Category Archives: Neurotechnology

Ben Williamson

The brain sciences are playing an increasingly powerful role in the development of the digital technologies thatmay augment everyday life in future years. Neurotechnology is a broad field of brain-centred technical R&D. It includes advanced imaging systems for real-time brain monitoring and mining the mind via the collection of brain data, but also new and emerging brain stimulator systems that may have thecapacity to influence brain activity. Along with new developments in data-driven psycho-informatics in the field of psychology,the possibilities associated with brain-machine interaction have begun to attract educational interest, raising significant concerns about how young peoples mental states may in the future be governed through neurotechnology.

The human brain has become the focus of intense interest across scientific, technical R&D, governmental, and commercial domains in recent years. Neuroscientific research into the brain itself has advanced significantly with the development and refinement of brain imaging neurotechnologies. Driven by massive research grants and private partnerships, huge teams of neuroscience experts associated with international projectssuch as the US-led BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative and European Human Brain Projecthave begun to visualize and build wiring diagrams and computational models of the cells and neural circuits of the brain at a highly granular, neuromolecular level of detail and fidelity, all based on the collection and analysis of massive records of brain data.

This knowledge of the brain developed by neuroscience is being applied to the design of new brain-machine interface technologies such as neuroprosthetic devices that can be implanted in the brainwith algorithms that can translate thought into movementand noninvasive neurostimulators that might modify cognition and emotions. In the last few months, technology entrepreneurs from some of Silicon Valleys most successful companies have also begun to concentrateR&D resources on Brain-Computer Interfaces (BCI) and brain-signalled remote control of devicesas well as more speculative attempts to hybridize the human brain with artificial intelligence implants. Tesla boss Elon Musk, for instance, has established Neuralink to use brain implants to directly link human minds to computers and augment the slow, imprecise communication of our voices with a direct brain-to-computer linkup. Facebook, meanwhile, has announced it is pursuing the development of a new kind of noninvasive brain-machine interfacepossibly a cap or headbandthat lets people text and share their thoughts by simply thinking rather than typing. Its intention is to use optical technologies to use light, like LEDs or lasers, to sense neural signals emanating from the cerebral cortex.

At the same time, the brain is being treated as an inspiration for the design of neurocomputing systems. These complex cognitive computing, neural networks and AI systems are designed to emulate some of the brains capacities, especially for efficient low-energy information storage, processing, retrieval and learning, in order to maximize the efficiency and speed of big data processing and machine learning algorithms. Neural-network research, for example, focuses simultaneously on improving understanding of the human brain and nervous system, and on using that knowledge to find inspiration to construct information processing systems inspired by natural, biological functions and thus gain the advantages of these systems.The development of bio-inspired or bio-mimetic systems in neural-network research, and neurocomputing more generally, is already being applied in many settings, notably through companies like IBM. IBMs recent advances in cognitive computing, such as Watson, take inspiration from neuroscience for the design of brain-like neural networks algorithms and neurocomputational devices that are now being deployed in healthcare, business and educational settings.

A huge field has developed around Brain-Computer Interface research and development too. BCI, or sometimes Brain-Machine Interface R&D, depends on signal processing of brain data to allow brain activities to control external devices or even computers through electrodesthe enabling technologies that allow brain information to be encoded by different techniques and algorithms providing input to control devices. Although previously largely confined to clinical and laboratory research, the possibilities of brain-machine mental control have begun to attract significant research grant funding along with commercial interest in recent years. The growth in interest at least partly stems from advances in BCI R&D which have seen the invasive implantation of microelectrodes within the brain itself being displaced by increasingly noninvasive techniques. Noninvasive BCI does not involve penetration of the scalp or skull with electrode implantsbut still holds the potential for mental control over devices through the real-time capture of brain activity data using portable EEG neuroimaging technologies.

Various portable and wearable EEG headbands that allow easy attachment of electrodes to the skull have become commercially and clinically available, with brand-names including Emotiv, Neurosky, BrainBand, Myndwave and BrainControl. Mental control videogaming is a major commercial application of BCI. Further out in R&D terms, other neuroscience inspired brain interface proposals include neural dust consisting of microscopic free-floating sensors that could be spread around the brain.

The policy implications of neuroscientific and neurotechnological development have been articulated by, among others, the Potomac Institute for Policy Studies, a policy institute with its own Center for Neurotechnology Studies. Its report on enhancing the brain and reshaping society has called for collaborative efforts between policymakers, scientists and the private sector to develop novel neurotechnologies that can improve individuals cognitive abilities and behaviours as well as the social order, and thereby ensure neuroenhancement of the individual will result in enrichment of our society as a whole.

As with all technical development, neurotechnology is not merely technical.It isimprinted with powerful social visions ofa future in which brain data can be used to know and monitor populations, and to enhance the mental states of individuals to meet certain objectives and aspirations for society at large.

Neurotechnological development and application of neuroenhancement techniques may seem far removed from education. However, neuroscience itself is currently enjoying fast growth within educational research and practice, with new research centres in educational neuroscienceappearing, with support from grant awarding bodies,andresearch results and applications increasingly being shared by global community using the Twitter hashtag #edneuro. Thejournal Learning, Media and Technology ran a special issue in 2015 on neuroscience and educational technology.

