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Sample records for brain machine interfaces

  1. Brain-machine and brain-computer interfaces.

    Science.gov (United States)

    Friehs, Gerhard M; Zerris, Vasilios A; Ojakangas, Catherine L; Fellows, Mathew R; Donoghue, John P

    2004-11-01

    The idea of connecting the human brain to a computer or machine directly is not novel and its potential has been explored in science fiction. With the rapid advances in the areas of information technology, miniaturization and neurosciences there has been a surge of interest in turning fiction into reality. In this paper the authors review the current state-of-the-art of brain-computer and brain-machine interfaces including neuroprostheses. The general principles and requirements to produce a successful connection between human and artificial intelligence are outlined and the authors' preliminary experience with a prototype brain-computer interface is reported.

  2. Intention concepts and brain-machine interfacing

    Directory of Open Access Journals (Sweden)

    Franziska eThinnes-Elker

    2012-11-01

    Full Text Available Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs that are currently being developed to restore speech and motor control in paralyzed subjects. Such BMI devices record the brain activity of the agent, interpret (‘decode’ the agent’s intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent’s intentions from neural signals in practical BMI applications.

  3. Intention concepts and brain-machine interfacing.

    Science.gov (United States)

    Thinnes-Elker, Franziska; Iljina, Olga; Apostolides, John Kyle; Kraemer, Felicitas; Schulze-Bonhage, Andreas; Aertsen, Ad; Ball, Tonio

    2012-01-01

    Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs) that are currently being developed to restore speech and motor control in paralyzed patients. Such BMI devices record the brain activity of the agent, interpret ("decode") the agent's intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent's intentions from neural signals in practical BMI applications.

  4. Future developments in brain-machine interface research.

    Science.gov (United States)

    Lebedev, Mikhail A; Tate, Andrew J; Hanson, Timothy L; Li, Zheng; O'Doherty, Joseph E; Winans, Jesse A; Ifft, Peter J; Zhuang, Katie Z; Fitzsimmons, Nathan A; Schwarz, David A; Fuller, Andrew M; An, Je Hi; Nicolelis, Miguel A L

    2011-01-01

    Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

  5. Future developments in brain-machine interface research

    Directory of Open Access Journals (Sweden)

    Mikhail A. Lebedev

    2011-01-01

    Full Text Available Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL Center for Neuroprosthetics, to bring this new technology to clinical fruition.

  6. Neuro-robotics from brain machine interfaces to rehabilitation robotics

    CERN Document Server

    Artemiadis

    2014-01-01

    Neuro-robotics is one of the most multidisciplinary fields of the last decades, fusing information and knowledge from neuroscience, engineering and computer science. This book focuses on the results from the strategic alliance between Neuroscience and Robotics that help the scientific community to better understand the brain as well as design robotic devices and algorithms for interfacing humans and robots. The first part of the book introduces the idea of neuro-robotics, by presenting state-of-the-art bio-inspired devices. The second part of the book focuses on human-machine interfaces for pe

  7. What turns assistive into restorative brain-machine interfaces?

    Directory of Open Access Journals (Sweden)

    Alireza Gharabaghi

    2016-10-01

    Full Text Available Brain-machine interfaces (BMI may support motor impaired patients during activities of daily living by controlling external devices such as prostheses (assistive BMI. Moreover, BMIs are applied in conjunction with robotic orthoses for rehabilitation of lost motor function via neurofeedback training (restorative BMI. Using assistive BMI in a rehabilitation context does not automatically turn them into restorative devices. This perspective article suggests key features of restorative BMI and provides the supporting evidence:In summary, BMI may be referred to as restorative tools when demonstrating subsequently (i operant learning and progressive evolution of specific brain states/dynamics, (ii correlated modulations of functional networks related to the therapeutic goal, (iii subsequent improvement in a specific task, and (iv an explicit correlation between the modulated brain dynamics and the achieved behavioral gains. Such findings would provide the rationale for translating BMI-based interventions into clinical settings for reinforcement learning and motor rehabilitation following stroke.

  8. What limits the performance of current invasive Brain Machine Interfaces?

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    Gytis eBaranauskas

    2014-04-01

    Full Text Available The concept of a brain-machine interface (BMI or a computer-brain interface is simple: BMI creates a communication pathway for a direct control by brain of an external device. In reality BMIs are very complex devices and only recently the increase in computing power of microprocessors enabled a boom in BMI research that continues almost unabated to this date, the high point being the insertion of electrode arrays into the brains of 5 human patients in a clinical trial run by Cyberkinetics with few other clinical tests still in progress. Meanwhile several EEG-based BMI devices (non-invasive BMIs were launched commercially. Modern electronics and dry electrode technology made possible to drive the cost of some of these devices below few hundred dollars. However, the initial excitement of the direct control by brain waves of a computer or other equipment is dampened by large efforts required for learning, high error rates and slow response speed. All these problems are directly related to low information transfer rates typical for such EEG-based BMIs. In invasive BMIs employing multiple electrodes inserted into the brain one may expect much higher information transfer rates than in EEG-based BMIs because, in theory, each electrode provides an independent information channel. However, although invasive BMIs require more expensive equipment and have ethical problems related to the need to insert electrodes in the live brain, such financial and ethical costs are often not offset by a dramatic improvement in the information transfer rate. Thus the main topic of this review is why in invasive BMIs an apparently much larger information content obtained with multiple extracellular electrodes does not translate into much higher rates of information transfer? This paper explores possible answers to this question by concluding that more research on what movement parameters are encoded by neurons in motor cortex is needed before we can enjoy the next

  9. Cortical and subcortical mechanisms of brain-machine interfaces.

    Science.gov (United States)

    Marchesotti, Silvia; Martuzzi, Roberto; Schurger, Aaron; Blefari, Maria Laura; Del Millán, José R; Bleuler, Hannes; Blanke, Olaf

    2017-06-01

    Technical advances in the field of Brain-Machine Interfaces (BMIs) enable users to control a variety of external devices such as robotic arms, wheelchairs, virtual entities and communication systems through the decoding of brain signals in real time. Most BMI systems sample activity from restricted brain regions, typically the motor and premotor cortex, with limited spatial resolution. Despite the growing number of applications, the cortical and subcortical systems involved in BMI control are currently unknown at the whole-brain level. Here, we provide a comprehensive and detailed report of the areas active during on-line BMI control. We recorded functional magnetic resonance imaging (fMRI) data while participants controlled an EEG-based BMI inside the scanner. We identified the regions activated during BMI control and how they overlap with those involved in motor imagery (without any BMI control). In addition, we investigated which regions reflect the subjective sense of controlling a BMI, the sense of agency for BMI-actions. Our data revealed an extended cortical-subcortical network involved in operating a motor-imagery BMI. This includes not only sensorimotor regions but also the posterior parietal cortex, the insula and the lateral occipital cortex. Interestingly, the basal ganglia and the anterior cingulate cortex were involved in the subjective sense of controlling the BMI. These results inform basic neuroscience by showing that the mechanisms of BMI control extend beyond sensorimotor cortices. This knowledge may be useful for the development of BMIs that offer a more natural and embodied feeling of control for the user. Hum Brain Mapp 38:2971-2989, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  10. Brain-Machine Interface Enables Bimanual Arm Movements in Monkeys

    Science.gov (United States)

    Ifft, Peter J.; Shokur, Solaiman; Li, Zheng; Lebedev, Mikhail A.; Nicolelis, Miguel A. L.

    2014-01-01

    Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to severely paralyzed patients. However, previous BMIs enabled only single arm functionality, and control of bimanual movements was a major challenge. Here, we developed and tested a bimanual BMI that enabled rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extracellular activity of 374–497 neurons recorded from several frontal and parietal cortical areas of both cerebral hemispheres. Cortical activity was transformed into movements of the two arms with a decoding algorithm called a 5th order unscented Kalman filter (UKF). The UKF is well-suited for BMI decoding because it accounts for both characteristics of reaching movements and their representation by cortical neurons. The UKF was trained either during a manual task performed with two joysticks or by having the monkeys passively observe the movements of avatar arms. Most cortical neurons changed their modulation patterns when both arms were engaged simultaneously. Representing the two arms jointly in a single UKF decoder resulted in improved decoding performance compared with using separate decoders for each arm. As the animals’ performance in bimanual BMI control improved over time, we observed widespread plasticity in frontal and parietal cortical areas. Neuronal representation of the avatar and reach targets was enhanced with learning, whereas pairwise correlations between neurons initially increased and then decreased. These results suggest that cortical networks may assimilate the two avatar arms through BMI control. PMID:24197735

  11. Brain Machine Interfaces : technology status, applications and the way to the future

    NARCIS (Netherlands)

    Erp, J.B.F. van; Duistermaat, M.; Philippens, I.H.C.H.M.; Veen, H.A.H.C. van; Werkhoven, P.J.

    2006-01-01

    Brain Machine Interfaces (BMIs) enable direct communication between the brain or nervous system and a machine without involving the sensory-motor system. BMIs are an embryonic technology and remarkable accomplishments have recently been reported. BMIs have a high potential and possibly an enormous

  12. Brain Machine Interfaces for Robotic Control in Space Applications, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — This SBIR will study the application of a brain machine interface (BMI) to enable crew to remotely operate and monitor robots from inside a flight vehicle, habitat...

  13. Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control

    Science.gov (United States)

    Oh, Sechang; Kumar, Prashanth S.; Kwon, Hyeokjun; Varadan, Vijay K.

    2012-04-01

    A brain-machine interface (BMI) links a user's brain activity directly to an external device. It enables a person to control devices using only thought. Hence, it has gained significant interest in the design of assistive devices and systems for people with disabilities. In addition, BMI has also been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, fire fighting etc. There are mainly two types of BMI based on the measurement method of brain activity; invasive and non-invasive. Invasive BMI can provide pristine signals but it is expensive and surgery may lead to undesirable side effects. Recent advances in non-invasive BMI have opened the possibility of generating robust control signals from noisy brain activity signals like EEG and EOG. A practical implementation of a non-invasive BMI such as robot control requires: acquisition of brain signals with a robust wearable unit, noise filtering and signal processing, identification and extraction of relevant brain wave features and finally, an algorithm to determine control signals based on the wave features. In this work, we developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot.

  14. Independent Mobility Achieved through a Wireless Brain-Machine Interface.

    Directory of Open Access Journals (Sweden)

    Camilo Libedinsky

    Full Text Available Individuals with tetraplegia lack independent mobility, making them highly dependent on others to move from one place to another. Here, we describe how two macaques were able to use a wireless integrated system to control a robotic platform, over which they were sitting, to achieve independent mobility using the neuronal activity in their motor cortices. The activity of populations of single neurons was recorded using multiple electrode arrays implanted in the arm region of primary motor cortex, and decoded to achieve brain control of the platform. We found that free-running brain control of the platform (which was not equipped with any machine intelligence was fast and accurate, resembling the performance achieved using joystick control. The decoding algorithms can be trained in the absence of joystick movements, as would be required for use by tetraplegic individuals, demonstrating that the non-human primate model is a good pre-clinical model for developing such a cortically-controlled movement prosthetic. Interestingly, we found that the response properties of some neurons differed greatly depending on the mode of control (joystick or brain control, suggesting different roles for these neurons in encoding movement intention and movement execution. These results demonstrate that independent mobility can be achieved without first training on prescribed motor movements, opening the door for the implementation of this technology in persons with tetraplegia.

