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Tampilkan postingan dengan label brain computer interface. Tampilkan semua postingan
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Kamis, 16 Maret 2017

The Entrepreneur with the $100 Million Plan to Link Brains to Computers

The Entrepreneur with the $100 Million Plan to Link Brains to Computers

  • ILLUSTRATION BY KEITH RANKIN
  • Rewriting Life

    Via MIT Tech Review

    https://www.technologyreview.com/s/603771/the-entrepreneur-with-the-100-million-plan-to-link-brains-to-computers/






    Tech big shots are charging into neuroscience, but do they even have a clue?
    Entrepreneur Bryan Johnson says he wanted to become very rich in order to do something great for humankind.
    Last year Johnson, founder of the online payments company Braintree, starting making news when he threw $100 million behind Kernel, a startup he founded to enhance human intelligence by developing brain implants capable of linking people’s thoughts to computers.
    Johnson isn’t alone in believing that “neurotechnology” could be the next big thing. To many in Silicon Valley, the brain looks like an unconquered frontier whose importance dwarfs any achievement made in computing or the Web.
    According to neuroscientists, several figures from the tech sector are currently scouring labs across the U.S. for technology that might fuse human and artificial intelligence. In addition to Johnson, Elon Musk has been teasing a project called “neural lace,” which he said at a 2016 conference will lead to “symbiosis with machines.” And Mark Zuckerberg declared in a 2015 Q&A that people will one day be able to share “full sensory and emotional experiences,” not just photos. Facebook has been hiring neuroscientists for an undisclosed project at Building 8, its secretive hardware division.
    As these people see it, computing keeps achieving new heights, but our ability to interface with silicon is stuck in the keyboard era. Even when speaking to a computer program like Alexa or Siri, you can convey at most about 40 bits per second of information and only for short bursts. Compare that to data transfer records of a trillion bits per second along a fiber-optic cable.
    “Ridiculously slow,” Musk complained.
    But it turns out that connecting to the brain isn’t so easy. Six months after launching Kernel amid a media blitz, Johnson says he’s dropped his initial plans for a “memory implant,” switched scientific advisors, hired a new team, and decided to instead invest in developing a more general-purpose technology for recording and stimulating the brain using electrodes.
    Johnson says the switch-up is part of trying something new. “If you look at the key contributing technologies of society, the ones with the most impact, like rockets, the Internet, biology—there was a transition point from academia to the private sector, and for the most part neuroscience hasn’t made that jump,” says Johnson. “The most critical element is timing, when is the right time to pursue this.”
    Memory implants
    After making a fortune selling Braintree to eBay for $800 million in 2013, Johnson, now 39, reportedly sought the advice of nearly 200 people on how to invest his new wealth. He settled on neurotechnology and, last August, he announced he’d create Kernel and build the first neural prosthetic for human intelligence enhancement.
    But Johnson’s business plan was extremely vague; one scientist called it “metaphysical.” Kernel’s website was plastered with book-jacket-like endorsements from scientific celebrities including J. Craig Venter and Tim O’Reilly, extolling his “great” and “serious” commitment to understanding human intelligence, not to mention the impressive $100 million he later promised to invest in Kernel.
    Bryan Johnson
    The reality is that interfacing with the brain is tough: electronics irritate its tissue and stop working after a while, and no one will get brain surgery just in order to send an e-mail. What’s more, even if you can communicate with the brain, you might not know what it is saying.
    “Billionaires entering the broader neurotechnology field are very optimistic and may overlook details of the problem, which is we are far away from meaningfully understanding the brain,” says Konrad Kording, a Northwestern University neuroscientist who has advised Johnson. “But neurotechnology allows you to work on the most interesting questions in the universe while potentially making money, and so that is exciting.”
    Johnson’s persona is part buttoned-down Mormon missionary (he once was one), part hard-driving door-to-door credit-processing salesman (he was that too), but now, with his new wealth, he’s also taken on the mantle of a technology prophet. At a 2016 startup conference in Silicon Valley, he showed up with his hair unbrushed, wearing a T-shirt with holes in it, and gave a wide-ranging lecture on human tool use from prehistory into the present, arguing that now “our very existence is programmable” through biology and machine interfaces.
    Kernel’s original technology was a memory prosthesis, developed by Theodore Berger of the University of Southern California, who until recently was also the company’s chief scientific officer. Berger’s technology (see “10 Breakthrough Technologies: Memory Implants”) is a way of recording memories of rats and monkeys, storing these patterns on a computer chip, and re-delivering them to the hippocampus. One version of the setup, Berger says, has been tested in a handful of human patients undergoing brain surgery for other reasons.
    But a mere six months after starting Kernel, Berger is no longer part of the company, and memory implants are no longer part of Kernel’s near-term plans. Johnson and Berger both confirmed the separation.
    Berger’s vision, according to several people, was too complex, too speculative, and too far from becoming a medical reality, while Johnson hoped to see a return on his investment sometime soon. “They have a new direction, but we’re still talking,” says Berger. “The basic reason is it was going to take too long. It’s one thing to think about this and quite another to do it.”
    Johnson says he concluded that Berger’s work “is really interesting, but not an entry point” into a commercially viable business.
    Brain interface
    By last November, Johnson was already exploring a pivot for his company, meeting with Christian Wentz, head of a small Cambridge startup, Kendall Research Systems, that sells equipment for recording in the neurons of mice and other animals. The company spun out of the laboratory of Edward Boyden, a professor at MIT who invents new ways of analyzing brain tissue.  
    In February, Johnson acquired Wentz’s company (for an undisclosed sum) and with it brought in a new team, including Wentz and Adam Marblestone, a noted theorist of both the limitations and possibilities of brain interfaces, who will become chief scientific officer. Both are former Boyden lab members, as are two other Kernel scientists, Caroline Moore-Kochlacs and Jake Bernstein.

