct brain-to-brain connection between pairs of people as part of a scientific study following the team's initial demonstration a year ago. The researchers were able to transmit the signals from one person's brain over the Internet and use these signals to control the hand motions of another person within a split second of sending that signal.

University of Washington researchers have successfully replicated a direct brain-to-brain connection between pairs of people as part of a scientific study following the team’s initial demonstration a year ago. In the newly published study, which involved six people, researchers were able to transmit the signals from one person’s brain over the Internet and use these signals to control the hand motions of another person within a split second of sending that signal.

At the time of the first experiment in August 2013, the UW team was the first to demonstrate two human brains communicating in this way. The researchers then tested their brain-to-brain interface in a more comprehensive study, published in the journal PLOS ONE.

In this photo above, UW students Darby Losey, left, and Jose Ceballos are positioned in two different buildings on campus as they would be during a brain-to-brain interface demonstration. The sender, left, thinks about firing a cannon at various points throughout a computer game. That signal is sent over the Web directly to the brain of the receiver, right, whose hand hits a touchpad to fire the cannon.

“The new study brings our brain-to-brain interfacing paradigm from an initial demonstration to something that is closer to a deliverable technology,” said co-author Andrea Stocco, a research assistant professor of psychology and a researcher at UW’s Institute for Learning & Brain Sciences. “Now we have replicated our methods and know that they can work reliably with walk-in participants.”

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Collaborator Rajesh Rao, a UW professor of computer science and engineering, is the lead author on this work.

The research team combined two kinds of noninvasive instruments and fine-tuned software to connect two human brains in real time. The process is fairly straightforward. One participant is hooked to an electroencephalography machine that reads brain activity and sends electrical pulses via the Web to the second participant, who is wearing a swim cap with a transcranial magnetic stimulation coil placed near the part of the brain that controls hand movements.

Using this setup, one person can send a command to move the hand of the other by simply thinking about that hand movement.

The UW study involved three pairs of participants. Each pair included a sender and a receiver with different roles and constraints. They sat in separate buildings on campus about a half mile apart and were unable to interact with each other in any way – except for the link between their brains.

"The new study brings our brain-to-brain interfacing paradigm from an initial demonstration to something that is closer to a deliverable technology."

Each sender was in front of a computer game in which he or she had to defend a city by firing a cannon and intercepting rockets launched by a pirate ship. But because the senders could not physically interact with the game, the only way they could defend the city was by thinking about moving their hand to fire the cannon.

Across campus, each receiver sat wearing headphones in a dark room – with no ability to see the computer game – with the right hand positioned over the only touchpad that could actually fire the cannon. If the brain-to-brain interface was successful, the receiver’s hand would twitch, pressing the touchpad and firing the cannon that was displayed on the sender’s computer screen across campus.

Researchers found that accuracy varied among the pairs, ranging from 25 to 83 percent. Misses mostly were due to a sender failing to accurately execute the thought to send the “fire” command. The researchers also were able to quantify the exact amount of information that was transferred between the two brains.

Another research team from the company Starlab in Barcelona, Spain, recently published results in the same journal showing direct communication between two human brains, but that study only tested one sender brain instead of different pairs of study participants and was conducted offline instead of in real time over the Web.

Now, with a new grant from the W.M. Keck Foundation, the UW research team is taking the work a step further in an attempt to decode and transmit more complex brain processes.

The project could also eventually lead to “brain tutoring,” in which knowledge is transferred directly from the brain of a teacher to a student.

“Imagine someone who’s a brilliant scientist but not a brilliant teacher. Complex knowledge is hard to explain – we’re limited by language,” said co-author Chantel Prat, a faculty member at the Institute for Learning & Brain Sciences and a UW assistant professor of psychology.  The technology may even be the precursor of a neural bridge, like the one depicted in Pacific Rim.



SOURCE  University of Washington

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 Semiconductors  

Scientists have developed what they believe is the thinnest-possible semiconductor, a new class of nanoscale materials made in sheets only three atoms thick.

R esearchers at the University of Washington have developed what they believe is the thinnest-possible semiconductor, a new class of nanoscale materials made in sheets only three atoms thick.