Various neurotechnologies such as brain imaging are being used by ed-neuro researchers in ways which are intended to generate insights for educational policymakers and practitioners.One ed-neuro study hasmade use of mobile, wearable EEG headbands to study students brain-to-brain synchrony within the classroom context. EEG neuroimaging has even been used to visualize the brain lighting up when students have adopted a growth mindset. Attempts have also been made to use brain imaging technologies to analyse the possible biological mechanisms by which socio-economic status influences and effects brain and cognitive development in children. Studies have used neuroimaging toexamine whether socioeconomic status correlates with differences in brain structure, and measured the electrical activity in the brains of children from lower SES groups todetect deficits in their selective attention. Such studies and conclusions have begun to influence policymakers, who can interpret the results to specify remedial interventions such as early years education provision. In these ways, neurotechnologies are becoming integral parts of new policy science approaches, the instruments that enable policymakers tosee policy problems visualized in the neurobiological detail provided by highly persuasive brain images.

Neurotechnology-based cognitive computing systems developed by commercial organizations have also appeared in the educational landscape. The edu-business Pearson has partnered with IBM to bring IBMs Watson system into the learning process, as previously detailed. For at least the last decade, IBM has been engaged in an extensive program of brain-based computing R&D, involving neurocomputing, neural-network research and the development of specific neurosynaptic and neuromorphic hardware and software. For IBM, as detailed in its white paper on Computing, cognition and the future of knowing, cognitive tools are natural systems with human qualities which are inspiring the next generation of human cognition, in which we think and reason in new and powerful ways:

Its true that cognitive systems are machines that are inspired by the human brain. But its also true that these machines will inspire the human brain, increase our capacity for reason and rewire the ways in which we learn.

Pearson has itself articulated a vision of AI teaching assistants and cognitive tutors using technologies based on advances in educational neuroscience and psychology. For both Pearson and IBM cognitive computing does not just mean smarter computing systems, but cognitively optimized individuals whose very brain circuitry has been rewired through interfacing and interactingwith machine cognition.

Political support for commercial educational neurotechnology has also emerged. Recently-appointed head of the US Department of Education, the private-education advocate Betsy DeVos, is a major investor and former board member of Neurocore, a brain-training treatment company that specializes in neurofeedback technology. The company uses real-time EEG with electrodes attached to the scalp to diagnose individuals symptomsby comparing their brainwaves to a massive database of others brainwaves. Its proprietorial neurofeedback software can then be applied to run a game that rewardsthe desired brain activity. Over time, Neurocore claims, the brain starts to learn to produce activity that was rewarded by the increase in stimulation. One of Neurocores targets is children with ADHD (Attention Deficit Hyperactivity Disorder); its natural treatments with drug-free neurofeedback work with a childs natural ability to learn, helping them reach their full potential (though its underlying neuroscience has been contested).

From a more speculative perspective the Center for Neurotechnology Studies at the Potomac Institute has issued a report on neurotechnology futures with some key implications for education.It describes how brain interface technologiescouldbecome applications for augmented cognition, including non-invasive devices that complement or supplement human capabilities, such as tools for learning and training augmentation. It has detailed how greater understanding of the neural mechanisms of learning and memory is needed to provide the appropriate theoretical basis for neurotechnologically enhancing learning and enabling the educational system to significantly improve teaching techniques for iteratively more complex knowledge. It even suggests the provocative possibility of technology that could down-load experience and facilitate learning in a time-compressed manner.

The Potomac Institute provides advice to the US military. And the US military Defense Advanced Research Projects Agency (DARPA) has itself begun exploring the potential to boost the acquisition of skills and learning through its Targeted Neuroplasticity Training (TNT) program, itself part of the BRAIN Initiative. The program aims to develop safe, noninvasiveneurostimulation methods for activating synaptic plasticitythe ability of the brain to connect neurons which is understood to be the neural requirement for learning. According to a press release from the TNT program manager,

Targeted Neuroplasticity Training (TNT) seeks to advance the pace and effectiveness of a specific kind of learningcognitive skills trainingthrough the precise activation of peripheral nerves that can in turn promote and strengthen neuronal connections in the brain. TNT will pursue development of a platform technology to enhance learning of a wide range of cognitive skills. The TNT program seeks to use peripheral nerve stimulation to speed up learning processes in the brain by boosting release of brain chemicals, such as acetylcholine, dopamine, serotonin, and norepinephrine. These so-called neuromodulators play a role in regulating synaptic plasticity, the process by which connections between neurons change to improve brain function during learning. By combining peripheral neurostimulation with conventional training practices, the TNT program seeks to leverage endogenous neural circuitry to enhance learning by facilitating tuning of neural networks responsible for cognitive functions.

Although TNT is primarily aimed at military training, it clearly indicates how the scientific and technical possibilities of neurotechnology are being taken up in relation to education and learning.