    Johnson says Kernel will now develop a “generalized human electrophysiology platform”—that is, a flexible way of measuring the electrical impulses from many neurons at once, and stimulating them, too. The eventual objective is to use such electronics to treat major diseases, like depression or Alzheimer’s. “It’s for clinical use,” he says. “We are a for-profit company.”  
    Wentz says as part of the acquisition he and Johnson agreed that much more R&D on brain interfaces will probably be needed. “We have a very sober view of what can and can’t be done,” Wentz says. “We are not naïve.” He calls Kernel’s effort a “15-year endeavor,” although he adds that “we want to do in that period what has been done in the last 100 years.”
    With the pivot, Johnson is effectively jumping on an opportunity created by the Brain Initiative, an Obama-era project which plowed money into new schemes for recording neurons. That influx of cash has spurred the formation of several other startups, including Paradromics and Cortera, also developing novel hardware for collecting brain signals. As part of the government brain project, the defense R&D agency DARPA says it is close to announcing $60 million in contracts under a program to create a “high-fidelity” brain interface able to simultaneously record from one million neurons (the current record is about 200) and stimulate 100,000 at a time.
    “It’s time for neuroscience to graduate from academia to a general neuroscience platform,” says Johnson. With such a technology “a whole range of new applications—a lot of white space—would open up.”
    Johnson declined to describe the specifics of Kernel’s technological approach to connecting with the brain, as did Boyden and Wentz. However, the team members have been working on well-identified problems. Wentz has been involved with developing electronics for high-speed reading of data emitted by wireless implants. Already, the flow of information that can be collected from a mouse’s brain in real time outruns what a laptop computer can handle. The team also needs a way to interface with the human brain. Boyden’s lab has worked on several concepts to do so, including needle-shaped probes with tiny electrodes etched onto their surface. Another idea is to record neural activity by threading tiny optical fibers through the brain’s capillaries, an idea roughly similar to Musk’s neural lace.

    More sophisticated means of reading and writing to the brain are seen as potential ways to treat psychiatric disorders. Under a concept that Boyden calls “brain coprocessors,” it may be possible to create closed-loop systems that detect certain brain signals—say, those associated with depression—and shock the brain to reverse them. Some surgeons and doctors funded by another DARPA program are in the early stages of determining whether serious mental conditions can be treated in this way (see “A Shocking Way to Fix the Brain”).
    Boyden says Johnson’s $100 million makes a big difference to how he and his students view the entrepreneur’s goals. “A lot of neurotechnology has come and gone. But one thing is that it’s very expensive,” he says. “The inventing is expensive, the clinical work is expensive. It’s not easy. And here is someone putting money into the game.”

    Rabu, 15 Maret 2017

    How to ensure future brain technologies will help and not harm society

    How to ensure future brain technologies will help and not harm society


    A boy who was addicted to the internet, has his brain scanned for research purposes at Daxing Internet Addiction Treatment Center in Beijing February 22, 2014.  As growing numbers of young people in China immerse themselves in the cyber world, spending hours playing games online, worried parents are increasingly turning to boot camps to crush addiction. Military-style boot camps, designed to wean young people off their addiction to the internet, number as many as 250 in China alone. Picture taken February 22, 2014. REUTERS/Kim Kyung-Hoon (CHINA - Tags: SOCIETY)ATTENTION EDITORS - PICTURE 21 OF 33 FOR PACKAGE 'CURING CHINA'S INTERNET ADDICTS'TO FIND ALL IMAGES SEARCH 'INTERNET BOOT CAMP' - RTR3WL7Y
    We need a more informed public debate on neuroscience
    Image: REUTERS/Kim Kyung-Hoon 
    Written by
    Murali Doraiswamy, Professor, Duke University Health System
    Hermann Garden, Organisation for Economic Co-operation and Development
    David Winickoff, Organisation for Economic Co-operation and Development
    Wednesday 1 March 2017
    Thomas Edison, one of the great minds of the second industrial revolution, once said that “the chief function of the body is to carry the brain around.” Understanding the human brain – how it works, and how it is afflicted by diseases and disorders – is an important frontier in science and society today.
    Advances in neuroscience and technology increasingly impact intellectual wellbeing, education, business, and social norms. Recent findings confirm the plasticity of the brain over the individual’s life. Imaging technologies and brain stimulation technologies are opening up totally new approaches in treating disease and potentially augmenting cognitive capacity. Unravelling the brain’s many secrets will have profound societal implications that require a closer “contract” between science and society.

    Convergence across physical science, engineering, biological science, social science and humanities has boosted innovation in brain science and technological innovation. It offers large potential for a systems biology approach to unify heterogeneous data from “omics” tools, imaging technologies such as fMRI, and behavioural science. 

    Citizen science – the convergence between science and society – already proved successful in EyeWire where people competed to map the 1,000-neuron connectome of the mouse retina. Also, the use of nanoparticles as coating of implanted abiotic devices offers great potential to improve the immunologic acceptance of invasive diagnostics. Brain-inspired neuromorphic engineering aims to develop novel computer systems with brain-like characteristics, including low energy consumption, adequate fault tolerance, self-learning capabilities, and some sort of intelligence. Here, the convergence of nanotechnology with neuroscience could help building neuro-inspired computer chips; brain-machine interfaces and robots with artificial intelligence systems.
    Future opportunities for cognitive enhancement for improved attentiveness, memory, decision making, and control through, for example, non-invasive brain stimulation and neural implants have raised, and shall continue to raise, profound ethical, legal, and social questions. What is societally acceptable and desirable, both now and in the future? 