The scientists have demonstrated that two of these single-layer semiconductor materials can be connected in an atomically seamless fashion known as a heterojunction. This result could be the basis for next-generation flexible and transparent computing, better light-emitting diodes, or LEDs, and solar technologies.

“Heterojunctions are fundamental elements of electronic and photonic devices,” said senior author Xiaodong Xu, a UW assistant professor of materials science and engineering and of physics. “Our experimental demonstration of such junctions between two-dimensional materials should enable new kinds of transistors, LEDs, nanolasers, and solar cells to be developed for highly integrated electronic and optical circuits within a single atomic plane.”

The research was published online this week in Nature Materials.

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The researchers discovered that two flat semiconductor materials can be connected edge-to-edge with crystalline perfection. They worked with two single-layer, or monolayer, materials – molybdenum diselenide and tungsten diselenide – that have very similar structures, which was key to creating the composite two-dimensional semiconductor.

Collaborators from the electron microscopy center at the University of Warwick in England found that all the atoms in both materials formed a single honeycomb lattice structure, without any distortions or discontinuities. This provides the strongest possible link between two single-layer materials, necessary for flexible devices. Within the same family of materials it is feasible that researchers could bond other pairs together in the same way.

The researchers created the junctions in a small furnace at the UW. First, they inserted a powder mixture of the two materials into a chamber heated to 900 degrees Celsius (1,652 F). Hydrogen gas was then passed through the chamber and the evaporated atoms from one of the materials were carried toward a cooler region of the tube and deposited as single-layer crystals in the shape of triangles.

As seen under an optical microscope, the heterostructures have a triangular shape. The two different monolayer semiconductors can be recognized through their different colors. Image Source - University of Washington

After a while, evaporated atoms from the second material then attached to the edges of the triangle to create a seamless semiconducting heterojunction.

"Our experimental demonstration of such junctions between two-dimensional materials should enable new kinds of transistors, LEDs, nanolasers, and solar cells to be developed for highly integrated electronic and optical circuits within a single atomic plane."

“This is a scalable technique,” said Sanfeng Wu, a UW doctoral student in physics and one of the lead authors. “Because the materials have different properties, they evaporate and separate at different times automatically. The second material forms around the first triangle that just previously formed. That’s why these lattices are so beautifully connected.”

With a larger furnace, it would be possible to mass-produce sheets of these semiconductor heterostructures, the researchers said. On a small scale, it takes about five minutes to grow the crystals, with up to two hours of heating and cooling time.

“We are very excited about the new science and engineering opportunities provided by these novel structures,” said senior author David Cobden, a UW professor of physics. “In the future, combinations of two-dimensional materials may be integrated together in this way to form all kinds of interesting electronic structures such as in-plane quantum wells and quantum wires, superlattices, fully functioning transistors, and even complete electronic circuits.”

The researchers have already demonstrated that the junction interacts with light much more strongly than the rest of the monolayer, which is encouraging for optoelectric and photonic applications like solar cells.


SOURCE  University of Washington

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 Artificial Intelligence  

Computer scientists from the University of Washington and the Allen Institute for Artificial Intelligence in Seattle have created the first fully automated computer program that teaches everything there is to know about any visual concept.

Computer scientists from the University of Washington and the Allen Institute for Artificial Intelligence in Seattle have created the first fully automated computer program that teaches everything there is to know about any visual concept. Called Learning Everything about Anything, or LEVAN, the program searches millions of books and images on the Web to learn all possible variations of a concept, then displays the results to users as a comprehensive, browsable list of images, helping them explore and understand topics quickly in great detail.

"The program learns to tightly couple rich sets of phrases with pixels in images. This means that it can recognize instances of specific concepts when it sees them."

“It is all about discovering associations between textual and visual data,” said Ali Farhadi, a UW assistant professor of computer science and engineering. “The program learns to tightly couple rich sets of phrases with pixels in images. This means that it can recognize instances of specific concepts when it sees them.”

The research team will present the project and a related paper this month at the Computer Vision and Pattern Recognition annual conference in Columbus, Ohio.