  15. The PennBMBI: Design of a General Purpose Wireless Brain-Machine-Brain Interface System.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Subei, Basheer; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

    2015-04-01

    In this paper, a general purpose wireless Brain-Machine-Brain Interface (BMBI) system is presented. The system integrates four battery-powered wireless devices for the implementation of a closed-loop sensorimotor neural interface, including a neural signal analyzer, a neural stimulator, a body-area sensor node and a graphic user interface implemented on the PC end. The neural signal analyzer features a four channel analog front-end with configurable bandpass filter, gain stage, digitization resolution, and sampling rate. The target frequency band is configurable from EEG to single unit activity. A noise floor of 4.69 μVrms is achieved over a bandwidth from 0.05 Hz to 6 kHz. Digital filtering, neural feature extraction, spike detection, sensing-stimulating modulation, and compressed sensing measurement are realized in a central processing unit integrated in the analyzer. A flash memory card is also integrated in the analyzer. A 2-channel neural stimulator with a compliance voltage up to ± 12 V is included. The stimulator is capable of delivering unipolar or bipolar, charge-balanced current pulses with programmable pulse shape, amplitude, width, pulse train frequency and latency. A multi-functional sensor node, including an accelerometer, a temperature sensor, a flexiforce sensor and a general sensor extension port has been designed. A computer interface is designed to monitor, control and configure all aforementioned devices via a wireless link, according to a custom designed communication protocol. Wireless closed-loop operation between the sensory devices, neural stimulator, and neural signal analyzer can be configured. The proposed system was designed to link two sites in the brain, bridging the brain and external hardware, as well as creating new sensory and motor pathways for clinical practice. Bench test and in vivo experiments are performed to verify the functions and performances of the system.

  16. European public deliberation on brain machine interface technology: five convergence seminars.

    Science.gov (United States)

    Jebari, Karim; Hansson, Sven-Ove

    2013-09-01

    We present a novel procedure to engage the public in ethical deliberations on the potential impacts of brain machine interface technology. We call this procedure a convergence seminar, a form of scenario-based group discussion that is founded on the idea of hypothetical retrospection. The theoretical background of this procedure and the results of five seminars are presented.

  17. Brain versus Machine Control.

    Directory of Open Access Journals (Sweden)

    Jose M Carmena

    2004-12-01

    Full Text Available Dr. Octopus, the villain of the movie "Spiderman 2", is a fusion of man and machine. Neuroscientist Jose Carmena examines the facts behind this fictional account of a brain- machine interface

  18. CONTROL OF A ROBOTIC ARM THROUGH A BRAIN MACHINE INTERFACE WITH MUTUAL LEARNING

    OpenAIRE

    ALEXANDRE ORMIGA GALVAO BARBOSA

    2010-01-01

    Esse trabalho apresenta o desenvolvimento de uma interface cérebro-máquina (Brain Machine Interface - BMI) como um meio alternativo de comunicação para uso na robótica. O trabalho engloba o projeto e construção de um eletroencefalógrafo (EEG), assim como o desenvolvimento de todos os algoritmos computacionais e demais técnicas necessárias para o reconhecimento de atividades mentais. A interface cérebro-máquina desenvolvida é utilizada para comandar os movimentos de um manipulador robótico MA2...

  19. Habit learning and brain-machine interfaces (BMI): a tribute to Valentino Braitenberg's "Vehicles".

    Science.gov (United States)

    Birbaumer, Niels; Hummel, Friedhelm C

    2014-10-01

    Brain-Machine Interfaces (BMI) allow manipulation of external devices and computers directly with brain activity without involvement of overt motor actions. The neurophysiological principles of such robotic brain devices and BMIs follow Hebbian learning rules as described and realized by Valentino Braitenberg in his book "Vehicles," in the concept of a "thought pump" residing in subcortical basal ganglia structures. We describe here the application of BMIs for brain communication in totally locked-in patients and argue that the thought pump may extinguish-at least partially-in those people because of extinction of instrumentally learned cognitive responses and brain responses. We show that Pavlovian semantic conditioning may allow brain communication even in the completely paralyzed who does not show response-effect contingencies. Principles of skill learning and habit acquisition as formulated by Braitenberg are the building blocks of BMIs and neuroprostheses.

  20. Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke

    OpenAIRE

    McConnell, Alistair C; Moioli, Renan C; Brasil, Fabricio L; Vallejo, Marta; Corne, David W; Vargas, Patricia A; Stokes, Adam A

    2017-01-01

    OBJECTIVE: To review the state of the art of robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain-machine interfaces. Each patient has a unique clinical history and, in response to personalized treatment needs, research into individualized and at-home treatment options has expanded rapidly in recent years. This has resulted in the development of many devices and design strategies for use in stroke rehabilitation.METHODS: The development progression of ro...

  1. Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke.

    Science.gov (United States)

    McConnell, Alistair C; Moioli, Renan C; Brasil, Fabricio L; Vallejo, Marta; Corne, David W; Vargas, Patricia A; Stokes, Adam A

    2017-06-28

    To review the state of the art of robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain-machine interfaces. Each patient has a unique clinical history and, in response to personalized treatment needs, research into individualized and at-home treatment options has expanded rapidly in recent years. This has resulted in the development of many devices and design strategies for use in stroke rehabilitation. The development progression of robotic-aided hand physiotherapy devices and brain-machine interface systems is outlined, focussing on those with mechanisms and control strategies designed to improve recovery outcomes of the hand post-stroke. A total of 110 commercial and non-commercial hand and wrist devices, spanning the 2 major core designs: end-effector and exoskeleton are reviewed. The growing body of evidence on the efficacy and relevance of incorporating brain-machine interfaces in stroke rehabilitation is summarized. The challenges involved in integrating robotic rehabilitation into the healthcare system are discussed. This review provides novel insights into the use of robotics in physiotherapy practice, and may help system designers to develop new devices.

  2. Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

    Science.gov (United States)

    Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

    The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

  3. Soft brain-machine interfaces for assistive robotics: A novel control approach.

    Science.gov (United States)

    Schiatti, Lucia; Tessadori, Jacopo; Barresi, Giacinto; Mattos, Leonardo S; Ajoudani, Arash

    2017-07-01

    Robotic systems offer the possibility of improving the life quality of people with severe motor disabilities, enhancing the individual's degree of independence and interaction with the external environment. In this direction, the operator's residual functions must be exploited for the control of the robot movements and the underlying dynamic interaction through intuitive and effective human-robot interfaces. Towards this end, this work aims at exploring the potential of a novel Soft Brain-Machine Interface (BMI), suitable for dynamic execution of remote manipulation tasks for a wide range of patients. The interface is composed of an eye-tracking system, for an intuitive and reliable control of a robotic arm system's trajectories, and a Brain-Computer Interface (BCI) unit, for the control of the robot Cartesian stiffness, which determines the interaction forces between the robot and environment. The latter control is achieved by estimating in real-time a unidimensional index from user's electroencephalographic (EEG) signals, which provides the probability of a neutral or active state. This estimated state is then translated into a stiffness value for the robotic arm, allowing a reliable modulation of the robot's impedance. A preliminary evaluation of this hybrid interface concept provided evidence on the effective execution of tasks with dynamic uncertainties, demonstrating the great potential of this control method in BMI applications for self-service and clinical care.

  4. Toward an autonomous brain machine interface: integrating sensorimotor reward modulation and reinforcement learning.

    Science.gov (United States)

    Marsh, Brandi T; Tarigoppula, Venkata S Aditya; Chen, Chen; Francis, Joseph T

    2015-05-13

    For decades, neurophysiologists have worked on elucidating the function of the cortical sensorimotor control system from the standpoint of kinematics or dynamics. Recently, computational neuroscientists have developed models that can emulate changes seen in the primary motor cortex during learning. However, these simulations rely on the existence of a reward-like signal in the primary sensorimotor cortex. Reward modulation of the primary sensorimotor cortex has yet to be characterized at the level of neural units. Here we demonstrate that single units/multiunits and local field potentials in the primary motor (M1) cortex of nonhuman primates (Macaca radiata) are modulated by reward expectation during reaching movements and that this modulation is present even while subjects passively view cursor motions that are predictive of either reward or nonreward. After establishing this reward modulation, we set out to determine whether we could correctly classify rewarding versus nonrewarding trials, on a moment-to-moment basis. This reward information could then be used in collaboration with reinforcement learning principles toward an autonomous brain-machine interface. The autonomous brain-machine interface would use M1 for both decoding movement intention and extraction of reward expectation information as evaluative feedback, which would then update the decoding algorithm as necessary. In the work presented here, we show that this, in theory, is possible. Copyright © 2015 the authors 0270-6474/15/357374-14$15.00/0.

  5. Human-machine interface based on muscular and brain signals applied to a robotic wheelchair

    International Nuclear Information System (INIS)

    Ferreira, A; Silva, R L; Celeste, W C; Filho, T F Bastos; Filho, M Sarcinelli

    2007-01-01

    This paper presents a Human-Machine Interface (HMI) based on the signals generated by eye blinks or brain activity. The system structure and the signal acquisition and processing are shown. The signals used in this work are either the signal associated to the muscular movement corresponding to an eye blink or the brain signal corresponding to visual information processing. The variance is the feature extracted from such signals in order to detect the intention of the user. The classification is performed by a variance threshold which is experimentally determined for each user during the training stage. The command options, which are going to be sent to the commanded device, are presented to the user in the screen of a PDA (Personal Digital Assistant). In the experiments here reported, a robotic wheelchair is used as the device being commanded

  6. Human-machine interface based on muscular and brain signals applied to a robotic wheelchair

    Energy Technology Data Exchange (ETDEWEB)

    Ferreira, A; Silva, R L; Celeste, W C; Filho, T F Bastos; Filho, M Sarcinelli [Electrical Engineering Department, Federal University of Espirito Santo (UFES), Av. Fernando Ferrari, 514, Vitoria, 29075-910 (Brazil)

    2007-11-15

    This paper presents a Human-Machine Interface (HMI) based on the signals generated by eye blinks or brain activity. The system structure and the signal acquisition and processing are shown. The signals used in this work are either the signal associated to the muscular movement corresponding to an eye blink or the brain signal corresponding to visual information processing. The variance is the feature extracted from such signals in order to detect the intention of the user. The classification is performed by a variance threshold which is experimentally determined for each user during the training stage. The command options, which are going to be sent to the commanded device, are presented to the user in the screen of a PDA (Personal Digital Assistant). In the experiments here reported, a robotic wheelchair is used as the device being commanded.

  7. A bidirectional brain-machine interface featuring a neuromorphic hardware decoder

    Directory of Open Access Journals (Sweden)

    Fabio Boi

    2016-12-01

    Full Text Available Bidirectional brain-machine interfaces (BMIs establish a two-way direct communication link4 between the brain and the external world. A decoder translates recorded neural activity into motor5 commands and an encoder delivers sensory information collected from the environment directly6 to the brain creating a closed-loop system. These two modules are typically integrated in bulky7 external devices. However, the clinical support of patients with severe motor and sensory deficits8 requires compact, low-power, and fully implantable systems that can decode neural signals to9 control external devices. As a first step toward this goal, we developed a modular bidirectional BMI10 setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented11 a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits.12 On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn13 to decode neural signals recorded from the brain into motor outputs controlling the movements14 of an external device. The modularity of the BMI allowed us to tune the individual components15 of the setup without modifying the whole system. In this paper we present the features of16 this modular BMI, and describe how we configured the network of spiking neuron circuits to17 implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm18 that connects bidirectionally the brain of an anesthetized rat with an external object. We show that19 the chip learned the decoding task correctly, allowing the interfaced brain to control the object’s20 trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is21 mature enough for the development of BMI modules that are sufficiently low-power and compact,22 while being highly computationally powerful and adaptive.

  8. A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

    Science.gov (United States)

    Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

    2016-01-01

    Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

  9. On the applicability of brain reading for predictive human-machine interfaces in robotics.

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    Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

    2013-01-01

    The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

  10. On the applicability of brain reading for predictive human-machine interfaces in robotics.

    Directory of Open Access Journals (Sweden)

    Elsa Andrea Kirchner

    Full Text Available The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR, a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

  11. A Symbiotic Brain-Machine Interface through Value-Based Decision Making

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    Mahmoudi, Babak; Sanchez, Justin C.