    At a recent OECD workshop, we identified five possible systemic changes that could help speed up neurotechnology developments to meet pressing health challenges and societal needs.

    1. Responsible research
    There is growing interest in discussing and unpacking the ethical and societal aspects of brain science as the technologies and applications are developed. Much can be learned from other experiences in disruptive innovation. The international Human Genome Project (1990-2003), for example, was one of the earlier large-scale initiatives in which social scientists worked in parallel with the natural sciences in order to consider the ethical, legal and social issues (ELSI) of their work. 
    The deliberation of ELSI and Responsible Research and Innovation (RRI) in nanotechnologies is another example of how societies, in some jurisdictions, have approached R&D activities, and the role of the public in shaping, or at least informing, their trajectory. RRI knits together activities that previously seemed sporadic. According to Jack Stilgoe, Senior Lecturer in the Department of Science and Technology Studies, University College London, the aim of responsible innovation is to connect the practice of research and innovation in the present to the futures that it promises. 
    Frameworks, such as ELSI and RRI should more actively engage patients and patient organisations early in the development cycle, and in a meaningful way. This could be achieved through continuous public platforms and policy discussion instead of traditional one-off public engagement and the deliberation of scientific advances and ELSI through culture and art. 
    Research funders – public agencies, private investors, foundations, as well as universities themselves – are particularly well positioned to shape trajectories of technology and society. Through their funding power, they have unique capacity to help place scientific work within social, ethical, and regulatory contexts. 
    It is an opportune time for funders to: 1) strengthen the array of approaches and mechanisms for building a robust and meaningful neurotechnology landscape that meaningfully engages human values and is informed by it; 2) discuss options to foster open and responsible innovation; and 3) better understand the opportunities and challenges for building joint initiatives in research and product development.
    2. Anticipatory governance
    Society and industry would benefit from earlier, and more inclusive, discussions about the ethical, legal and social implications of how neurotechnologies are being developed and their entry onto the market. For example, the impact of neuromodulatory devices that promise to enhance cognition, alter mood, or improve physical performance on human dignity, privacy, and equitable access could be considered earlier in the research and development process.
    3. Open innovation
    Given the significant investment risks and high failure rates of clinical trials in central nervous systems disorders, companies could adopt more open innovation approaches in which public and private stakeholders actively collaborate, share assets including intellectual property, and invest together.
    4. Avoiding neuro-hype
    Popular media is full of colourful brain images used to illustrate stories about neuroscience. Unproven health claims, including those which give rise to so-called ‘neuro-hype’ and ‘neuro-myths’. Misinformation is a strong possibility where scientific work potentially carries major social implications (for example, work on mental illness, competency, intelligence, etc). 
    It has the potential to result in public mistrust and to undermine the formation of markets. There is a need for evidence-based policies and guidelines to help the responsible development and use of neurotechnology in medical practice and in over-the-counter products. Policymakers and regulators could lead the development of a clear path to translate neurotechnology discoveries into human health advantages that are commercially viable and sustainable.
    5. Access and equity
    Policymakers should discuss the socio-economic questions raised by neurotechnology. Rising disparities in access to often high-priced medical innovation require tailored solutions for poorer countries. The development of public-private partnerships and simplification of technology help access to innovation in resource-limited countries.
    In addition to helping people with neurological and psychiatric disorders, the biggest cause of disability worldwide, neurotechnologies will shape every aspect of society in the future. A roadmap for guiding responsible research and innovation in neurotechnology may be transformative.

    Kamis, 02 Maret 2017

    Steering A Turtle With Your Thoughts

    Steering A Turtle With Your Thoughts



    Researchers have developed a technology that can remotely control an animal’s movement with human thought. 

    Asian Scientist Newsroom | March 2, 2017 | Technology AsianScientist (Mar. 2, 2017) 

    Researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed a brain-computer interface (BCI) that can control a turtle using human thought. Their findings have been published in the Journal of Bionic Engineering. 

    Unlike previous research—most notably in insects—that has tried to control animal movement by applying invasive methods Professors Lee Phill-Seung and Jo Sungho of KAIST propose a conceptual system that can guide an animal’s moving path by controlling its instinctive escape behavior. 

    They chose a turtle because of its cognitive abilities as well as its ability to distinguish different wavelengths of light. Specifically, turtles can recognize a white light source as an open space and so move toward it. They also show specific avoidance behavior to things that might obstruct their view. 

    Turtles also move toward and away from obstacles in their environment in a predictable manner. The entire human-turtle setup is as follows: A head-mounted display (HMD) is combined with a BCI to immerse the human user in the turtle’s environment. The human operator wears the BCI-HMD system, while the turtle has a ‘cyborg system’—consisting of a camera, Wi-Fi transceiver, computer control module, and battery—all mounted on the turtle’s upper shell. Also included on the turtle’s shell is a black semi-cylinder with a slit, which forms the ‘stimulation device.’ This can be turned ±36 degrees via the BCI. The human operator receives images from the camera mounted on the turtle. These real-time video images allow the human operator to decide where the turtle should move. The human provides thought commands that are recognized by the wearable BCI system as electroencephalography signals. The BCI can distinguish between three mental states: left, right, and idle. The left and right commands activate the turtle’s stimulation device via Wi-Fi, turning it so that it obstructs the turtle’s view. This invokes its natural instinct to move toward light and change its direction. 