At least one educational entrepreneur has leapt upon the potential of frictionless brain-computer interfaces of the kind imagined by DARPA, Silicon Valley entrepreneurs like Elon Musk and the vision of neurotechnologically-enhanced learning promoted by the Potomac Institute. Donald Clark, the founder of the AI-based online learning company Wildfire Learning, the worlds first AI content creation service for education, has imagined that invisible, frictionless and seamless interfaces between human brains and AI will have massive implications for education:

The implications for learning are obvious. When we know what you think, we know whether you are learning, optimise that learning, provide relevant feedback and also reliably assess. To read the mind is to read the learning process. We are augmenting the brain by making it part of a larger network ready to interface directly with knowledge and skills, at first with deviceless natural interfaces using voice, gesture and looks, then frictionless brain communications and finally seamless brain links. Clumsy interfaces inhibit learning, clean smooth, deviceless, frictionless and seamless interfaces enhance and accelerate learning. This all plays to enhancing the weaknesses of the evolved biological brain and [to] think at levels beyond the current limitations of our flawed brains.

These aspirations for the future of education merge the scientific R&D of the emerging ed-neuro field with the kind of techno-optimism often found in educational technology, or ed-tech, development and marketing, to suggest the emergence of a new hybrid field of ed-neurotech.

Like the plans of Musk and Facebook, the ed-neurotech imaginary of a deviceless, frictionless and seamless neurotechnological future of education is likely to be highly controversial and contested. Part of this resistance will be on primarily technical and scientific groundsneurotechnologies of brain imaging are one thing, and seamless neuroenhancement of the so-called flawed brain quite another. But another part of the resistance will be animated by concerns over theaspirations of either governments or commercial companies to engage in mental interference andcognitive modification of young people.

Neuroenhancement may not be quite as scientifically and technically feasible yet as its advocates hope, but the fact remains that certain powerful individuals and organizations want it to happen. Theyhave attached their technicalaspirations to particular visions of social order and progress that appear to be attainable through the application of neurotechnologies to brain analytics and even neuro-optimization. As STS researchers of neuroscience Simon Williams, Stephen Katz & Paul Martin have argued,theprospects of cognitive enhancementare part of a neurofuture in-the-making that needs as much critical scrutiny as the alleged brain facts produced by brain scanning technologies.

In anew article on neuroscience, neurotechnology and human rights, the bioethicists Marcello Ienca and Roberto Andorno have mapped outsome of the challengesraised by these emergingbrain-society-computer entanglements. The neurotechnology revolution in neuroimaging, they argue, highlights how the possibility of mining the mind (or at least informationally rich structural aspects of the mind) can be potentially used not only to infer mental preferences, but also to prime, imprint or trigger those preferences. They note how brain imaging techniques have been taken up in pervasive neurotechnology applications such asBCIsthat use EEG recordings to monitor electrical activity in the brain for a variety of purposes including neuromonitoring (real time evaluation of brain functioning), neurocognitive training (using certain frequency bands to enhance neurocognitive functions), and noninvasive brain device control.

In addition to neuroimaging and brain-computer interface and device control, however, Ienca and Andorno also note the emergence of brain stimulators or neurostimulators. Unlike neuroimaging tools, these are not primarilyused for recording or decoding brain activity but rather for stimulating or modulating brain activity electrically.Available neurostimulators include portable, easy-to-use, consumer-based transcranial direct current stimulation (tDCS) devices aimed at optimizing brain performance on a variety of cognitive tasks, and applications based on transcranial magnetic stimulation (TMS), a magnetic method used to briefly stimulate small regions of the brain for both diagnostic and therapeutic purposes, which has also evolved into portable devices. In sum, they state,

if in the past decades neurotechnology has unlocked the human brain and made it readable under scientific lenses, the upcoming decades will see neurotechnology becoming pervasive and embedded in numerous aspects of our lives and increasingly effective in modulating the neural correlates of our psychology and behaviour.

The emergence of neuroimaging, neuromodulation of behaviours,andcognition-stimulating neurotechnologiestherefore raises considerable challenges, as Ienca and Androno articulate them:

These concerns reflect the emergence of what some social scientific critics of the brain sciences have begun to term neurogovernance or neuropower.As Victoria Pitts-Taylor puts it in her recent book The Brains Body, neuroscience-based programs designed to mould and modulate behaviour through targeting the brain for modification represent strategies of preemptive neurogovernance that are intended to promote the economic and political optimization of the population. She notes how neuroscience concepts like brain plasticity have been taken upby developers of cognitive exercises, brain-machine interfaces, drugs, supplements, electric stimulators, and brain mapping technologies, in order to target the brain for modification and rewiring.These technical advances clearly amplify the possibilities of preemptive neurogovernance, and the shaping of society and the social order through the modification of the mental states, affects and thoughts of individuals. The plasticity of the brain has become the basis for technoscientific ambitions to monitor, control and transform processes of life for political and commercial purposes, Pitts Taylor argues. And Nikolas Rose and Joelle Abi-Rached, in their book Neuro, have argued that the plastic brain is now the focus for attempts to govern the futureas is especially the case with interventions into the developing brains andhence future lives of children.

As a consequence, Ienca and Andorno suggest that neurotechnologies raisesignificant challenges for human rights.In particular they highlight recent debates about the right to cognitive liberty, or the right to alter ones mental states with the help of neurotools, and the associated right to refuse to do so. Ultimately, cognitive liberty is a conceptual update of the right to freedom of thought that takes into account the power available to states and companies to use neurotechnology coercively to manipulate the embrained mental states of citizens. They also add the right to mental privacy, defined as a neuro-specific privacy right which protects private or sensitive information in a persons mind from unauthorized collection, storage, use or even deletion in digital form or otherwise.Cognitive liberty and mental privacy, in other words, constitute new rights to take control of ones own mental life in the face of creeping techniques of neurogovernance in spheres of life including social media, government, consumption, and education.