    2011-01-01

    Background In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC). Methodology The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. Conclusions Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward

  12. A symbiotic brain-machine interface through value-based decision making.

    Directory of Open Access Journals (Sweden)

    Babak Mahmoudi

    Full Text Available BACKGROUND: In the development of Brain Machine Interfaces (BMIs, there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC. METHODOLOGY: The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1 and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. CONCLUSIONS: Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and

  13. Decoder calibration with ultra small current sample set for intracortical brain-machine interface

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    Zhang, Peng; Ma, Xuan; Chen, Luyao; Zhou, Jin; Wang, Changyong; Li, Wei; He, Jiping

    2018-04-01

    Objective. Intracortical brain-machine interfaces (iBMIs) aim to restore efficient communication and movement ability for paralyzed patients. However, frequent recalibration is required for consistency and reliability, and every recalibration will require relatively large most current sample set. The aim in this study is to develop an effective decoder calibration method that can achieve good performance while minimizing recalibration time. Approach. Two rhesus macaques implanted with intracortical microelectrode arrays were trained separately on movement and sensory paradigm. Neural signals were recorded to decode reaching positions or grasping postures. A novel principal component analysis-based domain adaptation (PDA) method was proposed to recalibrate the decoder with only ultra small current sample set by taking advantage of large historical data, and the decoding performance was compared with other three calibration methods for evaluation. Main results. The PDA method closed the gap between historical and current data effectively, and made it possible to take advantage of large historical data for decoder recalibration in current data decoding. Using only ultra small current sample set (five trials of each category), the decoder calibrated using the PDA method could achieve much better and more robust performance in all sessions than using other three calibration methods in both monkeys. Significance. (1) By this study, transfer learning theory was brought into iBMIs decoder calibration for the first time. (2) Different from most transfer learning studies, the target data in this study were ultra small sample set and were transferred to the source data. (3) By taking advantage of historical data, the PDA method was demonstrated to be effective in reducing recalibration time for both movement paradigm and sensory paradigm, indicating a viable generalization. By reducing the demand for large current training data, this new method may facilitate the application

  14. Emergent coordination underlying learning to reach to grasp with a brain-machine interface.

    Science.gov (United States)

    Vaidya, Mukta; Balasubramanian, Karthikeyan; Southerland, Joshua; Badreldin, Islam; Eleryan, Ahmed; Shattuck, Kelsey; Gururangan, Suchin; Slutzky, Marc; Osborne, Leslie; Fagg, Andrew; Oweiss, Karim; Hatsopoulos, Nicholas G

    2018-04-01

    The development of coordinated reach-to-grasp movement has been well studied in infants and children. However, the role of motor cortex during this development is unclear because it is difficult to study in humans. We took the approach of using a brain-machine interface (BMI) paradigm in rhesus macaques with prior therapeutic amputations to examine the emergence of novel, coordinated reach to grasp. Previous research has shown that after amputation, the cortical area previously involved in the control of the lost limb undergoes reorganization, but prior BMI work has largely relied on finding neurons that already encode specific movement-related information. In this study, we taught macaques to cortically control a robotic arm and hand through operant conditioning, using neurons that were not explicitly reach or grasp related. Over the course of training, stereotypical patterns emerged and stabilized in the cross-covariance between the reaching and grasping velocity profiles, between pairs of neurons involved in controlling reach and grasp, and to a comparable, but lesser, extent between other stable neurons in the network. In fact, we found evidence of this structured coordination between pairs composed of all combinations of neurons decoding reach or grasp and other stable neurons in the network. The degree of and participation in coordination was highly correlated across all pair types. Our approach provides a unique model for studying the development of novel, coordinated reach-to-grasp movement at the behavioral and cortical levels. NEW & NOTEWORTHY Given that motor cortex undergoes reorganization after amputation, our work focuses on training nonhuman primates with chronic amputations to use neurons that are not reach or grasp related to control a robotic arm to reach to grasp through the use of operant conditioning, mimicking early development. We studied the development of a novel, coordinated behavior at the behavioral and cortical level, and the neural

  15. Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general.

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    Zander, Thorsten O; Kothe, Christian

    2011-04-01

    Cognitive monitoring is an approach utilizing realtime brain signal decoding (RBSD) for gaining information on the ongoing cognitive user state. In recent decades this approach has brought valuable insight into the cognition of an interacting human. Automated RBSD can be used to set up a brain-computer interface (BCI) providing a novel input modality for technical systems solely based on brain activity. In BCIs the user usually sends voluntary and directed commands to control the connected computer system or to communicate through it. In this paper we propose an extension of this approach by fusing BCI technology with cognitive monitoring, providing valuable information about the users' intentions, situational interpretations and emotional states to the technical system. We call this approach passive BCI. In the following we give an overview of studies which utilize passive BCI, as well as other novel types of applications resulting from BCI technology. We especially focus on applications for healthy users, and the specific requirements and demands of this user group. Since the presented approach of combining cognitive monitoring with BCI technology is very similar to the concept of BCIs itself we propose a unifying categorization of BCI-based applications, including the novel approach of passive BCI.

  16. sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

    Science.gov (United States)

    Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

    2011-10-01

    In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

  17. Neural control of finger movement via intracortical brain-machine interface

    Science.gov (United States)

    Irwin, Z. T.; Schroeder, K. E.; Vu, P. P.; Bullard, A. J.; Tat, D. M.; Nu, C. S.; Vaskov, A.; Nason, S. R.; Thompson, D. E.; Bentley, J. N.; Patil, P. G.; Chestek, C. A.

    2017-12-01

    Objective. Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s-1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe

  18. BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.

    Science.gov (United States)

    Zhao, Ming; Rattanatamrong, Prapaporn; DiGiovanna, Jack; Mahmoudi, Babak; Figueiredo, Renato J; Sanchez, Justin C; Príncipe, José C; Fortes, José A B

    2008-01-01

    Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.

  19. Craniux: a LabVIEW-based modular software framework for brain-machine interface research.

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    Degenhart, Alan D; Kelly, John W; Ashmore, Robin C; Collinger, Jennifer L; Tyler-Kabara, Elizabeth C; Weber, Douglas J; Wang, Wei

    2011-01-01

    This paper presents "Craniux," an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development.

  20. Craniux: A LabVIEW-Based Modular Software Framework for Brain-Machine Interface Research

    Directory of Open Access Journals (Sweden)

    Alan D. Degenhart

    2011-01-01

    Full Text Available This paper presents “Craniux,” an open-access, open-source software framework for brain-machine interface (BMI research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development.

  1. An online brain-machine interface using decoding of movement direction from the human electrocorticogram

    Science.gov (United States)

    Milekovic, Tomislav; Fischer, Jörg; Pistohl, Tobias; Ruescher, Johanna; Schulze-Bonhage, Andreas; Aertsen, Ad; Rickert, Jörn; Ball, Tonio; Mehring, Carsten

    2012-08-01

    A brain-machine interface (BMI) can be used to control movements of an artificial effector, e.g. movements of an arm prosthesis, by motor cortical signals that control the equivalent movements of the corresponding body part, e.g. arm movements. This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single neurons. We show that the same approach can be realized using brain activity measured directly from the surface of the human cortex using electrocorticography (ECoG). Five subjects, implanted with ECoG implants for the purpose of epilepsy assessment, took part in our study. Subjects used directionally dependent ECoG signals, recorded during active movements of a single arm, to control a computer cursor in one out of two directions. Significant BMI control was achieved in four out of five subjects with correct directional decoding in 69%-86% of the trials (75% on average). Our results demonstrate the feasibility of an online BMI using decoding of movement direction from human ECoG signals. Thus, to achieve such BMIs, ECoG signals might be used in conjunction with or as an alternative to intracortical neural signals.

  2. Brain machine interfaces combining microelectrode arrays with nanostructured optical biochemical sensors

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    Hajj-Hassan, Mohamad; Gonzalez, Timothy; Ghafer-Zadeh, Ebrahim; Chodavarapu, Vamsy; Musallam, Sam; Andrews, Mark

    2009-02-01

    Neural microelectrodes are an important component of neural prosthetic systems which assist paralyzed patients by allowing them to operate computers or robots using their neural activity. These microelectrodes are also used in clinical settings to localize the locus of seizure initiation in epilepsy or to stimulate sub-cortical structures in patients with Parkinson's disease. In neural prosthetic systems, implanted microelectrodes record the electrical potential generated by specific thoughts and relay the signals to algorithms trained to interpret these thoughts. In this paper, we describe novel elongated multi-site neural electrodes that can record electrical signals and specific neural biomarkers and that can reach depths greater than 8mm in the sulcus of non-human primates (monkeys). We hypothesize that additional signals recorded by the multimodal probes will increase the information yield when compared to standard probes that record just electropotentials. We describe integration of optical biochemical sensors with neural microelectrodes. The sensors are made using sol-gel derived xerogel thin films that encapsulate specific biomarker responsive luminophores in their nanostructured pores. The desired neural biomarkers are O2, pH, K+, and Na+ ions. As a prototype, we demonstrate direct-write patterning to create oxygen-responsive xerogel waveguide structures on the neural microelectrodes. The recording of neural biomarkers along with electrical activity could help the development of intelligent and more userfriendly neural prosthesis/brain machine interfaces as well as aid in providing answers to complex brain diseases and disorders.

  3. An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces.

    Science.gov (United States)

    Li, Simin; Li, Jie; Li, Zheng

    2016-01-01

    Brain-machine interfaces (BMIs) seek to connect brains with machines or computers directly, for application in areas such as prosthesis control. For this application, the accuracy of the decoding of movement intentions is crucial. We aim to improve accuracy by designing a better encoding model of primary motor cortical activity during hand movements and combining this with decoder engineering refinements, resulting in a new unscented Kalman filter based decoder, UKF2, which improves upon our previous unscented Kalman filter decoder, UKF1. The new encoding model includes novel acceleration magnitude, position-velocity interaction, and target-cursor-distance features (the decoder does not require target position as input, it is decoded). We add a novel probabilistic velocity threshold to better determine the user's intent to move. We combine these improvements with several other refinements suggested by others in the field. Data from two Rhesus monkeys indicate that the UKF2 generates offline reconstructions of hand movements (mean CC 0.851) significantly more accurately than the UKF1 (0.833) and the popular position-velocity Kalman filter (0.812). The encoding model of the UKF2 could predict the instantaneous firing rate of neurons (mean CC 0.210), given kinematic variables and past spiking, better than the encoding models of these two decoders (UKF1: 0.138, p-v Kalman: 0.098). In closed-loop experiments where each monkey controlled a computer cursor with each decoder in turn, the UKF2 facilitated faster task completion (mean 1.56 s vs. 2.05 s) and higher Fitts's Law bit rate (mean 0.738 bit/s vs. 0.584 bit/s) than the UKF1. These results suggest that the modeling and decoder engineering refinements of the UKF2 improve decoding performance. We believe they can be used to enhance other decoders as well.