    Finally, the human acquires updated visual feedback from the camera mounted on the shell and in this way continues to remotely navigate the turtle’s trajectory. The researchers demonstrates the animal guiding BCI in a variety of environments, with turtles moving indoors and outdoors on many different surfaces, like gravel and grass, and tackling a range of obstacles, such as shallow water and trees. This technology could be developed to integrate positioning systems and improved augmented and virtual reality techniques, enabling various applications, including devices for military reconnaissance and surveillance. 

    The article can be found at: Kim et al. (2016) Remote Navigation of Turtle by Controlling Instinct Behavior via Human Brain-computer Interface

    Source: Korea Advanced Institute of Science and Technology. Disclaimer: This article does not necessarily reflect the views of AsianScientist or its staff. Read more from Asian Scientist Magazine at: https://www.asianscientist.com/2017/03/tech/turtle-human-brain-computer-interface/

    Kamis, 23 Februari 2017

    Brain–Computer Interface Allows Speediest Typing to Date

    Brain–Computer Interface Allows Speediest Typing to Date

    Via Scientific American -- https://www.scientificamerican.com/article/brain-computer-interface-allows-speediest-typing-to-date/
    A new interface system allowed three paralyzed individuals to type words up to four times faster than the speed that had been demonstrated in earlier studies
    A participant enrolled by Stanford University in the BrainGate clinical trial uses the brain-computer interface to type by controlling a computer cursor with her thoughts. Credit: Courtesy Stanford University
    Ten years ago Dennis Degray’s life changed forever when he slipped and fell while taking out the trash in the rain. He landed on his chin, causing a severe spinal cord injury that left him paralyzed below the neck. Now he’s the star participant in an investigative trial of a system that aims to help people with paralysis type words using only their thoughts.
    The promise of brain–computer interfaces (BCIs) for restoring function to people with disabilities has driven researchers for decades, yet few devices are ready for widespread practical use. Several obstacles exist, depending on the application. For typing, however, one important barrier has been reaching speeds sufficient to justify adopting the technology, which usually involves surgery. A study published Tuesday in eLife reports the results of a system that enabled three participants—Degray and two people with amyotrophic lateral sclerosis (ALS, or Lou Gehrig's disease, a neurodegenerative disease that causes progressive paralysis)—to type at the fastest speeds yet achieved using a BCI—speeds that bring the technology within reach of being practically useful. “We're approaching half of what, for example, I could probably type on a cell phone,” says neurosurgeon and co-senior author, Jaimie Henderson of Stanford University.
    The researchers measured performance using three tasks. To demonstrate performance in the most natural scenario possible, one participant was assessed in a “free typing” task, where she just answered questions using the device. But typing speeds are conventionally measured using copy typing, which involves typing out set phrases, so all three participants were also assessed this way. The woman who performed the free-typing task achieved faster than six words-per-minute, the other ALS patient managed nearly three and Degray achieved almost eight. The group reported comparable results in a Nature Medicine studyin 2015 but these were achieved using software that exploited the statistics of English to predict subsequent letters. No such software was employed in this study.
    The drawback of copy typing is performance can vary with the specific phrases and keyboard layouts used. To get a measure independent of any of these factors, the third task involved selecting squares on a six by six grid as they lit up randomly. This gets closer to quantifying the maximum speed the system can output information, and is easily converted into a digital “bits per second” measure. The team used this range of tasks, without predictive software, because one of the study’s central aims was to develop standardized measures. “We need to establish measures so that—in spite of potential variability between people, methods and researchers—we can really say, ‘clearly this new advance led to higher performance,’ because we have systematic ways of comparing that,” says co-lead author Chethan Pandarinath, then a postdoctoral fellow at Stanford. “It's critical for moving this technology forward.”
    The two ALS patients achieved 2.2 and 1.4 bits per second, respectively, more than doubling previous records (held by these same participants in a previous study from this group). Degray achieved 3.7 bits per second, which is four times faster than the previous best speed. “This is a pretty large leap in performance in comparison to previous clinical studies of BCIs,” Pandarinath says.
    Other researchers agree these are state-of-the-art results. “This is the fastest typing anyone has shown with a BCI,” says biomedical engineer Jennifer Collinger, of the University of Pittsburgh, who was not involved in the study. “It's on par with technologies like eye-trackers, but there are groups those technologies don’t work for such as people who are “locked-in.” These speeds also approach what ALS patients questioned in a survey said they would want from a BCI device. “You're getting to the point where performance is good enough that users would actually want to have it,” Collinger says.
    Participants had either one or two tiny (one-sixth-inch) electrode arrays implanted on the surfaces of their brains. These “intracortical” implants contain 96 microelectrodes that penetrate one to 1.5 millimeters into parts of the motor cortex that control arm movements. Two of the surgeries were performed by Henderson, who co-directs Stanford’s Neural Prosthetics Translational Laboratory with the study’s senior co-author, bioengineer Krishna Shenoy. The neural signals recorded by the electrodes are transmitted via a cable to a computer where algorithms developed in Shenoy's lab decode the participant's intentions and translate the signals into movements of a computer cursor. The Stanford team is part of a multi-institute consortium called BrainGate, which includes Massachusetts General Hospital and Brown University, among others.
    Other methods of interfacing with the brain via electrodes include those put on the scalp for electroencephalography (EEG) and ones placed under the skull on the brain’s surface, known as electrocorticography (ECoG). The advantage of intracortical implants is they can pick out activity from single cells whereas the other methods capture the average activity of thousands of neurons. “This performance is 10 times better than anything you would get from EEG or ECoG, [which don’t] contain enough information to do this kind of task at this level,” says neurobiologist Andrew Schwartz, at Pitt, who was not involved in the study. Movement and scarring reduces signal quality over roughly the first two years after implantation, but what remains is still useful—“much better than you get with any other technique,” he says.
    The biggest drawback, currently, is having wires coming out of people's heads and attached to cables, which is cumbersome and carries risks. “The future is making these devices wireless,” Pandarinath says. “We're not there yet with people but we’re probably closer to five than 10 years away, and that’s a critical step [toward] a device that you could send somebody home with and be less worried about potential risks like infection.” The devices would need wireless power but several groups are already working on this. “Most of the technology is basically there,” Schwartz says. “You can do that inductively using coils—like wirelessly charging your cell phone in a cradle with coils on either side.”
    The team attributes the improvements to better systems engineering and decoding algorithms. “Performing repeated computations rapidly is critical in a real-time control system,” Pandarinath says. The researchers published a study last year, led by Stanford bioengineer Paul Nuyujukian. In it they trained two macaque monkeys to perform a similar task to the grid exercise used in this study. The animals typed sentences by selecting characters on a screen as they changed color (although they wouldn’t have understood what the words meant). When the team added a separate algorithm to detect the monkeys’ intention to stop, their best speed increased by two words per minute.
    This “discrete click decoder” was also used in the current study. “We've basically created a ‘point and click’ interface here, like a mouse. That’s a good interface for things like modern smartphones or tablets,” Pandarinath says, “which would open a whole new realm of function beyond communication: surfing the Web, playing music, all sorts of things able-bodied people take for granted.”
    The Stanford team is already investigating wireless technology, and has ambitious long-term goals for the project. “The vision we hope to achieve someday would be to be able to plug a wireless receiver into any computer and use it using your brain,” Henderson says. “One of our main goals is to allow 24 hours a day, seven days a week, 365 days a year control of a standard computer interface using only brain signals.”