Theapplication ofneurotechnology to education that we arejust beginning to detect needs to be undertaken in ways which are sensitive to issues of neurogovernance, cognitive liberty and mental privacy. As parts of an educational neurofuture in-the-making, optimistic aspirations towards neuroenhancement and cognitive modification of flawed brains through neurotechnologically enhanced education need to be countered not just with technical and scientific scepticism.Greater awareness of the political, militaryand commercial interests involved in new and developing neurotechnology markets and interventions are required, as well as theoretically engaged studies of the sociotechnical processes involved in producing neurotechnologies andoftheir uptake and effects in education. Deeply social questions also need to be asked about the use of brain data to exercise neuropower over young peoples mental states, and about how to safeguard their cognitive liberty and mental privacy amid persuasive and coercive promises about neuroenhancement in the direction of personal cognitive improvement.

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Posted on June 19, 2017

Neurotechnology has announced SentiVeillance Server, a new facial recognition solution designed for easy deployment on video surveillance systems.

Its compatible with the video management systems Evo Global, Evo S, Luxriot Evo, and Milestone XProtect VMS, enabling user to quickly identify faces in video streams, and to configure automatic alert notifications when certain faces or unknown faces are spotted. It also enables users to filter video by the age, gender, or face of individuals in the feed.

In a statement announcing the solution, Neurotechnology head of software development Aurimas Juska said SentiVeillance Server offers an enhanced surveillance system with only a small amount of configuration and no need for programming.

In keeping with Neurotechnologys recently upgraded SentiVeillance SDK, the new solution allows for up to ten different video feeds to be scanned simultaneously. A trial version is available now from Neurotechnology and the companys distributors.

June 19, 2017 by Alex Perala

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Neurotechnology Announces SentiVeillance Server Facial Recognition Solution - findBIOMETRICS

SentiVeillance Server supports most popular video management systems: Milestone XProtect VMS and Luxriot Evo, Evo S and Evo Global. SentiVeillance Server can process up to 10 video streams from multiple video management systems, all in real time.

SentiVeillance Server includes Neurotechnology's latest deep neural-network-based facial detection and recognition algorithm which significantly improves identification accuracy and speed. The algorithm is based on more than 13 years of development and research and has been tested in the NIST Face Recognition Vendor Test (FRVT) Ongoing. It is also included in other Neurotechnology products, such as the VeriLook and MegaMatcher software development kits (SDK), which have millions of deployments worldwide.

Neurotechnology also offers the SentiVeillance SDK for development of solutions using facial identification and object recognition from surveillance video.

SentiVeillance Server and the SDKs noted above are all available through Neurotechnology or from distributors worldwide. For more information and trial version, go to: http://www.neurotechnology.com.

About Neurotechnology

Neurotechnology is a developer of high-precision algorithms and software based on deep neural network (DNN) and other AI-related technologies. The company offers a range of products for biometric fingerprint, face, iris, palmprint and voice identification as well as AI, computer vision, object recognition and robotics. Drawing from years of academic research in the fields of neuroinformatics, image processing and pattern recognition, Neurotechnology was founded in 1990 in Vilnius, Lithuania and released its first fingerprint identification system in 1991. Since that time the company has released more than 130 products and version upgrades. More than 3000 system integrators, security companies and hardware providers integrate Neurotechnology's algorithms into their products, with millions of customer installations worldwide. Neurotechnology's algorithms also achieved top results in independent technology evaluations including NIST MINEX and IREX.

Media Contact Jennifer Allen Newton Bluehouse Consulting Group, Inc. +1-503-805-7540 jennifer(at)bluehousecg(dot)com

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New SentiVeillance Server from Neurotechnology Adds Face Recognition and Analytics to Video Management Systems - PR Newswire (press release)

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Accuray (ARAY) versus Stryker Corporation (SYK) Head-To-Head Review The Cerbat Gem The Company offers a range of medical technologies, including orthopedic, medical and surgical, and neurotechnology and spine products. The Company's segments include Orthopaedics; MedSurg; Neurotechnology and Spine, and Corporate and Other. $1.51 EPS Expected for Stryker Corporation (SYK)Weekly Register Stryker Corporation (SYK)Zacks.com all 93 news articles »

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Accuray (ARAY) versus Stryker Corporation (SYK) Head-To-Head Review - The Cerbat Gem

In the 1995 film Batman Forever, the Riddler used 3-D television to secretly access viewers most personal thoughts in his hunt for Batmans true identity. By 2011, the metrics company Nielsen had acquired Neurofocus and had created a consumer neuroscience division that uses integrated conscious and unconscious data to track customer decision-making habits. What was once a nefarious scheme in a Hollywood blockbuster seems poised to become a reality.

Recent announcements by Elon Musk and Facebook about brain-computer interface (BCI) technology are just the latest headlines in an ongoing science-fiction-becomes-reality story.