  4. Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Miriam eZacksenhouse

    2015-05-01

    Full Text Available Recent experiments with brain-machine-interfaces (BMIs indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

  5. Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

    Science.gov (United States)

    Benyamini, Miri; Zacksenhouse, Miriam

    2015-01-01

    Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

  6. A bidirectional brain-machine interface algorithm that approximates arbitrary force-fields.

    Directory of Open Access Journals (Sweden)

    Alessandro Vato

    Full Text Available We examine bidirectional brain-machine interfaces that control external devices in a closed loop by decoding motor cortical activity to command the device and by encoding the state of the device by delivering electrical stimuli to sensory areas. Although it is possible to design this artificial sensory-motor interaction while maintaining two independent channels of communication, here we propose a rule that closes the loop between flows of sensory and motor information in a way that approximates a desired dynamical policy expressed as a field of forces acting upon the controlled external device. We previously developed a first implementation of this approach based on linear decoding of neural activity recorded from the motor cortex into a set of forces (a force field applied to a point mass, and on encoding of position of the point mass into patterns of electrical stimuli delivered to somatosensory areas. However, this previous algorithm had the limitation that it only worked in situations when the position-to-force map to be implemented is invertible. Here we overcome this limitation by developing a new non-linear form of the bidirectional interface that can approximate a virtually unlimited family of continuous fields. The new algorithm bases both the encoding of position information and the decoding of motor cortical activity on an explicit map between spike trains and the state space of the device computed with Multi-Dimensional-Scaling. We present a detailed computational analysis of the performance of the interface and a validation of its robustness by using synthetic neural responses in a simulated sensory-motor loop.

  7. Augmenting intracortical brain-machine interface with neurally driven error detectors

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    Even-Chen, Nir; Stavisky, Sergey D.; Kao, Jonathan C.; Ryu, Stephen I.; Shenoy, Krishna V.

    2017-12-01

    Objective. Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. Approach. We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. Main results. We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error ‘detect-and-act’ system that attempts to automatically ‘undo’ or ‘prevent’ mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). Significance. Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.

  8. A fast intracortical brain-machine interface with patterned optogenetic feedback.

    Science.gov (United States)

    Abbasi, Aamir; Goueytes, Dorian; Shulz, Daniel E; Ego-Stengel, Valerie; Estebanez, Luc

    2018-04-04

    The development of brain-machine interfaces (BMIs) brings a new perspective to patients with a loss of autonomy. By combining online recordings of brain activity with a decoding algorithm, patients can learn to control a robotic arm in order to perform simple actions. However, in contrast to the vast amounts of somatosensory information channeled by limbs to the brain, current BMIs are devoid of touch and force sensors. Patients must therefore rely solely on vision and audition, which are maladapted to the control of a prosthesis. In contrast, in a healthy limb, somatosensory inputs alone can efficiently guide the handling of a fragile object, or ensure a smooth trajectory. We have developed a BMI in the mouse that includes a rich artificial somatosensory-like cortical feedback. Our setup includes online recordings of the activity of multiple neurons in the whisker primary motor cortex (vM1), and delivers feedback simultaneously via a low-latency, high-refresh rate and spatially structured photo-stimulation of the whisker primary somatosensory cortex (vS1), based on a mapping obtained by intrinsic imaging. We demonstrate the operation of the loop and show that mice can detect the neuronal spiking in vS1 triggered by the photo-stimulations. Finally, we show that the mice can learn a behavioral task relying only on the artificial inputs and outputs of the closed-loop BMI. This is the first motor BMI that includes a short-latency, intracortical, somatosensory-like feedback. It will be a useful platform to discover efficient cortical feedback schemes towards future human BMI applications. Creative Commons Attribution license.

  9. Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation

    Directory of Open Access Journals (Sweden)

    Alireza eGharabaghi

    2014-03-01

    Full Text Available Motor recovery after stroke is an unsolved challenge despite intensive rehabilitation training programs. Brain stimulation techniques have been explored in addition to traditional rehabilitation training to increase the excitability of the stimulated motor cortex. This modulation of cortical excitability augments the response to afferent input during motor exercises, thereby enhancing skilled motor learning by long-term potentiation-like plasticity. Recent approaches examined brain stimulation applied concurrently with voluntary movements to induce more specific use-dependent neural plasticity during motor training for neurorehabilitation. Unfortunately, such approaches are not applicable for the many severely affected stroke patients lacking residual hand function. These patients require novel activity-dependent stimulation paradigms based on intrinsic brain activity. Here, we report on such brain state-dependent stimulation (BSDS combined with haptic feedback provided by a robotic hand orthosis. Transcranial magnetic stimulation of the motor cortex and haptic feedback to the hand were controlled by sensorimotor desynchronization during motor-imagery and applied within a brain-machine interface environment in one healthy subject and one patient with severe hand paresis in the chronic phase after stroke. BSDS significantly increased the excitability of the stimulated motor cortex in both healthy and post-stroke conditions, an effect not observed in non-BSDS protocols. This feasibility study suggests that closing the loop between intrinsic brain state, cortical stimulation and haptic feedback provides a novel neurorehabilitation strategy for stroke patients lacking residual hand function, a proposal that warrants further investigation in a larger cohort of stroke patients.

  10. Analysis of neural activity in human motor cortex -- Towards brain machine interface system

    Science.gov (United States)

    Secundo, Lavi

    The discovery of directional tuned neurons in the primary motor cortex has advanced motor research in several domains. For instance, in the area of brain machine interface (BMI), researchers have exploited the robust characteristic of tuned motor neurons to allow monkeys to learn control of various machines. In the first chapter of this work we examine whether this phenomena can be observed using the less invasive method of recording electrocorticographic signals (ECoG) from the surface of a human's brain. Our findings reveal that individual ECoG channels contain complex movement information about the neuronal population. While some ECoG channels are tuned to hand movement direction (direction specific channels), others are associated to movement but do not contain information regarding movement direction (non-direction specific channels). More specifically, directionality can vary temporally and by frequency within one channel. In addition, a handful of channels contain no significant information regarding movement at all. These findings strongly suggest that directional and non-directional regions of cortex can be identified with ECoG and provide solutions to decoding movement at the signal resolution provided by ECoG. In the second chapter we examine the influence of movement context on movement reconstruction accuracy. We recorded neuronal signals recorded from electro-corticography (ECoG) during performance of cued- and self-initiated movements. ECoG signals were used to train a reconstruction algorithm to reconstruct continuous hand movement. We found that both cued- and self-initiated movements could be reconstructed with similar accuracy from the ECoG data. However, while an algorithm trained on the cued task could reconstruct performance on a subsequent cued trial, it failed to reconstruct self-initiated arm movement. The same task-specificity was observed when the algorithm was trained with self-initiated movement data and tested on the cued task. Thus

  11. An implantable integrated low-power amplifier-microelectrode array for Brain-Machine Interfaces.

    Science.gov (United States)

    Patrick, Erin; Sankar, Viswanath; Rowe, William; Sanchez, Justin C; Nishida, Toshikazu

    2010-01-01

    One of the important challenges in designing Brain-Machine Interfaces (BMI) is to build implantable systems that have the ability to reliably process the activity of large ensembles of cortical neurons. In this paper, we report the design, fabrication, and testing of a polyimide-based microelectrode array integrated with a low-power amplifier as part of the Florida Wireless Integrated Recording Electrode (FWIRE) project at the University of Florida developing a fully implantable neural recording system for BMI applications. The electrode array was fabricated using planar micromachining MEMS processes and hybrid packaged with the amplifier die using a flip-chip bonding technique. The system was tested both on bench and in-vivo. Acute and chronic neural recordings were obtained from a rodent for a period of 42 days. The electrode-amplifier performance was analyzed over the chronic recording period with the observation of a noise floor of 4.5 microVrms, and an average signal-to-noise ratio of 3.8.

  12. A four-dimensional virtual hand brain-machine interface using active dimension selection.

    Science.gov (United States)

    Rouse, Adam G

    2016-06-01

    Brain-machine interfaces (BMI) traditionally rely on a fixed, linear transformation from neural signals to an output state-space. In this study, the assumption that a BMI must control a fixed, orthogonal basis set was challenged and a novel active dimension selection (ADS) decoder was explored. ADS utilizes a two stage decoder by using neural signals to both (i) select an active dimension being controlled and (ii) control the velocity along the selected dimension. ADS decoding was tested in a monkey using 16 single units from premotor and primary motor cortex to successfully control a virtual hand avatar to move to eight different postures. Following training with the ADS decoder to control 2, 3, and then 4 dimensions, each emulating a grasp shape of the hand, performance reached 93% correct with a bit rate of 2.4 bits s(-1) for eight targets. Selection of eight targets using ADS control was more efficient, as measured by bit rate, than either full four-dimensional control or computer assisted one-dimensional control. ADS decoding allows a user to quickly and efficiently select different hand postures. This novel decoding scheme represents a potential method to reduce the complexity of high-dimension BMI control of the hand.

  13. Virtual reality hardware and graphic display options for brain-machine interfaces.

    Science.gov (United States)

    Marathe, Amar R; Carey, Holle L; Taylor, Dawn M

    2008-01-15

    Virtual reality hardware and graphic displays are reviewed here as a development environment for brain-machine interfaces (BMIs). Two desktop stereoscopic monitors and one 2D monitor were compared in a visual depth discrimination task and in a 3D target-matching task where able-bodied individuals used actual hand movements to match a virtual hand to different target hands. Three graphic representations of the hand were compared: a plain sphere, a sphere attached to the fingertip of a realistic hand and arm, and a stylized pacman-like hand. Several subjects had great difficulty using either stereo monitor for depth perception when perspective size cues were removed. A mismatch in stereo and size cues generated inappropriate depth illusions. This phenomenon has implications for choosing target and virtual hand sizes in BMI experiments. Target-matching accuracy was about as good with the 2D monitor as with either 3D monitor. However, users achieved this accuracy by exploring the boundaries of the hand in the target with carefully controlled movements. This method of determining relative depth may not be possible in BMI experiments if movement control is more limited. Intuitive depth cues, such as including a virtual arm, can significantly improve depth perception accuracy with or without stereo viewing.

  14. Neuron-Type-Specific Utility in a Brain-Machine Interface: a Pilot Study.

    Science.gov (United States)

    Garcia-Garcia, Martha G; Bergquist, Austin J; Vargas-Perez, Hector; Nagai, Mary K; Zariffa, Jose; Marquez-Chin, Cesar; Popovic, Milos R

    2017-11-01

    Firing rates of single cortical neurons can be volitionally modulated through biofeedback (i.e. operant conditioning), and this information can be transformed to control external devices (i.e. brain-machine interfaces; BMIs). However, not all neurons respond to operant conditioning in BMI implementation. Establishing criteria that predict neuron utility will assist translation of BMI research to clinical applications. Single cortical neurons (n=7) were recorded extracellularly from primary motor cortex of a Long-Evans rat. Recordings were incorporated into a BMI involving up-regulation of firing rate to control the brightness of a light-emitting-diode and subsequent reward. Neurons were classified as 'fast-spiking', 'bursting' or 'regular-spiking' according to waveform-width and intrinsic firing patterns. Fast-spiking and bursting neurons were found to up-regulate firing rate by a factor of 2.43±1.16, demonstrating high utility, while regular-spiking neurons decreased firing rates on average by a factor of 0.73±0.23, demonstrating low utility. The ability to select neurons with high utility will be important to minimize training times and maximize information yield in future clinical BMI applications. The highly contrasting utility observed between fast-spiking and bursting neurons versus regular-spiking neurons allows for the hypothesis to be advanced that intrinsic electrophysiological properties may be useful criteria that predict neuron utility in BMI implementation.

  15. Learning to control a brain-machine interface for reaching and grasping by primates.

    Directory of Open Access Journals (Sweden)

    Jose M Carmena

    2003-11-01

    Full Text Available Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain-machine interface (BMIc that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.

  16. Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates

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    Rajangam, Sankaranarayani; Tseng, Po-He; Yin, Allen; Lehew, Gary; Schwarz, David; Lebedev, Mikhail A.; Nicolelis, Miguel A. L.