    Rabu, 11 Mei 2016

    Wheelchair controlled by facial expressions to hit the market within 2 years

    Wheelchair controlled by facial expressions to hit the market within 2 years


    Wheelie uses facial commands to move
    Wheelie uses facial commands to move. View gallery (4 images)
    Brazilian researchers have developed a wheelchair that can be controlled through small facial, head or iris movements. The team at Faculdade de Engenharia Elétrica e de Computação da Universidade Estadual de Campinas (FEEC/Unicamp) says the technology could help people with cerebral palsy, those who have suffered a stroke or live with amyotrophic lateral sclerosis and other conditions that prevent precise hand movements.
    The team started to look into brain-computer interface (BCI) techniques (the acquisition and processing of signals that allow communication between the brain and an external device) in 2011. The group then decided to test those techniques in a real life situation.
    The researchers built their prototype from a standard motored chair, removed the joystick and equipped it with sensors that can gauge the distance between walls and other objects, and pick up variations on floor surface.
    A notebook that sends commands directly to the chair using a 3D camera running on Intel's RealSense technology was installed, which caters for interaction with a computer through facial and body expressions. RealSense is a stand-alone camera that uses depth-sensing technology and can be attached to any computer. The set includes a standard camera, an infrared laser projector, an infrared camera and a microphone array.
    "The camera can identify more than 70 facial points around the mouth, nose and eyes. By moving these points, it is possible to get simple commands, such as forward, backward, left or right and, most importantly, stop," says the researcher Eleri Cardozo. Voice interaction is also possible, but the researchers say it less reliable because of differences in voice timbre and ambient noise.
    The chair was also equipped with a Wi-Fi antenna that allows a caregiver to steer the equipment remotely through the internet, which could be handy should a wheelchair user get tired. 
    For patients with more serious conditions where facial movement is not possible, the team is also looking into a BCI technology that can pick signals directly from the brain through external electrodes and turn them into commands, though this type of equipment has not been added on the robotic chair yet.
    The research team recently received extra funding to move forward with the project so it can be adapted and marketed in Brazil within the next two years. "Our objective is that the final product costs, at most, twice as much as joystick-controlled models, which sell for around R$7,000 (US$1,994)." To do that, a start-up called Hoo-Box has been created, which will focus on developing the Wheelie system further.
    The researchers presented their project at the Third Brainn Congress in the city of Campinas, Brazil, in April.
    Source: Fapesp

    Rabu, 30 September 2015

    Neural Implant Enables Paralyzed ALS Patient to Type Six Words per Minute

    By Eliza Strickland
    Posted

    Photo: Stanford University/Nature Medicine
    A paralyzed ALS patient uses a brain implant to steer a computer cursor to various targets.

    Typing six words per minute may not sound very impressive. But for paralyzed people typing via a brain-computer interface (BCI), it’s a new world record.
    To pull off this feat, two paralyzed people used prosthetics implanted in their brains to control computer cursors with unprecedented accuracy and speed. The experiment, reported today in Nature Medicine, was the latest from a team testing a neural system called BrainGate2. While this implant is only approved for experiments right now, researchers say this demonstration proves that such technology can be truly useful to quadriplegics, and points the way toward regular at-home use.
    The two people who volunteered for this study have amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig’s disease, a degenerative neural disorder that leads to complete paralysis. Lead researcher Jaimie Henderson, co-director of Stanford’s Neural Prosthetics Translational Lab, calls it a “humbling experience” to work with quadriplegic patients who willingly undergo brain surgery and devote themselves to science experiments that will push forward this early-stage technology. “They’ve become true partners with us in this endeavor,” Henderson says.
    The BrainGate2 system consists of an array of minuscule electrodes implanted, in this case, in a region of the motor cortex known as the “hand knob.” The electrodes record the patterns of electrical activity in the neurons there, which fire when the person either moves or imagines moving their hand. The BrainGate2 system also includes decoding software, which turns a messy signal into a clear command for an external device—in this case, a computer cursor. Other experiments have used BCIs to control robotic arms, and they could theoretically be used to control wheelchairs, cars, or anything else that can be moved by remote control.
    In this study’s first task, the participants repeatedly moved their cursors to targets on a computer screen (see video below), which they accomplished by imagining their index fingers moving on computer trackpads. They each averaged about 2.5 seconds per target. This is a significant improvement over a previous BrainGate2 trial, in which a different patient performed the same task but averaged 8.5 seconds per target.