BCIs use brain signals to control objects in the outside world. Theyre a potentially world-changing innovation imagine being paralyzed but able to reach for something with a prosthetic arm just by thinking about it. But the revolutionary technology also raises concerns. Here at the University of Washingtons Center for Sensorimotor Neural Engineering (CSNE) we and our colleagues are researching BCI technology and a crucial part of that includes working on issues such as neuroethics and neural security. Ethicists and engineers are working together to understand and quantify risks and develop ways to protect the public now.

All BCI technology relies on being able to collect information from a brain that a device can then use or act on in some way. There are numerous places from which signals can be recorded, as well as infinite ways the data can be analyzed, so there are many possibilities for how a BCI can be used.

Some BCI researchers zero in on one particular kind of regularly occurring brain signal that alerts us to important changes in our environment. Neuroscientists call these signals event-related potentials. In the lab, they help us identify a reaction to a stimulus.

Examples of event-related potentials (ERPs), electrical signals produced by the brain in response to a stimulus. Tamara Bonaci, CC BY-ND

In particular, we capitalize on one of these specific signals, called the P300. Its a positive peak of electricity that occurs toward the back of the head about 300 milliseconds after the stimulus is shown. The P300 alerts the rest of your brain to an oddball that stands out from the rest of whats around you.

For example, you dont stop and stare at each persons face when youre searching for your friend at the park. Instead, if we were recording your brain signals as you scanned the crowd, there would be a detectable P300 response when you saw someone who could be your friend. The P300 carries an unconscious message alerting you to something important that deserves attention. These signals are part of a still unknown brain pathway that aids in detection and focusing attention.

P300s reliably occur any time you notice something rare or disjointed, like when you find the shirt you were looking for in your closet or your car in a parking lot. Researchers can use the P300 in an experimental setting to determine what is important or relevant to you. Thats led to the creation of devices like spellers that allow paralyzed individuals to type using their thoughts, one character at a time.

It also can be used to determine what you know, in whats called a guilty knowledge test. In the lab, subjects are asked to choose an item to steal or hide, and are then shown many images repeatedly of both unrelated and related items. For instance, subjects choose between a watch and a necklace, and are then shown typical items from a jewelry box; a P300 appears when the subject is presented with the image of the item he took.

Everyones P300 is unique. In order to know what theyre looking for, researchers need training data. These are previously obtained brain signal recordings that researchers are confident contain P300s; theyre then used to calibrate the system. Since the test measures an unconscious neural signal that you dont even know you have, can you fool it? Maybe, if you know that youre being probed and what the stimuli are.

Techniques like these are still considered unreliable and unproven, and thus U.S. courts have resisted admitting P300 data as evidence.

For now, most BCI technology relies on somewhat cumbersome EEG hardware that is definitely not stealth. Mark Stone, University of Washington, CC BY-ND

Imagine that instead of using a P300 signal to solve the mystery of a stolen item in the lab, someone used this technology to extract information about what month you were born or which bank you use without your telling them. Our research group has collected data suggesting this is possible. Just using an individuals brain activity specifically, their P300 response we could determine a subjects preferences for things like favorite coffee brand or favorite sports.

But we could do it only when subject-specific training data were available. What if we could figure out someones preferences without previous knowledge of their brain signal patterns? Without the need for training, users could simply put on a device and go, skipping the step of loading a personal training profile or spending time in calibration. Research on trained and untrained devices is the subject of continuing experiments at the University of Washington and elsewhere.

Its when the technology is able to read someones mind who isnt actively cooperating that ethical issues become particularly pressing. After all, we willingly trade bits of our privacy all the time when we open our mouths to have conversations or use GPS devices that allow companies to collect data about us. But in these cases we consent to sharing whats in our minds. The difference with next-generation P300 technology under development is that the protection consent gives us may get bypassed altogether.

What if its possible to decode what youre thinking or planning without you even knowing? Will you feel violated? Will you feel a loss of control? Privacy implications may be wide-ranging. Maybe advertisers could know your preferred brands and send you personalized ads which may be convenient or creepy. Or maybe malicious entities could determine where you bank and your accounts PIN which would be alarming.

The potential ability to determine individuals preferences and personal information using their own brain signals has spawned a number of difficult but pressing questions: Should we be able to keep our neural signals private? That is, should neural security be a human right? How do we adequately protect and store all the neural data being recorded for research, and soon for leisure? How do consumers know if any protective or anonymization measures are being made with their neural data? As of now, neural data collected for commercial uses are not subject to the same legal protections covering biomedical research or health care. Should neural data be treated differently?

Neuroethicists from the UW Philosophy department discuss issues related to neural implants. Mark Stone, University of Washington, CC BY-ND

These are the kinds of conundrums that are best addressed by neural engineers and ethicists working together. Putting ethicists in labs alongside engineers as we have done at the CSNE is one way to ensure that privacy and security risks of neurotechnology, as well as other ethically important issues, are an active part of the research process instead of an afterthought. For instance, Tim Brown, an ethicist at the CSNE, is housed within a neural engineering research lab, allowing him to have daily conversations with researchers about ethical concerns. Hes also easily able to interact with and, in fact, interview research subjects about their ethical concerns about brain research.