    2016-03-01

    Several groups have developed brain-machine-interfaces (BMIs) that allow primates to use cortical activity to control artificial limbs. Yet, it remains unknown whether cortical ensembles could represent the kinematics of whole-body navigation and be used to operate a BMI that moves a wheelchair continuously in space. Here we show that rhesus monkeys can learn to navigate a robotic wheelchair, using their cortical activity as the main control signal. Two monkeys were chronically implanted with multichannel microelectrode arrays that allowed wireless recordings from ensembles of premotor and sensorimotor cortical neurons. Initially, while monkeys remained seated in the robotic wheelchair, passive navigation was employed to train a linear decoder to extract 2D wheelchair kinematics from cortical activity. Next, monkeys employed the wireless BMI to translate their cortical activity into the robotic wheelchair’s translational and rotational velocities. Over time, monkeys improved their ability to navigate the wheelchair toward the location of a grape reward. The navigation was enacted by populations of cortical neurons tuned to whole-body displacement. During practice with the apparatus, we also noticed the presence of a cortical representation of the distance to reward location. These results demonstrate that intracranial BMIs could restore whole-body mobility to severely paralyzed patients in the future.

  17. Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

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    Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline; Sanchez, Justin C

    2012-01-01

    Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

  18. A brain-machine-muscle interface for restoring hindlimb locomotion after complete spinal transection in rats.

    Directory of Open Access Journals (Sweden)

    Monzurul Alam

    Full Text Available A brain-machine interface (BMI is a neuroprosthetic device that can restore motor function of individuals with paralysis. Although the feasibility of BMI control of upper-limb neuroprostheses has been demonstrated, a BMI for the restoration of lower-limb motor functions has not yet been developed. The objective of this study was to determine if gait-related information can be captured from neural activity recorded from the primary motor cortex of rats, and if this neural information can be used to stimulate paralysed hindlimb muscles after complete spinal cord transection. Neural activity was recorded from the hindlimb area of the primary motor cortex of six female Sprague Dawley rats during treadmill locomotion before and after mid-thoracic transection. Before spinal transection there was a strong association between neural activity and the step cycle. This association decreased after spinal transection. However, the locomotive state (standing vs. walking could still be successfully decoded from neural recordings made after spinal transection. A novel BMI device was developed that processed this neural information in real-time and used it to control electrical stimulation of paralysed hindlimb muscles. This system was able to elicit hindlimb muscle contractions that mimicked forelimb stepping. We propose this lower-limb BMI as a future neuroprosthesis for human paraplegics.

  19. Assessment of brain-machine interfaces from the perspective of people with paralysis.

    Science.gov (United States)

    Blabe, Christine H; Gilja, Vikash; Chestek, Cindy A; Shenoy, Krishna V; Anderson, Kim D; Henderson, Jaimie M

    2015-08-01

    One of the main goals of brain-machine interface (BMI) research is to restore function to people with paralysis. Currently, multiple BMI design features are being investigated, based on various input modalities (externally applied and surgically implantable sensors) and output modalities (e.g. control of computer systems, prosthetic arms, and functional electrical stimulation systems). While these technologies may eventually provide some level of benefit, they each carry associated burdens for end-users. We sought to assess the attitudes of people with paralysis toward using various technologies to achieve particular benefits, given the burdens currently associated with the use of each system. We designed and distributed a technology survey to determine the level of benefit necessary for people with tetraplegia due to spinal cord injury to consider using different technologies, given the burdens currently associated with them. The survey queried user preferences for 8 BMI technologies including electroencephalography, electrocorticography, and intracortical microelectrode arrays, as well as a commercially available eye tracking system for comparison. Participants used a 5-point scale to rate their likelihood to adopt these technologies for 13 potential control capabilities. Survey respondents were most likely to adopt BMI technology to restore some of their natural upper extremity function, including restoration of hand grasp and/or some degree of natural arm movement. High speed typing and control of a fast robot arm were also of interest to this population. Surgically implanted wireless technologies were twice as 'likely' to be adopted as their wired equivalents. Assessing end-user preferences is an essential prerequisite to the design and implementation of any assistive technology. The results of this survey suggest that people with tetraplegia would adopt an unobtrusive, autonomous BMI system for both restoration of upper extremity function and control of

  20. Assessment of brain-machine interfaces from the perspective of people with paralysis

    Science.gov (United States)

    Blabe, Christine H.; Gilja, Vikash; Chestek, Cindy A.; Shenoy, Krishna V.; Anderson, Kim D.; Henderson, Jaimie M.

    2015-08-01

    Objective. One of the main goals of brain-machine interface (BMI) research is to restore function to people with paralysis. Currently, multiple BMI design features are being investigated, based on various input modalities (externally applied and surgically implantable sensors) and output modalities (e.g. control of computer systems, prosthetic arms, and functional electrical stimulation systems). While these technologies may eventually provide some level of benefit, they each carry associated burdens for end-users. We sought to assess the attitudes of people with paralysis toward using various technologies to achieve particular benefits, given the burdens currently associated with the use of each system. Approach. We designed and distributed a technology survey to determine the level of benefit necessary for people with tetraplegia due to spinal cord injury to consider using different technologies, given the burdens currently associated with them. The survey queried user preferences for 8 BMI technologies including electroencephalography, electrocorticography, and intracortical microelectrode arrays, as well as a commercially available eye tracking system for comparison. Participants used a 5-point scale to rate their likelihood to adopt these technologies for 13 potential control capabilities. Main Results. Survey respondents were most likely to adopt BMI technology to restore some of their natural upper extremity function, including restoration of hand grasp and/or some degree of natural arm movement. High speed typing and control of a fast robot arm were also of interest to this population. Surgically implanted wireless technologies were twice as ‘likely’ to be adopted as their wired equivalents. Significance. Assessing end-user preferences is an essential prerequisite to the design and implementation of any assistive technology. The results of this survey suggest that people with tetraplegia would adopt an unobtrusive, autonomous BMI system for both

  1. Designing Closed-Loop Brain-Machine Interfaces Using Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Gautam Kumar

    2016-06-01

    Full Text Available Brain-machine interfaces (BMIs are broadly defined as systems that establish direct communications between living brain tissue and external devices, such as artificial arms. By sensing and interpreting neuronal activities to actuate an external device, BMI-based neuroprostheses hold great promise in rehabilitating motor disabled subjects, such as amputees. In this paper, we develop a control-theoretic analysis of a BMI-based neuroprosthetic system for voluntary single joint reaching task in the absence of visual feedback. Using synthetic data obtained through the simulation of an experimentally validated psycho-physiological cortical circuit model, both the Wiener filter and the Kalman filter based linear decoders are developed. We analyze the performance of both decoders in the presence and in the absence of natural proprioceptive feedback information. By performing simulations, we show that the performance of both decoders degrades significantly in the absence of the natural proprioception. To recover the performance of these decoders, we propose two problems, namely tracking the desired position trajectory and tracking the firing rate trajectory of neurons which encode the proprioception, in the model predictive control framework to design optimal artificial sensory feedback. Our results indicate that while the position trajectory based design can only recover the position and velocity trajectories, the firing rate trajectory based design can recover the performance of the motor task along with the recovery of firing rates in other cortical regions. Finally, we extend our design by incorporating a network of spiking neurons and designing artificial sensory feedback in the form of a charged balanced biphasic stimulating current.

  2. Decoding the non-stationary neuron spike trains by dual Monte Carlo point process estimation in motor Brain Machine Interfaces.

    Science.gov (United States)

    Liao, Yuxi; Li, Hongbao; Zhang, Qiaosheng; Fan, Gong; Wang, Yiwen; Zheng, Xiaoxiang

    2014-01-01

    Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration of the decoding performance when the model is fixed. To track the non-stationary neuron tuning during decoding, we propose a dual model approach based on Monte Carlo point process filtering method that enables the estimation also on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning parameters, which raises a promising way to design a long-term-performing model for Brain Machine Interfaces decoder.

  3. Selective visual attention to drive cognitive brain machine interfaces: from concepts to neurofeedback and rehabilitation applications

    Directory of Open Access Journals (Sweden)

    Elaine eAstrand

    2014-08-01

    Full Text Available Brain Machine Interfaces (BMI using motor cortical activity to drive an external effector like a screen cursor or a robotic arm have seen enormous success and proven their great rehabilitation potential. An emerging parallel effort is now directed to BMIs controlled by endogenous cognitive activity, also called cognitive BMIs. While more challenging, this approach opens new dimensions to the rehabilitation of cognitive disorders. In the present work, we focus on BMIs driven by visuospatial attention signals and we provide a critical review of these studies in the light of the accumulated knowledge about the psychophysics, anatomy and neurophysiology of visual spatial attention. Importantly, we provide a unique comparative overview of the several studies, ranging from noninvasive to invasive human and non-human primates studies, that decode attention-related information from ongoing neuronal activity. We discuss these studies in the light of the challenges attention-driven cognitive BMIs have to face. In a second part of the review, we discuss past and current attention-based neurofeedback studies, describing both the covert effects of neurofeedback onto neuronal activity and its overt behavioral effects. Importantly, we compare neurofeedback studies based on the amplitude of cortical activity to studies based on the enhancement of cortical information content. Last, we discuss several lines of future research and applications for attention-driven cognitive BCIs, including the rehabilitation of cognitive deficits, restored communication in locked-in patients, and open-field applications for enhanced cognition in normal subjects. The core motivation of this work is the key idea that the improvement of current cognitive BMIs for therapeutic and open field applications needs to be grounded in a proper interdisciplinary understanding of the physiology of the cognitive function of interest, be it spatial attention, working memory or any other

  4. Biomimetic rehabilitation engineering: the importance of somatosensory feedback for brain-machine interfaces

    Science.gov (United States)

    Perruchoud, David; Pisotta, Iolanda; Carda, Stefano; Murray, Micah M.; Ionta, Silvio

    2016-08-01

    Objective. Brain-machine interfaces (BMIs) re-establish communication channels between the nervous system and an external device. The use of BMI technology has generated significant developments in rehabilitative medicine, promising new ways to restore lost sensory-motor functions. However and despite high-caliber basic research, only a few prototypes have successfully left the laboratory and are currently home-deployed. Approach. The failure of this laboratory-to-user transfer likely relates to the absence of BMI solutions for providing naturalistic feedback about the consequences of the BMI’s actions. To overcome this limitation, nowadays cutting-edge BMI advances are guided by the principle of biomimicry; i.e. the artificial reproduction of normal neural mechanisms. Main results. Here, we focus on the importance of somatosensory feedback in BMIs devoted to reproducing movements with the goal of serving as a reference framework for future research on innovative rehabilitation procedures. First, we address the correspondence between users’ needs and BMI solutions. Then, we describe the main features of invasive and non-invasive BMIs, including their degree of biomimicry and respective advantages and drawbacks. Furthermore, we explore the prevalent approaches for providing quasi-natural sensory feedback in BMI settings. Finally, we cover special situations that can promote biomimicry and we present the future directions in basic research and clinical applications. Significance. The continued incorporation of biomimetic features into the design of BMIs will surely serve to further ameliorate the realism of BMIs, as well as tremendously improve their actuation, acceptance, and use.

  5. A wireless brain-machine interface for real-time speech synthesis.

    Directory of Open Access Journals (Sweden)

    Frank H Guenther

    2009-12-01

    Full Text Available Brain-machine interfaces (BMIs involving electrodes implanted into the human cerebral cortex have recently been developed in an attempt to restore function to profoundly paralyzed individuals. Current BMIs for restoring communication can provide important capabilities via a typing process, but unfortunately they are only capable of slow communication rates. In the current study we use a novel approach to speech restoration in which we decode continuous auditory parameters for a real-time speech synthesizer from neuronal activity in motor cortex during attempted speech.Neural signals recorded by a Neurotrophic Electrode implanted in a speech-related region of the left precentral gyrus of a human volunteer suffering from locked-in syndrome, characterized by near-total paralysis with spared cognition, were transmitted wirelessly across the scalp and used to drive a speech synthesizer. A Kalman filter-based decoder translated the neural signals generated during attempted speech into continuous parameters for controlling a synthesizer that provided immediate (within 50 ms auditory feedback of the decoded sound. Accuracy of the volunteer's vowel productions with the synthesizer improved quickly with practice, with a 25% improvement in average hit rate (from 45% to 70% and 46% decrease in average endpoint error from the first to the last block of a three-vowel task.Our results support the feasibility of neural prostheses that may have the potential to provide near-conversational synthetic speech output for individuals with severely impaired speech motor control. They also provide an initial glimpse into the functional properties of neurons in speech motor cortical areas.