    The improvement, Henderson says, came from four factors.
    1) The system architecture provided faster processing than before. With a lag time of only about 20 milliseconds between the user’s thought and the cursor’s action, the participants got useful feedback while doing the task.
    2) Signal processing filters carefully extracted the neural signals from the ambient electromagnetic noise—a necessity, as these experiments were conducted in the volunteers’ homes.
    3) The imagined motion that the participants ultimately used to control the cursor (an index finger moving on a trackpad) provided a clearer neural signal than other imagined motions they tried out (whole arm and wrist movements).
    ​4) Perhaps most importantly, an improved decoding algorithm was better able to translate neural signals into intended movements. Essentially, it was better able to identify the direction the user intended to steer the cursor, and could therefore correct for deviations in the neural signal that would have otherwise steered the cursor off track.
    But what about the typing, you ask? For that task, the participants used the same imagined finger movement to pick out letters in a text-entering program called Dasher. With this interface, once the user selects a letter, the program predicts which letters are likely to come next and makes them easier to select, speeding up the construction of words.

    One of the participants typed 115 words in 19 minutes, or about 6 words per minute. That user had previous experience with the Dasher interface using a different control method, but it’s still a pretty impressive result. While this participant is still able to talk naturally, such a communication method could benefit people who have lost the ability to control their mouth muscles, such as people with more advanced ALS or “locked-in” patients.
    Henderson and his colleagues have previously surveyed people with paralysis to see whether they’d be eager to adopt BCI technologies in their everyday lives, and what capabilities they’d hope to gain from such gear. High on the wish-list was the ability to communicate easily through fast typing, which the survey defined as 40 words per minute.
    Henderson says he has plenty of ideas for how to reach that ambitious target. A future study might make use of electrodes implanted in a region of the brain that encodes a person’s intentions to move, before they actually make a motion. “We want to see if using those signals from the planning part of the brain helps improve performance,” he says.
    It’s not clear what level of performance will be required before an implanted BCI device is considered ready for domestic use. But Henderson thinks the BrainGate2 system is well on the way: “We think we’re making very good progress,” he says.   

    Rabu, 26 Agustus 2015

    CSUF News Service

    Student Research for ALS Patients

    User-Friendly Prototype Helps People Communicate Online

    ALS Interface TrialStudent David Diaz tests an electronic communication system with the help of alumnus Dean Zarkos, which would allow him and other ALS patients to use a computer using thoughts, facial expressions and head movements.
    Computer engineering major Krystle Ilisastigui hopes her efforts to help develop a high-tech communication device will improve the quality of life for those with amyotrophic lateral sclerosis, or ALS.
    Ilisastigui and several of her classmates are developing an electronic communication system to enable ALS patients to access the Internet and communicate via email, text document, chat or Skype using thoughts, facial expressions and head movements, said Kiran George, associate professor of computer engineering.
    George and his students have worked on the prototype since February — supported by a $100,000 grant from the Oakland-based Disability Communications Fund — and partnered with the ALS Association Orange County Chapter to fine-tune the technology and design.
    This summer, the communication device was tested with the help of patients at the chapter's Tustin office.
    "I have 100 percent faith in you," said Cal State Fullerton alumnus Dean Zarkos, diagnosed with ALS in 2011, as students placed a wireless headset on him.
    With the device, patients like Zarkos — who uses a motorized wheelchair and is unable to move his hands, arms or legs — can communicate online with head tilts and facial expressions.
    "What they are doing is phenomenal: it's cutting-edge technology. Anything that can help patients like myself is a tremendous asset for us," said Zarkos '78 (B.A. political science) of Seal Beach, who holds an MBA and law degree and owns a property management business.
    "I can see it opening up the world for people like me. You can do email — communicate with anybody. These students make me proud to be a Titan."
    ALS, commonly known as Lou Gehrig's disease, is a progressive neurodegenerative disease. An estimated 75 percent of ALS patients lose their ability to speak, along with use of their hands, said George. Speech problems are progressive, and most will experience a severe breakdown in their ability to communicate with others, he added.
    "Patients face tremendous barriers that make electronic communication a challenge. This inability to communicate is equally frustrating and emotionally devastating," George added. "But this device will help them to engage in electronic communication and allow them to stay connected to friends and family."
    What is most appealing about the technology is that the device is user-friendly, requires minimal training and is low cost, observed Jared Mullins '04 (B.A. political science), executive director of the ALS Orange County Chapter.
    The wireless communication system utilizes commercially off-the-shelf components to minimize design time and cost, George explained. The goal is to keep the device's cost under $150.
    While the project allows students to apply what they learn in class and put it to practical use, it also is an eye-opening experience in seeing how their work could help ALS patients regain control of simple tasks.
    "It's been challenging and a great learning experience for us to work directly with the patients," said graduate student David Diaz. "It's real hands-on — something you are not going to get in the classroom."
    Fellow graduate student Aaron Castillo added that one of the biggest challenges has been to personalize the device to meet patients' needs as the disease progresses.
    "We're going to give this project everything we have; we just want to help," Castillo said.
    George and his students also are working on other brain-controlled systems for ALS patients, in which thoughts and expressions can be used to control a robotic arm and electric wheelchair.
    For more photos, visit online
    - See more at: http://news.fullerton.edu/2015su/ALS-research.aspx#sthash.VTDHjMjP.dpuf