There are important ethical and legal lessons to be drawn about technology and privacy from other areas, such as genetics and neuromarketing. But there seems to be something important and different about reading neural data. Theyre more intimately connected to the mind and who we take ourselves to be. As such, ethical issues raised by BCI demand special attention.

As we wrestle with how to address these privacy and security issues, there are two features of current P300 technology that will buy us time.

First, most commercial devices available use dry electrodes, which rely solely on skin contact to conduct electrical signals. This technology is prone to a low signal-to-noise ratio, meaning that we can extract only relatively basic forms of information from users. The brain signals we record are known to be highly variable (even for the same person) due to things like electrode movement and the constantly changing nature of brain signals themselves. Second, electrodes are not always in ideal locations to record.

All together, this inherent lack of reliability means that BCI devices are not nearly as ubiquitous today as they may be in the future. As electrode hardware and signal processing continue to improve, it will be easier to continuously use devices like these, and make it easier to extract personal information from an unknowing individual as well. The safest advice would be to not use these devices at all.

The goal should be that the ethical standards and the technology will mature together to ensure future BCI users are confident their privacy is being protected as they use these kinds of devices. Its a rare opportunity for scientists, engineers, ethicists and eventually regulators to work together to create even better products than were originally dreamed of in science fiction.

Eran Klein, Adjunct Assistant Professor of Neurology at Oregon Health and Sciences University and Affiliate Assistant Professor of Philosophy, University of Washington and Katherine Pratt, Ph.D. Student in Electrical Engineering, University of Washington

This article was originally published on The Conversation. Read the original article.

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Helping or Hacking? Engineers, Ethicists Must Work Together on Brain-Computer Interface Technology - Government Technology

The Fisheries Monitoring competition was one of the biggest Kaggle competitions. According to The Nature Conservancy, which initiated this competition, illegal, unreported and unregulated fishing practices are threatening marine ecosystems, global seafood supplies and local livelihoods. By using computer technology to aid in monitoring fisheries, human capital can be re-allocated to management and enforcement, helping local, regional and global partners preserve the integrity and viability of these fisheries today and into the future.

2,293 teams submitted algorithms for the identification of fish and other marine species from video streams. Competing solutions were evaluated based on an unseen test set that resembles a real-life scenario.

The Neurotechnology employees, who entered the competition independently under the team name "Towards Robust-Optimal Learning of Learning," used state-of-the-art deep neural networks to solve the problem and provide the best overall solution in the competition. The winning team is comprised of Gediminas Peksys, Ignas Namajunas and Jonas Bialopetravicius, all of whom work on Neurotechnology's AI development team, which designs and delivers a range of products and services based on deep neural networks, including computer vision and object recognition.

"This was one of the first Kaggle competitions that was comprised of two stages, which means that models developed during the first stage were frozen and evaluated on unseen data that was made available during the second stage," said Gediminas Peksys from the Towards Robust Optimal Learning of Learning team. "In such a setting, it is very easy for a team's models to overfit the data by using too many trainable parameters. We were able to utilize our team's experience using deep neural networks to come up with a robust model that performed a lot closer to the original estimate from stage one and generalized in a predictable manner on unseen data."

"We congratulate our employees who won this difficult competition," said Dr. Algimantas Malickas, owner of Neurotechnology. "These individuals along with many other excellent employees working on our client projects demonstrate the qualifications of our Neurotechnology staff and their ability to solve the most complex pattern recognition and neural network training problems."

About Neurotechnology

Neurotechnology is a developer of high-precision algorithms and software based on deep neural network (DNN) and other AI-related technologies. The company offers a range of products for biometric fingerprint, face, iris, palmprint and voice identification as well as AI, computer vision, object recognition and robotics. Drawing from years of academic research in the fields of neuroinformatics, image processing and pattern recognition, Neurotechnology was founded in 1990 in Vilnius, Lithuania and released its first fingerprint identification system in 1991. Since that time the company has released more than 130 products and version upgrades. More than 3000 system integrators, security companies and hardware providers integrate Neurotechnology's algorithms into their products, with millions of customer installations worldwide. Neurotechnology's algorithms also achieved top results in independent technology evaluations including NIST MINEX and IREX.

Media ContactJennifer Allen Newton Bluehouse Consulting Group, Inc. +1-503-805-7540 jennifer (at) bluehousecg (dot) com

To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/neurotechnology-researchers-win-kaggle-competition-with-deep-neural-network-solution-for-the-nature-conservancy-fisheries-monitoring-300473515.html

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Neurotechnology Researchers Win Kaggle Competition with Deep Neural Network Solution for The Nature ... - PR Newswire (press release)

Posted on June 14, 2017

Neurotechnology researchers have won first place in a competition designed to find AI solutions for fisheries monitoring.

The competition was organized by Kaggle, an online crowdsourcing platform aimed at the tech and research communities. Organized by The Nature Conservancy and aimed ultimately at applying sophisticated computer vision technology to the fight to protect global fisheries from overfishing and other threats, the Nature Conservancy Fisheries Monitoring competition essentially asked participants to develop algorithms that could automatically detect and identify different species of fish and other marine life.

A group composed entirely of members of Neurotechnologys AI development team, working under the team name TROLL (Towards Robust Optimal Learning of Learning), beat 2,292 other teams to take the companys $50,000 first prize with their algorithm solution.