The program learns which terms are relevant by looking at the content of the images found on the Web and identifying characteristic patterns across them using object recognition algorithms. It’s different from online image libraries because it draws upon a rich set of phrases to understand and tag photos by their content and pixel arrangements, not simply by words displayed in captions.

Users can browse the existing library of roughly 175 concepts. Existing concepts range from “airline” to “window,” and include “beautiful,” “breakfast,” “shiny,” “cancer,” “innovation,” “skateboarding,” “robot,” and the researchers’ first-ever input, “horse.”

If the concept you’re looking for doesn’t exist, you can submit any search term and the program will automatically begin generating an exhaustive list of subcategory images that relate to that concept. For example, a search for “dog” brings up the obvious collection of subcategories: Photos of “Chihuahua dog,” “black dog,” “swimming dog,” “scruffy dog,” “greyhound dog.” But also “dog nose,” “dog bowl,” “sad dog,” “ugliest dog,” “hot dog” and even “down dog,” as in the yoga pose.

The technique works by searching the text from millions of books written in English and available on Google Books, scouring for every occurrence of the concept in the entire digital library. Then, an algorithm filters out words that aren’t visual. For example, with the concept “horse,” the algorithm would keep phrases such as “jumping horse,” “eating horse” and “barrel horse,” but would exclude non-visual phrases such as “my horse” and “last horse.”

Once it has learned which phrases are relevant, the program does an image search on the Web, looking for uniformity in appearance among the photos retrieved. When the program is trained to find relevant images of, say, “jumping horse,” it then recognizes all images associated with this phrase.

“Major information resources such as dictionaries and encyclopedias are moving toward the direction of showing users visual information because it is easier to comprehend and much faster to browse through concepts. However, they have limited coverage as they are often manually curated. The new program needs no human supervision, and thus can automatically learn the visual knowledge for any concept,” said Santosh Divvala, a research scientist at the Allen Institute for Artificial Intelligence and an affiliate scientist at UW in computer science and engineering.

The research team also includes Carlos Guestrin, a UW professor of computer science and engineering. The researchers launched the program in March with only a handful of concepts and have watched it grow since then to tag more than 13 million images with 65,000 different phrases.

Right now, the program is limited in how fast it can learn about a concept because of the computational power it takes to process each query, up to 12 hours for some broad concepts. The researchers are working on increasing the processing speed and capabilities.

The team wants the open-source program to be both an educational tool as well as an information bank for researchers in the computer vision community. The team also hopes to offer a smartphone app that can run the program to automatically parse out and categorize photos.


SOURCE  University of Washington

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 Genomics  

Scientists found that there is a second code hiding within DNA. The discovery of what are being called "duons" has major implications for how scientists and physicians interpret a patient’s genome and will open new doors to the diagnosis and treatment of disease.

R esearchers have discovered a second code hiding within DNA. This second code contains information that changes how scientists read the instructions contained in DNA and interpret mutations to make sense of health and disease.

A research team led by Dr. John Stamatoyannopoulos, University of Washington associate professor of genome sciences and of medicine, made the discovery. The findings were reported in the journal Science.

The work is part of the Encyclopedia of DNA Elements Project, also known as ENCODE. The National Human Genome Research Institute funded the multi-year, international effort. ENCODE aims to discover where and how the directions for biological functions are stored in the human genome.

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Since the genetic code was deciphered in the 1960s, scientists have assumed that it was used exclusively to write information about proteins. The University of Washington scientists were stunned to discover that genomes use the genetic code to write two separate languages. One describes how proteins are made, and the other instructs the cell on how genes are controlled. One language is written on top of the other, which is why the second language remained hidden for so long.

“For over 40 years we have assumed that DNA changes affecting the genetic code solely impact how proteins are made,” said Stamatoyannopoulos. “Now we know that this basic assumption about reading the human genome missed half of the picture. These new findings highlight that DNA is an incredibly powerful information storage device, which nature has fully exploited in unexpected ways.”

The genetic code uses a 64-letter alphabet called codons. The UW team discovered that some codons, which they called duons, can have two meanings, one related to protein sequence, and one related to gene control. These two meanings seem to have evolved in concert with each other. The gene control instructions appear to help stabilize certain beneficial features of proteins and how they are made.