  6. Brain-machine interfaces for controlling lower-limb powered robotic systems

    Science.gov (United States)

    He, Yongtian; Eguren, David; Azorín, José M.; Grossman, Robert G.; Phat Luu, Trieu; Contreras-Vidal, Jose L.

    2018-04-01

    Objective. Lower-limb, powered robotics systems such as exoskeletons and orthoses have emerged as novel robotic interventions to assist or rehabilitate people with walking disabilities. These devices are generally controlled by certain physical maneuvers, for example pressing buttons or shifting body weight. Although effective, these control schemes are not what humans naturally use. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs). A number of preliminary studies have been published on this topic, but a systematic understanding of the experimental design, tasks, and performance of BMI-exoskeleton systems for restoration of gait is lacking. Approach. To address this gap, we applied standard systematic review methodology for a literature search in PubMed and EMBASE databases and identified 11 studies involving BMI-robotics systems. The devices, user population, input and output of the BMIs and robot systems respectively, neural features, decoders, denoising techniques, and system performance were reviewed and compared. Main results. Results showed BMIs classifying walk versus stand tasks are the most common. The results also indicate that electroencephalography (EEG) is the only recording method for humans. Performance was not clearly presented in most of the studies. Several challenges were summarized, including EEG denoising, safety, responsiveness and others. Significance. We conclude that lower-body powered exoskeletons with automated gait intention detection based on BMIs open new possibilities in the assistance and rehabilitation fields, although the current performance, clinical benefits and several key challenging issues indicate that additional research and development is required to deploy these systems in the clinic and at home. Moreover, rigorous EEG denoising techniques, suitable performance metrics, consistent trial reporting, and more clinical trials are needed to advance the

  7. Tracking Single Units in Chronic, Large Scale, Neural Recordings for Brain Machine Interface Applications

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    Ahmed eEleryan

    2014-07-01

    Full Text Available In the study of population coding in neurobiological systems, tracking unit identity may be critical to assess possible changes in the coding properties of neuronal constituents over prolonged periods of time. Ensuring unit stability is even more critical for reliable neural decoding of motor variables in intra-cortically controlled brain-machine interfaces (BMIs. Variability in intrinsic spike patterns, tuning characteristics, and single-unit identity over chronic use is a major challenge to maintaining this stability, requiring frequent daily calibration of neural decoders in BMI sessions by an experienced human operator. Here, we report on a unit-stability tracking algorithm that efficiently and autonomously identifies putative single-units that are stable across many sessions using a relatively short duration recording interval at the start of each session. The algorithm first builds a database of features extracted from units' average spike waveforms and firing patterns across many days of recording. It then uses these features to decide whether spike occurrences on the same channel on one day belong to the same unit recorded on another day or not. We assessed the overall performance of the algorithm for different choices of features and classifiers trained using human expert judgment, and quantified it as a function of accuracy and execution time. Overall, we found a trade-off between accuracy and execution time with increasing data volumes from chronically implanted rhesus macaques, with an average of 12 seconds processing time per channel at ~90% classification accuracy. Furthermore, 77% of the resulting putative single-units matched those tracked by human experts. These results demonstrate that over the span of a few months of recordings, automated unit tracking can be performed with high accuracy and used to streamline the calibration phase during BMI sessions.

  8. Local-learning-based neuron selection for grasping gesture prediction in motor brain machine interfaces

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    Xu, Kai; Wang, Yiwen; Wang, Yueming; Wang, Fang; Hao, Yaoyao; Zhang, Shaomin; Zhang, Qiaosheng; Chen, Weidong; Zheng, Xiaoxiang

    2013-04-01

    Objective. The high-dimensional neural recordings bring computational challenges to movement decoding in motor brain machine interfaces (mBMI), especially for portable applications. However, not all recorded neural activities relate to the execution of a certain movement task. This paper proposes to use a local-learning-based method to perform neuron selection for the gesture prediction in a reaching and grasping task. Approach. Nonlinear neural activities are decomposed into a set of linear ones in a weighted feature space. A margin is defined to measure the distance between inter-class and intra-class neural patterns. The weights, reflecting the importance of neurons, are obtained by minimizing a margin-based exponential error function. To find the most dominant neurons in the task, 1-norm regularization is introduced to the objective function for sparse weights, where near-zero weights indicate irrelevant neurons. Main results. The signals of only 10 neurons out of 70 selected by the proposed method could achieve over 95% of the full recording's decoding accuracy of gesture predictions, no matter which different decoding methods are used (support vector machine and K-nearest neighbor). The temporal activities of the selected neurons show visually distinguishable patterns associated with various hand states. Compared with other algorithms, the proposed method can better eliminate the irrelevant neurons with near-zero weights and provides the important neuron subset with the best decoding performance in statistics. The weights of important neurons converge usually within 10-20 iterations. In addition, we study the temporal and spatial variation of neuron importance along a period of one and a half months in the same task. A high decoding performance can be maintained by updating the neuron subset. Significance. The proposed algorithm effectively ascertains the neuronal importance without assuming any coding model and provides a high performance with different

  9. Hermetic electronic packaging of an implantable brain-machine-interface with transcutaneous optical data communication.

    Science.gov (United States)

    Schuettler, Martin; Kohler, Fabian; Ordonez, Juan S; Stieglitz, Thomas

    2012-01-01

    Future brain-computer-interfaces (BCIs) for severely impaired patients are implanted to electrically contact the brain tissue. Avoiding percutaneous cables requires amplifier and telemetry electronics to be implanted too. We developed a hermetic package that protects the electronic circuitry of a BCI from body moisture while permitting infrared communication through the package wall made from alumina ceramic. The ceramic package is casted in medical grade silicone adhesive, for which we identified MED2-4013 as a promising candidate.

  10. A primer on brain-machine interfaces, concepts, and technology: a key element in the future of functional neurorestoration.

    Science.gov (United States)

    Lee, Brian; Liu, Charles Y; Apuzzo, Michael L J

    2013-01-01

    Conventionally, the practice of neurosurgery has been characterized by the removal of pathology, congenital or acquired. The emerging complement to the removal of pathology is surgery for the specific purpose of restoration of function. Advents in neuroscience, technology, and the understanding of neural circuitry are creating opportunities to intervene in disease processes in a reparative manner, thereby advancing toward the long-sought-after concept of neurorestoration. Approaching the issue of neurorestoration from a biomedical engineering perspective is the rapidly growing arena of implantable devices. Implantable devices are becoming more common in medicine and are making significant advancements to improve a patient's functional outcome. Devices such as deep brain stimulators, vagus nerve stimulators, and spinal cord stimulators are now becoming more commonplace in neurosurgery as we utilize our understanding of the nervous system to interpret neural activity and restore function. One of the most exciting prospects in neurosurgery is the technologically driven field of brain-machine interface, also known as brain-computer interface, or neuroprosthetics. The successful development of this technology will have far-reaching implications for patients suffering from a great number of diseases, including but not limited to spinal cord injury, paralysis, stroke, or loss of limb. This article provides an overview of the issues related to neurorestoration using implantable devices with a specific focus on brain-machine interface technology. Copyright © 2013 Elsevier Inc. All rights reserved.

  11. Toward FRP-Based Brain-Machine Interfaces-Single-Trial Classification of Fixation-Related Potentials.

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    Andrea Finke

    Full Text Available The co-registration of eye tracking and electroencephalography provides a holistic measure of ongoing cognitive processes. Recently, fixation-related potentials have been introduced to quantify the neural activity in such bi-modal recordings. Fixation-related potentials are time-locked to fixation onsets, just like event-related potentials are locked to stimulus onsets. Compared to existing electroencephalography-based brain-machine interfaces that depend on visual stimuli, fixation-related potentials have the advantages that they can be used in free, unconstrained viewing conditions and can also be classified on a single-trial level. Thus, fixation-related potentials have the potential to allow for conceptually different brain-machine interfaces that directly interpret cortical activity related to the visual processing of specific objects. However, existing research has investigated fixation-related potentials only with very restricted and highly unnatural stimuli in simple search tasks while participant's body movements were restricted. We present a study where we relieved many of these restrictions while retaining some control by using a gaze-contingent visual search task. In our study, participants had to find a target object out of 12 complex and everyday objects presented on a screen while the electrical activity of the brain and eye movements were recorded simultaneously. Our results show that our proposed method for the classification of fixation-related potentials can clearly discriminate between fixations on relevant, non-relevant and background areas. Furthermore, we show that our classification approach generalizes not only to different test sets from the same participant, but also across participants. These results promise to open novel avenues for exploiting fixation-related potentials in electroencephalography-based brain-machine interfaces and thus providing a novel means for intuitive human-machine interaction.

  12. Feedback for reinforcement learning based brain-machine interfaces using confidence metrics

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    Prins, Noeline W.; Sanchez, Justin C.; Prasad, Abhishek

    2017-06-01

    Objective. For brain-machine interfaces (BMI) to be used in activities of daily living by paralyzed individuals, the BMI should be as autonomous as possible. One of the challenges is how the feedback is extracted and utilized in the BMI. Our long-term goal is to create autonomous BMIs that can utilize an evaluative feedback from the brain to update the decoding algorithm and use it intelligently in order to adapt the decoder. In this study, we show how to extract the necessary evaluative feedback from a biologically realistic (synthetic) source, use both the quantity and the quality of the feedback, and how that feedback information can be incorporated into a reinforcement learning (RL) controller architecture to maximize its performance. Approach. Motivated by the perception-action-reward cycle (PARC) in the brain which links reward for cognitive decision making and goal-directed behavior, we used a reward-based RL architecture named Actor-Critic RL as the model. Instead of using an error signal towards building an autonomous BMI, we envision to use a reward signal from the nucleus accumbens (NAcc) which plays a key role in the linking of reward to motor behaviors. To deal with the complexity and non-stationarity of biological reward signals, we used a confidence metric which was used to indicate the degree of feedback accuracy. This confidence was added to the Actor’s weight update equation in the RL controller architecture. If the confidence was high (>0.2), the BMI decoder used this feedback to update its parameters. However, when the confidence was low, the BMI decoder ignored the feedback and did not update its parameters. The range between high confidence and low confidence was termed as the ‘ambiguous’ region. When the feedback was within this region, the BMI decoder updated its weight at a lower rate than when fully confident, which was decided by the confidence. We used two biologically realistic models to generate synthetic data for MI (Izhikevich

  13. Body-Machine Interfaces after Spinal Cord Injury: Rehabilitation and Brain Plasticity

    Directory of Open Access Journals (Sweden)

    Ismael Seáñez-González

    2016-12-01

    Full Text Available The purpose of this study was to identify rehabilitative effects and changes in white matter microstructure in people with high-level spinal cord injury following bilateral upper-extremity motor skill training. Five subjects with high-level (C5–C6 spinal cord injury (SCI performed five visuo-spatial motor training tasks over 12 sessions (2–3 sessions per week. Subjects controlled a two-dimensional cursor with bilateral simultaneous movements of the shoulders using a non-invasive inertial measurement unit-based body-machine interface. Subjects’ upper-body ability was evaluated before the start, in the middle and a day after the completion of training. MR imaging data were acquired before the start and within two days of the completion of training. Subjects learned to use upper-body movements that survived the injury to control the body-machine interface and improved their performance with practice. Motor training increased Manual Muscle Test scores and the isometric force of subjects’ shoulders and upper arms. Moreover, motor training increased fractional anisotropy (FA values in the cingulum of the left hemisphere by 6.02% on average, indicating localized white matter microstructure changes induced by activity-dependent modulation of axon diameter, myelin thickness or axon number. This body-machine interface may serve as a platform to develop a new generation of assistive-rehabilitative devices that promote the use of, and that re-strengthen, the motor and sensory functions that survived the injury.