Its extracurricular as far as Neurotechnologys business goes, but the Kaggle win highlights the talent at work in the company, which recently announced a new version of its MegaMatcher Accelerator platform, which Neurotechnology says is now the fastest biometric engine in the world.

June 14, 2017 by Alex Perala

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Neurotechnology Wins Fisheries-Focused Computer Vision ... - findBIOMETRICS

A Hardware Update for the Human Brain

The Wall Street Journal wrote an interesting article about upcoming business ventures in neurotechnology. Neurotechnology as some of you may know is the merging of technology, neuroscience, and neurology. Quite a few techies have a more vested interest in the field including the likes of Elon Musk and Bryan Johnson, not the former singer of AC/DC. As of now most neurotech companies are spending a majority of the research in medical applications and are hoping to chase the potential multi-billion dollar market. With my own hearing seemingly on the decline, I can sleep soundly knowing that potentially I won't have to have some hulking piece of equipment on my hear, but inside my skin.

To see how far we have to go, you need only look at attempts to use a wireless implant to reconnect a monkey's brain and limb after the animal's spinal cord has been severed. In an experiment conducted at the Swiss Federal Institute of Technology, scientists used an implant to create a wireless connection between a monkey's brain and a battery-powered stimulator in its paralyzed leg, allowing the monkey to walk again.

"I consider this to be the most important thing we could be working on in the human race,"Johnson says. He's convinced that cognitive enhancement from neurotech will unlock radical progress in every conceivable field. "The brain is the master tool," he says. "Everything else is downstream: health, climate science, governance, education, love-everything."

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A Hardware Update for the Human Brain - HardOCP

MegaMatcher Accelerator Extreme edition includes enhanced functionality and reliability compared to the previous MegaMatcher Accelerator version, with more fingerprint, face and eye iris capabilities and significantly faster speeds. It provides bigger capacity, handling up to 160 million fingerprints, up to 40 million faces and up to 200 million eye iris templates on single server.

Neurotechnology is offering a simple upgrade path into the Extreme edition from other MegaMatcher Accelerator editions for the existing customers.

MegaMatcher Accelerator is available through Neurotechnology or from distributors worldwide. For more information and trial version, go to: http://www.neurotechnology.com.

About Neurotechnology

Neurotechnology is a developer of high-precision algorithms and software based on deep neural network (DNN) and other AI-related technologies. The company offers a range of products for biometric fingerprint, face, iris, palmprint and voice identification as well as AI, computer vision, object recognition and robotics. Drawing from years of academic research in the fields of neuroinformatics, image processing and pattern recognition, Neurotechnology was founded in 1990 in Vilnius, Lithuania and released its first fingerprint identification system in 1991. Since that time the company has released more than 130 products and version upgrades. More than 3000 system integrators, security companies and hardware providers integrate Neurotechnology's algorithms into their products, with millions of customer installations worldwide. Neurotechnology's algorithms also received top results in independent technology evaluations such as NIST MINEX and IREX.

Media ContactJennifer Allen Newton Bluehouse Consulting Group, Inc. +1-503-805-7540 jennifer (at) bluehousecg (dot) com

To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/neurotechnology-releases-megamatcher-accelerator-extreme-the-fastest-biometric-engine-in-the-world-300472075.html

SOURCE Neurotechnology

http://www.neurotechnology.com

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Neurotechnology Releases MegaMatcher Accelerator Extreme, the Fastest Biometric Engine in the World - PR Newswire (press release)

Eran Klein, University of Washington and Katherine Pratt, University of Washington

(THE CONVERSATION) In the 1995 film Batman Forever, the Riddler used 3-D television to secretly access viewers most personal thoughts in his hunt for Batmans true identity. By 2011, the metrics company Nielsen had acquired Neurofocus and had created a consumer neuroscience division that uses integrated conscious and unconscious data to track customer decision-making habits. What was once a nefarious scheme in a Hollywood blockbuster seems poised to become a reality.

Recent announcements by Elon Muskand Facebook about brain-computer interface (BCI) technology are just the latest headlines in an ongoing science-fiction-becomes-reality story.

BCIs use brain signals to control objects in the outside world. Theyre a potentially world-changing innovation imagine being paralyzed but able to reach for something with a prosthetic arm just by thinking about it. But the revolutionary technology also raises concerns. Here at the University of Washingtons Center for Sensorimotor Neural Engineering (CSNE) we and our colleagues are researching BCI technology and a crucial part of that includes working on issues such as neuroethics and neural security. Ethicists and engineers are working together to understand and quantify risks and develop ways to protect the public now.

All BCI technology relies on being able to collect information from a brain that a device can then use or act on in some way. There are numerous places from which signals can be recorded, as well as infinite ways the data can be analyzed, so there are many possibilities for how a BCI can be used.

Some BCI researchers zero in on one particular kind of regularly occurring brain signal that alerts us to important changes in our environment. Neuroscientists call these signals event-related potentials. In the lab, they help us identify a reaction to a stimulus.

In particular, we capitalize on one of these specific signals, called the P300. Its a positive peak of electricity that occurs toward the back of the head about 300 milliseconds after the stimulus is shown. The P300 alerts the rest of your brain to an oddball that stands out from the rest of whats around you.