The discovery of duons has major implications for how scientists and physicians interpret a patient’s genome and will open new doors to the diagnosis and treatment of disease.

“The fact that the genetic code can simultaneously write two kinds of information means that many DNA changes that appear to alter protein sequences may actually cause disease by disrupting gene control programs or even both mechanisms simultaneously,” said Stamatoyannopoulos.

The research (and the way it has been presented)  has a critical response so far, but it could lead to new discoveries and treatments for disease.


SOURCE  University of Washington

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 Brain-Computer Interfaces  

University of Washington researchers have demonstrated that when humans use this technology – called a brain-computer interface – the brain behaves much like it does when completing simple motor skills such as kicking a ball, typing or waving a hand. Learning to control a robotic arm or a prosthetic limb could become second nature for people who are paralyzed.

Small electrodes placed on or inside the brain allow patients to interact with computers or control robotic limbs simply by thinking about how to execute those actions. This technology could improve communication and daily life for a person who is paralyzed or has lost the ability to speak from a stroke or neurodegenerative disease.

Now, University of Washington researchers have demonstrated that when humans use this technology – called a brain-computer interface – the brain behaves much like it does when completing simple motor skills such as kicking a ball, typing or waving a hand. Learning to control a robotic arm or a prosthetic limb could become second nature for people who are paralyzed.

“What we’re seeing is that practice makes perfect with these tasks,” said Rajesh Rao, a UW professor of computer science and engineering and a senior researcher involved in the study. “There’s a lot of engagement of the brain’s cognitive resources at the very beginning, but as you get better at the task, those resources aren’t needed anymore and the brain is freed up.”

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Rao and UW collaborators Jeffrey Ojemann, a professor of neurological surgery, andJeremiah Wander, a doctoral student in bioengineering, published their results online June 10 in the Proceedings of the National Academy of Sciences.

In this study, seven people with severe epilepsy were hospitalized for a monitoring procedure that tries to identify where in the brain seizures originate. Physicians cut through the scalp, drilled into the skull and placed a thin sheet of electrodes directly on top of the brain. While they were watching for seizure signals, the researchers also conducted this study.

The patients were asked to move a mouse cursor on a computer screen by using only their thoughts to control the cursor’s movement. Electrodes on their brains picked up the signals directing the cursor to move, sending them to an amplifier and then a laptop to be analyzed. Within 40 milliseconds, the computer calculated the intentions transmitted through the signal and updated the movement of the cursor on the screen.

Researchers found that when patients started the task, a lot of brain activity was centered in the prefrontal cortex, an area associated with learning a new skill. But after often as little as 10 minutes, frontal brain activity lessened, and the brain signals transitioned to patterns similar to those seen during more automatic actions.

“Now we have a brain marker that shows a patient has actually learned a task,” Ojemann said. “Once the signal has turned off, you can assume the person has learned it.”

While researchers have demonstrated success in using brain-computer interfaces in monkeys and humans, this is the first study that clearly maps the neurological signals throughout the brain. The researchers were surprised at how many parts of the brain were involved.

“We now have a larger-scale view of what’s happening in the brain of a subject as he or she is learning a task,” Rao said. “The surprising result is that even though only a very localized population of cells is used in the brain-computer interface, the brain recruits many other areas that aren’t directly involved to get the job done.”

Several types of brain-computer interfaces are being developed and tested. The least invasive is a device placed on a person’s head that can detect weak electrical signatures of brain activity. Basic commercial gaming products are on the market, but this technology isn’t very reliable yet because signals from eye blinking and other muscle movements interfere too much.

A more invasive alternative is to surgically place electrodes inside the brain tissue itself to record the activity of individual neurons. Researchers at Brown University and the University of Pittsburghhave demonstrated this in humans as patients, unable to move their arms or legs, have learned to control robotic arms using the signal directly from their brain.

The UW team tested electrodes on the surface of the brain, underneath the skull. This allows researchers to record brain signals at higher frequencies and with less interference than measurements from the scalp. A future wireless device could be built to remain inside a person’s head for a longer time to be able to control computer cursors or robotic limbs at home.