  14. Ultra-low-cost 3D gaze estimation: an intuitive high information throughput compliment to direct brain-machine interfaces

    Science.gov (United States)

    Abbott, W. W.; Faisal, A. A.

    2012-08-01

    Eye movements are highly correlated with motor intentions and are often retained by patients with serious motor deficiencies. Despite this, eye tracking is not widely used as control interface for movement in impaired patients due to poor signal interpretation and lack of control flexibility. We propose that tracking the gaze position in 3D rather than 2D provides a considerably richer signal for human machine interfaces by allowing direct interaction with the environment rather than via computer displays. We demonstrate here that by using mass-produced video-game hardware, it is possible to produce an ultra-low-cost binocular eye-tracker with comparable performance to commercial systems, yet 800 times cheaper. Our head-mounted system has 30 USD material costs and operates at over 120 Hz sampling rate with a 0.5-1 degree of visual angle resolution. We perform 2D and 3D gaze estimation, controlling a real-time volumetric cursor essential for driving complex user interfaces. Our approach yields an information throughput of 43 bits s-1, more than ten times that of invasive and semi-invasive brain-machine interfaces (BMIs) that are vastly more expensive. Unlike many BMIs our system yields effective real-time closed loop control of devices (10 ms latency), after just ten minutes of training, which we demonstrate through a novel BMI benchmark—the control of the video arcade game ‘Pong’.

  15. [Neurophysiological Foundations and Practical Realizations of the Brain-Machine Interfaces the Technology in Neurological Rehabilitation].

    Science.gov (United States)

    Kaplan, A Ya

    2016-01-01

    Technology brain-computer interface (BCI) based on the registration and interpretation of EEG has recently become one of the most popular developments in neuroscience and psychophysiology. This is due not only to the intended future use of these technologies in many areas of practical human activity, but also to the fact that IMC--is a completely new paradigm in psychophysiology, allowing test hypotheses about the possibilities of the human brain to the development of skills of interaction with the outside world without the mediation of the motor system, i.e. only with the help of voluntary modulation of EEG generators. This paper examines the theoretical and experimental basis, the current state and prospects of development of training, communicational and assisting complexes based on BCI to control them without muscular effort on the basis of mental commands detected in the EEG of patients with severely impaired speech and motor system.

  16. Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface.

    Science.gov (United States)

    Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

    2014-01-01

    In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them.

  17. Towards a miniaturized brain-machine-spinal cord interface (BMSI) for restoration of function after spinal cord injury.

    Science.gov (United States)

    Shahdoost, Shahab; Frost, Shawn; Van Acker, Gustaf; DeJong, Stacey; Dunham, Caleb; Barbay, Scott; Nudo, Randolph; Mohseni, Pedram

    2014-01-01

    Nearly 6 million people in the United States are currently living with paralysis in which 23% of the cases are related to spinal cord injury (SCI). Miniaturized closed-loop neural interfaces have the potential for restoring function and mobility lost to debilitating neural injuries such as SCI by leveraging recent advancements in bioelectronics and a better understanding of the processes that underlie functional and anatomical reorganization in an injured nervous system. This paper describes our current progress towards developing a miniaturized brain-machine-spinal cord interface (BMSI) that is envisioned to convert in real time the neural command signals recorded from the brain to electrical stimuli delivered to the spinal cord below the injury level. Specifically, the paper reports on a corticospinal interface integrated circuit (IC) as a core building block for such a BMSI that is capable of low-noise recording of extracellular neural spikes from the cerebral cortex as well as muscle activation using intraspinal microstimulation (ISMS) in a rat with contusion injury to the thoracic spinal cord. The paper further presents results from a neurobiological study conducted in both normal and SCI rats to investigate the effect of various ISMS parameters on movement thresholds in the rat hindlimb. Coupled with proper signal-processing algorithms in the future for the transformation between the cortically recorded data and ISMS parameters, such a BMSI has the potential to facilitate functional recovery after an SCI by re-establishing corticospinal communication channels lost due to the injury.

  18. A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients.

    Science.gov (United States)

    Sarasola-Sanz, Andrea; Irastorza-Landa, Nerea; Lopez-Larraz, Eduardo; Bibian, Carlos; Helmhold, Florian; Broetz, Doris; Birbaumer, Niels; Ramos-Murguialday, Ander

    2017-07-01

    Including supplementary information from the brain or other body parts in the control of brain-machine interfaces (BMIs) has been recently proposed and investigated. Such enriched interfaces are referred to as hybrid BMIs (hBMIs) and have been proven to be more robust and accurate than regular BMIs for assistive and rehabilitative applications. Electromyographic (EMG) activity is one of the most widely utilized biosignals in hBMIs, as it provides a quite direct measurement of the motion intention of the user. Whereas most of the existing non-invasive EEG-EMG-hBMIs have only been subjected to offline testings or are limited to one degree of freedom (DoF), we present an EEG-EMG-hBMI that allows the simultaneous control of 7-DoFs of the upper limb with a robotic exoskeleton. Moreover, it establishes a biologically-inspired hierarchical control flow, requiring the active participation of central and peripheral structures of the nervous system. Contingent visual and proprioceptive feedback about the user's EEG and EMG activity is provided in the form of velocity modulation during functional task training. We believe that training with this closed-loop system may facilitate functional neuroplastic processes and eventually elicit a joint brain and muscle motor rehabilitation. Its usability is validated during a real-time operation session in a healthy participant and a chronic stroke patient, showing encouraging results for its application to a clinical rehabilitation scenario.

  19. Implications of the dependence of neuronal activity on neural network states for the design of brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Stefano ePanzeri

    2016-04-01

    Full Text Available Brain-machine interfaces (BMIs can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brains. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

  20. Brain machine interface and limb reanimation technologies: restoring function after spinal cord injury through development of a bypass system.

    Science.gov (United States)

    Lobel, Darlene A; Lee, Kendall H

    2014-05-01

    Functional restoration of limb movement after traumatic spinal cord injury (SCI) remains the ultimate goal in SCI treatment and directs the focus of current research strategies. To date, most investigations in the treatment of SCI focus on repairing the injury site. Although offering some promise, these efforts have met with significant roadblocks because treatment measures that are successful in animal trials do not yield similar results in human trials. In contrast to biologic therapies, there are now emerging neural interface technologies, such as brain machine interface (BMI) and limb reanimation through electrical stimulators, to create a bypass around the site of the SCI. The BMI systems analyze brain signals to allow control of devices that are used to assist SCI patients. Such devices may include a computer, robotic arm, or exoskeleton. Limb reanimation technologies, which include functional electrical stimulation, epidural stimulation, and intraspinal microstimulation systems, activate neuronal pathways below the level of the SCI. We present a concise review of recent advances in the BMI and limb reanimation technologies that provides the foundation for the development of a bypass system to improve functional outcome after traumatic SCI. We also discuss challenges to the practical implementation of such a bypass system in both these developing fields. Copyright © 2014 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

  1. Alpha band functional connectivity correlates with the performance of brain-machine interfaces to decode real and imagined movements

    Directory of Open Access Journals (Sweden)

    Hisato eSugata

    2014-08-01

    Full Text Available Brain signals recorded from the primary motor cortex (M1 are known to serve a significant role in coding the information brain-machine interfaces (BMIs need to perform real and imagined movements, and also to form several functional networks with motor association areas. However, whether functional networks between M1 and other brain regions, such as these motor association areas, are related to performance of BMIs is unclear. To examine the relationship between functional connectivity and performance of BMIs, we analyzed the correlation coefficient between performance of neural decoding and functional connectivity over the whole brain using magnetoencephalography. Ten healthy participants were instructed to execute or imagine three simple right upper limb movements. To decode the movement type, we extracted 40 virtual channels in the left M1 via the beamforming approach, and used them as a decoding feature. In addition, seed-based functional connectivities of activities in the alpha band during real and imagined movements were calculated using imaginary coherence. Seed voxels were set as the same virtual channels in M1. After calculating the imaginary coherence in individuals, the correlation coefficient between decoding accuracy and strength of imaginary coherence was calculated over the whole brain. The significant correlations were distributed mainly to motor association areas for both real and imagined movements. These regions largely overlapped with brain regions that had significant connectivity to M1. Our results suggest that use of the strength of functional connectivity between M1 and motor association areas has the potential to improve the performance of BMIs to perform real and imagined movements.

  2. Feasibility of task-specific brain-machine interface training for upper-extremity paralysis in patients with chronic hemiparetic stroke.

    Science.gov (United States)

    Nishimoto, Atsuko; Kawakami, Michiyuki; Fujiwara, Toshiyuki; Hiramoto, Miho; Honaga, Kaoru; Abe, Kaoru; Mizuno, Katsuhiro; Ushiba, Junichi; Liu, Meigen

    2018-01-10

    Brain-machine interface training was developed for upper-extremity rehabilitation for patients with severe hemiparesis. Its clinical application, however, has been limited because of its lack of feasibility in real-world rehabilitation settings. We developed a new compact task-specific brain-machine interface system that enables task-specific training, including reach-and-grasp tasks, and studied its clinical feasibility and effectiveness for upper-extremity motor paralysis in patients with stroke. Prospective beforeâ€"after study. Twenty-six patients with severe chronic hemiparetic stroke. Participants were trained with the brain-machine interface system to pick up and release pegs during 40-min sessions and 40 min of standard occupational therapy per day for 10 days. Fugl-Meyer upper-extremity motor (FMA) and Motor Activity Log-14 amount of use (MAL-AOU) scores were assessed before and after the intervention. To test its feasibility, 4 occupational therapists who operated the system for the first time assessed it with the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST) 2.0. FMA and MAL-AOU scores improved significantly after brain-machine interface training, with the effect sizes being medium and large, respectively (pmachine interface system is feasible for use in real-world clinical settings.

  3. Brain-machine interfaces for assistive smart homes: A feasibility study with wearable near-infrared spectroscopy.

    Science.gov (United States)

    Ogawa, Takeshi; Hirayama, Jun-Ichiro; Gupta, Pankaj; Moriya, Hiroki; Yamaguchi, Shumpei; Ishikawa, Akihiro; Inoue, Yoshihiro; Kawanabe, Motoaki; Ishii, Shin

    2015-08-01

    Smart houses for elderly or physically challenged people need a method to understand residents' intentions during their daily-living behaviors. To explore a new possibility, we here developed a novel brain-machine interface (BMI) system integrated with an experimental smart house, based on a prototype of a wearable near-infrared spectroscopy (NIRS) device, and verified the system in a specific task of controlling of the house's equipments with BMI. We recorded NIRS signals of three participants during typical daily-living actions (DLAs), and classified them by linear support vector machine. In our off-line analysis, four DLAs were classified at about 70% mean accuracy, significantly above the chance level of 25%, in every participant. In an online demonstration in the real smart house, one participant successfully controlled three target appliances by BMI at 81.3% accuracy. Thus we successfully demonstrated the feasibility of using NIRS-BMI in real smart houses, which will possibly enhance new assistive smart-home technologies.

  4. Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity

    Directory of Open Access Journals (Sweden)

    Rosaleena Mohanty

    2018-05-01

    Full Text Available Interventional therapy using brain-computer interface (BCI technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke

  5. Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

    OpenAIRE

    Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

    2007-01-01

    We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic...

  6. Human-machine interface upgrade

    International Nuclear Information System (INIS)

    Kropik, M.; Matejka, K.; Sklenka, L.; Chab, V.