For example, you dont stop and stare at each persons face when youre searching for your friend at the park. Instead, if we were recording your brain signals as you scanned the crowd, there would be a detectable P300 response when you saw someone who could be your friend. The P300 carries an unconscious message alerting you to something important that deserves attention. These signals are part of a still unknown brain pathway that aids in detection and focusing attention.

P300s reliably occur any time you notice something rare or disjointed, like when you find the shirt you were looking for in your closet or your car in a parking lot. Researchers can use the P300 in an experimental setting to determine what is important or relevant to you. Thats led to the creation of devices like spellers that allow paralyzed individuals to type using their thoughts, one character at a time.

It also can be used to determine what you know, in whats called a guilty knowledge test. In the lab, subjects are asked to choose an item to steal or hide, and are then shown many images repeatedly of both unrelated and related items. For instance, subjects choose between a watch and a necklace, and are then shown typical items from a jewelry box; a P300 appears when the subject is presented with the image of the item he took.

Everyones P300 is unique. In order to know what theyre looking for, researchers need training data. These are previously obtained brain signal recordings that researchers are confident contain P300s; theyre then used to calibrate the system. Since the test measures an unconscious neural signal that you dont even know you have, can you fool it? Maybe, if you know that youre being probed and what the stimuli are.

Techniques like these are still considered unreliable and unproven, and thus U.S. courts have resisted admitting P300 data as evidence.

Imagine that instead of using a P300 signal to solve the mystery of a stolen item in the lab, someone used this technology to extract information about what month you were born or which bank you use without your telling them. Our research group has collected data suggesting this is possible. Just using an individuals brain activity specifically, their P300 response we could determine a subjects preferences for things like favorite coffee brand or favorite sports.

But we could do it only when subject-specific training data were available. What if we could figure out someones preferences without previous knowledge of their brain signal patterns? Without the need for training, users could simply put on a device and go, skipping the step of loading a personal training profile or spending time in calibration. Research on trained and untrained devices is the subject of continuing experiments at the University of Washingtonand elsewhere.

Its when the technology is able to read someones mind who isnt actively cooperating that ethical issues become particularly pressing. After all, we willingly trade bits of our privacy all the time when we open our mouths to have conversations or use GPS devices that allow companies to collect data about us. But in these cases we consent to sharing whats in our minds. The difference with next-generation P300 technology under development is that the protection consent gives us may get bypassed altogether.

What if its possible to decode what youre thinking or planning without you even knowing? Will you feel violated? Will you feel a loss of control? Privacy implications may be wide-ranging. Maybe advertisers could know your preferred brands and send you personalized ads which may be convenient or creepy. Or maybe malicious entities could determine where you bank and your accounts PIN which would be alarming.

The potential ability to determine individuals preferences and personal information using their own brain signals has spawned a number of difficult but pressing questions: Should we be able to keep our neural signals private? That is, should neural security be a human right? How do we adequately protect and store all the neural data being recorded for research, and soon for leisure? How do consumers know if any protective or anonymization measures are being made with their neural data? As of now, neural data collected for commercial uses are not subject to the same legal protections covering biomedical research or health care. Should neural data be treated differently?

These are the kinds of conundrums that are best addressed by neural engineers and ethicists working together. Putting ethicists in labs alongside engineers as we have done at the CSNE is one way to ensure that privacy and security risks of neurotechnology, as well as other ethically important issues, are an active part of the research process instead of an afterthought. For instance, Tim Brown, an ethicist at the CSNE, is housed within a neural engineering research lab, allowing him to have daily conversations with researchers about ethical concerns. Hes also easily able to interact with and, in fact, interview research subjects about their ethical concerns about brain research.

There are important ethical and legal lessons to be drawn about technology and privacy from other areas, such as genetics and neuromarketing. But there seems to be something important and different about reading neural data. Theyre more intimately connected to the mind and who we take ourselves to be. As such, ethical issues raised by BCI demand special attention.

As we wrestle with how to address these privacy and security issues, there are two features of current P300 technology that will buy us time.

First, most commercial devices available use dry electrodes, which rely solely on skin contact to conduct electrical signals. This technology is prone to a low signal-to-noise ratio, meaning that we can extract only relatively basic forms of information from users. The brain signals we record are known to be highly variable (even for the same person) due to things like electrode movement and the constantly changing nature of brain signals themselves. Second, electrodes are not always in ideal locations to record.

All together, this inherent lack of reliability means that BCI devices are not nearly as ubiquitous today as they may be in the future. As electrode hardware and signal processing continue to improve, it will be easier to continuously use devices like these, and make it easier to extract personal information from an unknowing individual as well. The safest advice would be to not use these devices at all.

The goal should be that the ethical standards and the technology will mature together to ensure future BCI users are confident their privacy is being protected as they use these kinds of devices. Its a rare opportunity for scientists, engineers, ethicists and eventually regulators to work together to create even better products than were originally dreamed of in science fiction.

This article was originally published on The Conversation. Read the original article here: http://theconversation.com/helping-or-hacking-engineers-and-ethicists-must-work-together-on-brain-computer-interface-technology-77759.

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Helping or hacking? Engineers and ethicists must work together on brain-computer interface technology - San Francisco Chronicle