    Jumat, 14 Maret 2014

    Reading Brains

    Reading Brains

    By Erica Klarreich
    Communications of the ACM, Vol. 57 No. 3, Pages 12-14
    10.1145/2567649





    patient wearing cap with electrodes
    A patient wears a cap studded with electrodes during a demonstration of a noninvasive brain-machine interface by the Swiss Federal Institute of Technology of Lausanne in January 2013.
    Credit: Fabrice Coffrini / AFP / Getty Images



    Mind reading has traditionally been the domain of mystics and science fiction writers. Increasingly, however, it is becoming the province of serious science.
    A new study from the laboratory of Marcel van Gerven of Radboud University Nijmegen in the Netherlands demonstrates it is possible to figure out what people are looking at by scanning their brains. When volunteers looked at handwritten letters, a computer model was able to produce fuzzy images of the letters they were seeing, based only on the volunteers' brain activity.
    The new work—which builds on an earlier mathematical model by Bertrand Thirion of the Institute for Research in Computer Science and Control in Gif-sur-Yvette, France—establishes a simple, elegant brain-decoding algorithm, says Jack Gallant, a neuroscientist at the University of California, Berkeley. Such decoding algorithms eventually could be used to create more sophisticated brain-machine interfaces, he says, to allow neurologically impaired people to manipulate computers and machinery with their thoughts.
    As technology improves, Gallant predicts, it eventually will be possible to use this type of algorithm to decode thoughts and visualizations, and perhaps even dreams. "I believe that eventually, there will be something like a radar gun that you can just point at someone's head to decode their mental state," he says. "We have the mathematical framework we need, for the most part, so the only major limitation is how well we can measure brain activity."


    A Simple Model

    In the new study, slated to appear in an upcoming issue of the journal Neuroimage, volunteers looked at handwritten copies of the letters B, R, A, I, N, and S, while a functional magnetic resonance imaging (fMRI) machine measured the responses of their primary visual cortex (V1), the brain region that does the initial, low-level processing of visual information. The research team then used this fMRI data to train a Bayesian computer model to read the volunteers' minds when they were presented with new instances of the six letters.
    "It's a very elegant study," says Thomas Naselaris, a neuroscientist at The Medical University of South Carolina in Charleston.
    According to Bayes' Law, to reconstruct the handwritten image most likely to have produced a particular pattern of brain activity, it is necessary to know two things about each candidate image: the "forward model," the probability that the candidate image would produce that particular brain pattern; and the "prior," the probability of that particular image cropping up in a collection of handwritten letters. Whichever candidate image maximizes the product of these two probabilities is the most likely image for the person to have seen.
    To create the forward model, the research team showed volunteers hundreds of different handwritten images of the six letters while measuring their brain activity, then used machine-learning techniques to model the most likely brain patterns that any new image would produce. To construct the prior, the team again set machine learning algorithms to work on 700 additional copies of each letter, to produce a model of the most likely arrangements of pixels when people write a letter by hand. Both models used simple linear Gaussian probability distributions, making brain decoding into a straightforward calculation, van Gerven says.
    "We've shown that simple mathematical models can get good reconstructions," he says.
    The research team also experimented with limiting the model's prior knowledge of the world of handwritten letters. If the model's prior information consisted only of images of the letters R, A, I, N, and S, for example, it could still produce decent reconstructions of the letter B, though not as good as when the prior included images of all six letters. The results, van Gerven says, demonstrate the decoding algorithm's ability to generalize—to reconstruct types of letters it has never "seen" before.
    The human brain is, of course, the master of this kind of generalization, and this ability goes much farther than simple reconstruction of unfamiliar images. "The visual system can do something no robot can do," Naselaris says. "It can walk into a room filled with things it has never seen before and identify each thing and understand the meaning of it all."
    While van Gerven's paper deals only with reconstructing the image a person has seen, other researchers have taken first steps toward deciphering the meanings a brain attaches to visual stimuli. For example, Gallant's group (including Naselaris, formerly a postdoc at Berkeley) has combined data from V1 and higher-order visual processing regions to reconstruct both the image a person has seen and the brain's interpretation of the objects in the image. More recently, in partially unpublished work, the team has done the same thing for movies, instead of still images.
    "We are starting to build a repertoire of models that can predict what is going on in higher levels of the vision hierarchy, where object recognition is taking place," Naselaris says.
    Other researchers are working on reading a brain's thoughts as it responds to verbal stimuli. For example, in 2010, the laboratory of Tom Mitchell at Carnegie Mellon University in Pittsburgh developed a model that could reconstruct which noun a person was reading. Van Gerven's lab is currently working on decoding the concepts volunteers consider as they listen to a children's story while inside an fMRI scanner.