“This is one push as to how we can improve the devices and make them more useful to people,” Wander said. “If we have an understanding of how someone learns to use these devices, we can build them to respond accordingly.”


SOURCE  University of Washington

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 Gesture Control  

University of Washington researchers have shown it's possible to leverage wi-fi signals around us to detect specific movements without needing sensors on the human body or cameras. Using a wi-fi router and a few wireless devices in the living room, users could control their electronic devices from any room in the home with a simple gesture.

Forget to turn off the lights before leaving the apartment? No problem. Just raise your hand, finger-swipe the air, and your lights will power down. Want to change the song playing on your music system in the other room? Move your hand to the right and flip through the songs.  Sound like a fantasy?  It may not be according to researchers at the University of Washington.

University of Washington computer scientists have developed gesture-recognition technology that brings this a step closer to reality. Researchers have shown it’s possible to leverage Wi-Fi signals around us to detect specific movements without needing sensors on the human body or cameras.

By using an adapted Wi-Fi router and a few wireless devices in the living room, users could control their electronics and household appliances from any room in the home with a simple gesture.

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“This is repurposing wireless signals that already exist in new ways,” said lead researcher Shyam Gollakota, a UW assistant professor of computer science and engineering. “You can actually use wireless for gesture recognition without needing to deploy more sensors.”

The UW research team that includes Shwetak Patel, an assistant professor of computer science and engineering and of electrical engineering and his lab, published their findings online this week. This technology, which they call “WiSee,” is to appear at The 19th Annual International Conference on Mobile Computing and Networking.

The concept is similar to Xbox Kinect – a commercial product that uses cameras to recognize gestures – but the UW technology is simpler, cheaper and doesn’t require users to be in the same room as the device they want to control. That’s because Wi-Fi signals can travel through walls and aren’t bound by line-of-sight or sound restrictions.

The UW researchers built a “smart” receiver device that essentially listens to all of the wireless transmissions coming from devices throughout a home, including smartphones, laptops and tablets. A standard Wi-Fi router could be adapted to function as a receiver.

WiSee technology uses multiple antennas to focus on one user to detect the person’s gesture.

When a person moves, there is a slight change in the frequency of the wireless signal. Moving a hand or foot causes the receiver to detect a pattern of changes known as the Doppler frequency shift.

These frequency changes are very small – only several hertz – when compared with Wi-Fi signals that have a 20 megahertz bandwidth and operate at 5 gigahertz. Researchers developed an algorithm to detect these slight shifts. The technology also accounts for gaps in wireless signals when devices aren’t transmitting.

The technology can identify nine different whole-body gestures, ranging from pushing, pulling and punching to full-body bowling. The researchers tested these gestures with five users in a two-bedroom apartment and an office environment. Out of the 900 gestures performed, WiSee accurately classified 94 percent of them.

“This is the first whole-home gesture recognition system that works without either requiring instrumentation of the user with sensors or deploying cameras in every room,” said Qifan Pu, a collaborator and visiting student at the UW.

The system requires one receiver with multiple antennas. Intuitively, each antenna tunes into a specific user’s movements, so as many as five people can move simultaneously in the same residence without confusing the receiver.

WiSee technology uses multiple antennas to focus on one user to detect the person’s gesture.
If a person wants to use the WiSee, she would perform a specific repetition gesture sequence to get access to the receiver. This password concept would also keep the system secure and prevent a neighbor – or hacker – from controlling a device in your home.

Once the wireless receiver locks onto the user, she can perform normal gestures to interact with the devices and appliances in her home. The receiver would be programmed to understand that a specific gesture corresponds to a specific device.

Collaborators Patel and Sidhant Gupta, a doctoral student in computer science and engineering, have worked with Microsoft Research on two similar technologies – SoundWave, which uses sound, and Humantenna, which uses radiation from electrical wires – that both sense whole-body gestures. But WiSee stands apart because it doesn’t require the user to be in the same room as the receiver or the device.

In this way, a smart home could become a reality, allowing you to turn off the oven timer with a simple wave of the hand, or turn on the coffeemaker from your bed.

The researchers plan to look next at the ability to control multiple devices at once. The initial work was funded by the UW department of computer science and engineering.



SOURCE  University of Washington

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