    Probing Thoughts

    Most mind-reading research to date has focused on reconstructing the external stimuli creating a particular pattern of brain activity. A natural question is whether brain-decoding algorithms can make the leap to reconstructing a person's private thoughts and visualizations, in the absence of any specific stimulus.
    The answer depends on the extent to which, for example, the brain processes mental images and real images in the same way. "The hypothesis is that perception and imagery activate the same brain regions in similar ways," van Gerven says. "There have been hints that this is largely the case, but we are not there yet."
    If, Naselaris says, "highly visual processes get evoked when you are just reasoning through something—planning your day, say—then it should be possible to develop sensitive probes of internal thoughts and do something very much like mind-reading just from knowing how V1 works," he says. "But that is a big 'if.' "
    Even if mind-reading turns out not to be as simple as decoding V1, Naselaris predicts that as neuroscientists develop forward models of the brain's higher-level processing regions, the decoding models will almost certainly provide a portal into people's thoughts. "I don't think there is anything that futuristic about the idea that in five to 20 years, we will be able to make pictures of what people are thinking about, or transcribe words that people are saying to themselves," he says.
    What may prove more difficult, Gallant says, is digging up a person's distant memories or unconscious associations. "If I ask you the name of your first-grade teacher, you can probably remember it, but we do not understand how that is being stored or represented," he says. "For the immediate future, we will only be able to decode the active stuff you're thinking about right now."
    Dream decoding is likely to prove another major challenge, Naselaris says. "There is so much we don't understand about sleep," he says. "Decoding dreams is way out in the future; that's my guess."
    Part of the problem is that with dreams, "you never have ground truth," Gallant says. When it comes to building a model of waking thoughts or visions, it is always possible to ask the person what he or she is thinking, or to directly control the stimuli the person's brain is receiving, but dreams have no reality check.
    The main option available to researchers, therefore, is to build models for reconstructing movies, and then treat a dream as if it were a movie playing in the person's mind. "That is not a valid model, but we use it anyway," Gallant says. "It is not going to be very accurate, but since we have no accuracy now, having lousy accuracy is better than nothing."
    In May 2013, a team led by Yukiyasu Kamitani of ATR Computational Neuroscience Laboratories in Kyoto, Japan, published a study in which they used fMRI data to reconstruct the categories of visual objects people experienced during hypnagogic dreams, the ones that occur as a person drifts into sleep. "They are not real dreams, but it is a proof of concept that it should be possible to decode dreams," Gallant says.


    Protecting Privacy

    The dystopian future Gallant pictures, in which we could read each other's private thoughts using something like a radar gun, is not going to happen any time soon. For now, the best tool researchers have at their disposal, fMRI, is at best a blunt instrument; instead of measuring neuronal responses directly, it can only detect blood flow in the brain—which Gallant calls "the echoes of neural activity." The resulting reconstructions are vague shadows of the original stimuli.
    What is more, fMRI-based mind reading is expensive, low-resolution, and the opposite of portable. It is also easily thwarted. "If I did not want my mind read, I could prevent it," Naselaris says. "It is easy to generate noisy signals in an MRI; you can just move your head, blink, think about other things, or go to sleep."
    These limitations also make fMRI an ineffective tool for most kinds of brain-machine interfaces. It is conceivable fMRI could eventually be used to allow doctors to read the thoughts of patients who are not able to speak, Gallant says, but most applications of brain-machine interfaces require a much more portable technology than fMRI.

    "The hypothesis is that perception and imagery activate the same brain regions in similar ways," van Gervan says.

    However, given the extraordinary pace at which technology moves, some more effective tool will replace fMRI before too long, Gallant predicts. When that happens, the brain decoding algorithms developed by Thirion, van Gerven, and others should plug right into the new technology, Gallant says. "The math is pretty much the same framework, no matter how we measure brain activity," he says.
    Despite the potential benefit to patients who need brain-machine interfaces, Gallant is concerned by the thought of a portable mind-reading technology. "It is pretty scary, but it is going to happen," he says. "We need to come up with privacy guidelines now, before it comes online."


    Further Reading

    Horikawa, T., Tamaki, M., Miyawaki, Y., Kamitani, Y.
    Neural Decoding of Visual Imagery During Sleep, Science Vol. 340 No., 6132, 639–642, 3 May 2013.
    Kay, K. N., Naselaris, T., Prenger, R. J., Gallant, J. L.
    Identifying Natural Images from Human Brain Activity, Nature 452, 352–355, March 20, 2008. http://www.ncbi.nlm.nih.gov/pubmed/18322462
    Mitchell, T., Shinkareva, S., Carlson, A., Chang, K-M., Malave, V., Mason, R., Just, M.
    Predicting Human Brain Activity Associated with the Meanings of Nouns, Science Vol. 320 No. 5880, 1191–1195, 30 May 2008. http://www.sciencemag.org/content/320/5880/1191
    Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu, B., Gallant, J. L.
    Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies, Current Biology Vol. 21 Issue 19, 1641–1646, 11 October 2011. http://www.sciencedirect.com/science/article/pii/S0960982211009377
    Schoenmakers, S., Barth, M., Heskes, T. van Gerven, M.
    Linear Reconstruction of Perceived Images from Human Brain Activity, Neuroimage 83, 951-61, December 2013. http://www.ncbi.nlm.nih.gov/pubmed/23886984
    Thirion, B., Duchesnay, E., Hubbard, E., Dubois, J., Poline, J. B., Lebihan, D., Dehaene, S.
    Inverse Retinotopy: Inferring the Visual Content of Images from Brain Activation Patterns, Neuroimage 33, 1104–1116, December 2006. http://www.ncbi.nlm.nih.gov/pubmed/17029988


    Author

    Erica Klarreich is a mathematics and science journalist based in Berkeley, CA.


    Figures

    UF1Figure. A patient wears a cap studded with electrodes during a demonstration of a noninvasive brain-machine interface by the Swiss Federal Institute of Technology of Lausanne in January 2013.