    2002-01-01

    The article describes a new human-machine interface that was installed at the VR-1 training reactor. The human-machine interface upgrade was completed in the summer 2001. The interface was designed with respect to functional, ergonomic and aesthetic requirements. The interface is based on a personal computer equipped with two displays. One display enables alphanumeric communication between the reactor operator and the nuclear reactor I and C. The second display is a graphical one. It presents the status of the reactor, principal parameters (as power, period), control rods positions, course of the reactor power. Furthermore, it is possible to set parameters, to show the active core configuration, to perform reactivity calculations, etc. The software for the new human-machine interface was produced with the InTouch developing tool of the Wonder-Ware Company. It is possible to switch the language of the interface between Czech and English because of many foreign students and visitors to the reactor. Microcomputer based communication units with proper software were developed to connect the new human-machine interface with the present reactor I and C. The new human-machine interface at the VR-1 training reactor improves the comfort and safety of the reactor utilisation, facilitates experiments and training, and provides better support for foreign visitors. (orig.)

  7. A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control

    Directory of Open Access Journals (Sweden)

    Zhichuan Tang

    2016-12-01

    Full Text Available To recognize the user’s motion intention, brain-machine interfaces (BMI usually decode movements from cortical activity to control exoskeletons and neuroprostheses for daily activities. The aim of this paper is to investigate whether self-induced variations of the electroencephalogram (EEG can be useful as control signals for an upper-limb exoskeleton developed by us. A BMI based on event-related desynchronization/synchronization (ERD/ERS is proposed. In the decoder-training phase, we investigate the offline classification performance of left versus right hand and left hand versus both feet by using motor execution (ME or motor imagery (MI. The results indicate that the accuracies of ME sessions are higher than those of MI sessions, and left hand versus both feet paradigm achieves a better classification performance, which would be used in the online-control phase. In the online-control phase, the trained decoder is tested in two scenarios (wearing or without wearing the exoskeleton. The MI and ME sessions wearing the exoskeleton achieve mean classification accuracy of 84.29% ± 2.11% and 87.37% ± 3.06%, respectively. The present study demonstrates that the proposed BMI is effective to control the upper-limb exoskeleton, and provides a practical method by non-invasive EEG signal associated with human natural behavior for clinical applications.

  8. ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: optimizing BMI learning and performance.

    Science.gov (United States)

    Soekadar, Surjo R; Witkowski, Matthias; Mellinger, Jürgen; Ramos, Ander; Birbaumer, Niels; Cohen, Leonardo G

    2011-10-01

    Event-related desynchronization (ERD) of sensori-motor rhythms (SMR) can be used for online brain-machine interface (BMI) control, but yields challenges related to the stability of ERD and feedback strategy to optimize BMI learning.Here, we compared two approaches to this challenge in 20 right-handed healthy subjects (HS, five sessions each, S1-S5) and four stroke patients (SP, 15 sessions each, S1-S15). ERD was recorded from a 275-sensor MEG system. During daily training,motor imagery-induced ERD led to visual and proprioceptive feedback delivered through an orthotic device attached to the subjects' hand and fingers. Group A trained with a heterogeneous reference value (RV) for ERD detection with binary feedback and Group B with a homogenous RV and graded feedback (10 HS and 2 SP in each group). HS in Group B showed better BMI performance than Group A (p learning was significantly better (p learning relative to use of a heterogeneous RV and binary feedback.

  9. A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control.

    Science.gov (United States)

    Tang, Zhichuan; Sun, Shouqian; Zhang, Sanyuan; Chen, Yumiao; Li, Chao; Chen, Shi

    2016-12-02

    To recognize the user's motion intention, brain-machine interfaces (BMI) usually decode movements from cortical activity to control exoskeletons and neuroprostheses for daily activities. The aim of this paper is to investigate whether self-induced variations of the electroencephalogram (EEG) can be useful as control signals for an upper-limb exoskeleton developed by us. A BMI based on event-related desynchronization/synchronization (ERD/ERS) is proposed. In the decoder-training phase, we investigate the offline classification performance of left versus right hand and left hand versus both feet by using motor execution (ME) or motor imagery (MI). The results indicate that the accuracies of ME sessions are higher than those of MI sessions, and left hand versus both feet paradigm achieves a better classification performance, which would be used in the online-control phase. In the online-control phase, the trained decoder is tested in two scenarios (wearing or without wearing the exoskeleton). The MI and ME sessions wearing the exoskeleton achieve mean classification accuracy of 84.29% ± 2.11% and 87.37% ± 3.06%, respectively. The present study demonstrates that the proposed BMI is effective to control the upper-limb exoskeleton, and provides a practical method by non-invasive EEG signal associated with human natural behavior for clinical applications.

  10. Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

    Science.gov (United States)

    Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

    2015-05-01

    Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

  11. Operant conditioning of a multiple degree-of-freedom brain-machine interface in a primate model of amputation.

    Science.gov (United States)

    Balasubramanian, Karthikeyan; Southerland, Joshua; Vaidya, Mukta; Qian, Kai; Eleryan, Ahmed; Fagg, Andrew H; Sluzky, Marc; Oweiss, Karim; Hatsopoulos, Nicholas

    2013-01-01

    Operant conditioning with biofeedback has been shown to be an effective method to modify neural activity to generate goal-directed actions in a brain-machine interface. It is particularly useful when neural activity cannot be mathematically mapped to motor actions of the actual body such as in the case of amputation. Here, we implement an operant conditioning approach with visual feedback in which an amputated monkey is trained to control a multiple degree-of-freedom robot to perform a reach-to-grasp behavior. A key innovation is that each controlled dimension represents a behaviorally relevant synergy among a set of joint degrees-of-freedom. We present a number of behavioral metrics by which to assess improvements in BMI control with exposure to the system. The use of non-human primates with chronic amputation is arguably the most clinically-relevant model of human amputation that could have direct implications for developing a neural prosthesis to treat humans with missing upper limbs.

  12. Closed-Loop Hybrid Gaze Brain-Machine Interface Based Robotic Arm Control with Augmented Reality Feedback

    Directory of Open Access Journals (Sweden)

    Hong Zeng

    2017-10-01

    Full Text Available Brain-machine interface (BMI can be used to control the robotic arm to assist paralysis people for performing activities of daily living. However, it is still a complex task for the BMI users to control the process of objects grasping and lifting with the robotic arm. It is hard to achieve high efficiency and accuracy even after extensive trainings. One important reason is lacking of sufficient feedback information for the user to perform the closed-loop control. In this study, we proposed a method of augmented reality (AR guiding assistance to provide the enhanced visual feedback to the user for a closed-loop control with a hybrid Gaze-BMI, which combines the electroencephalography (EEG signals based BMI and the eye tracking for an intuitive and effective control of the robotic arm. Experiments for the objects manipulation tasks while avoiding the obstacle in the workspace are designed to evaluate the performance of our method for controlling the robotic arm. According to the experimental results obtained from eight subjects, the advantages of the proposed closed-loop system (with AR feedback over the open-loop system (with visual inspection only have been verified. The number of trigger commands used for controlling the robotic arm to grasp and lift the objects with AR feedback has reduced significantly and the height gaps of the gripper in the lifting process have decreased more than 50% compared to those trials with normal visual inspection only. The results reveal that the hybrid Gaze-BMI user can benefit from the information provided by the AR interface, improving the efficiency and reducing the cognitive load during the grasping and lifting processes.

  13. Closed-Loop Hybrid Gaze Brain-Machine Interface Based Robotic Arm Control with Augmented Reality Feedback

    Science.gov (United States)

    Zeng, Hong; Wang, Yanxin; Wu, Changcheng; Song, Aiguo; Liu, Jia; Ji, Peng; Xu, Baoguo; Zhu, Lifeng; Li, Huijun; Wen, Pengcheng

    2017-01-01

    Brain-machine interface (BMI) can be used to control the robotic arm to assist paralysis people for performing activities of daily living. However, it is still a complex task for the BMI users to control the process of objects grasping and lifting with the robotic arm. It is hard to achieve high efficiency and accuracy even after extensive trainings. One important reason is lacking of sufficient feedback information for the user to perform the closed-loop control. In this study, we proposed a method of augmented reality (AR) guiding assistance to provide the enhanced visual feedback to the user for a closed-loop control with a hybrid Gaze-BMI, which combines the electroencephalography (EEG) signals based BMI and the eye tracking for an intuitive and effective control of the robotic arm. Experiments for the objects manipulation tasks while avoiding the obstacle in the workspace are designed to evaluate the performance of our method for controlling the robotic arm. According to the experimental results obtained from eight subjects, the advantages of the proposed closed-loop system (with AR feedback) over the open-loop system (with visual inspection only) have been verified. The number of trigger commands used for controlling the robotic arm to grasp and lift the objects with AR feedback has reduced significantly and the height gaps of the gripper in the lifting process have decreased more than 50% compared to those trials with normal visual inspection only. The results reveal that the hybrid Gaze-BMI user can benefit from the information provided by the AR interface, improving the efficiency and reducing the cognitive load during the grasping and lifting processes. PMID:29163123

  14. Online artifact removal for brain-computer interfaces using support vector machines and blind source separation.

    Science.gov (United States)

    Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

    2007-01-01

    We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.

  15. Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders.

    Science.gov (United States)

    Broccard, Frédéric D; Mullen, Tim; Chi, Yu Mike; Peterson, David; Iversen, John R; Arnold, Mike; Kreutz-Delgado, Kenneth; Jung, Tzyy-Ping; Makeig, Scott; Poizner, Howard; Sejnowski, Terrence; Cauwenberghs, Gert

    2014-08-01

    Traditional approaches for neurological rehabilitation of patients affected with movement disorders, such as Parkinson's disease (PD), dystonia, and essential tremor (ET) consist mainly of oral medication, physical therapy, and botulinum toxin injections. Recently, the more invasive method of deep brain stimulation (DBS) showed significant improvement of the physical symptoms associated with these disorders. In the past several years, the adoption of feedback control theory helped DBS protocols to take into account the progressive and dynamic nature of these neurological movement disorders that had largely been ignored so far. As a result, a more efficient and effective management of PD cardinal symptoms has emerged. In this paper, we review closed-loop systems for rehabilitation of movement disorders, focusing on PD, for which several invasive and noninvasive methods have been developed during the last decade, reducing the complications and side effects associated with traditional rehabilitation approaches and paving the way for tailored individual therapeutics. We then present a novel, transformative, noninvasive closed-loop framework based on force neurofeedback and discuss several future developments of closed-loop systems that might bring us closer to individualized solutions for neurological rehabilitation of movement disorders.

  16. Control of an ambulatory exoskeleton with a brain-machine interface for spinal cord injury gait rehabilitation

    Directory of Open Access Journals (Sweden)

    Eduardo López-Larraz

    2016-08-01

    Full Text Available The closed-loop control of rehabilitative technologies by neural commands has shown a greatpotential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces(BMI can be used as a natural control method for such technologies. BMI provide a continuousassociation between the brain activity and peripheral stimulation, with the potential to induceplastic changes in the nervous system. Paraplegic patients, and especially the ones with incompleteinjuries, constitute a potential target population to be rehabilitated with brain-controlledrobotic systems, as they may improve their gait function after the reinforcement of their sparedintact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatoryexoskeleton–without any weight or balance support–for gait rehabilitation of incomplete spinalcord injury (SCI patients. The integrated system was validated with three healthy subjects, andits viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm,the electroencephalographic signals of the subjects were used to decode their gait intention, andto trigger the movements of the exoskeleton. We designed a protocol with a special emphasison safety, since patients with poor balance were required to stand and walk. We continuouslymonitored their fatigue and exertion levels, and conducted usability and user-satisfaction testsafter the experiments. The results show that, for the three healthy subjects, 84.44□14.56% ofthe trials were correctly decoded. Three out of the four patients performed at least one successfulBMI session, with an average performance of 77.61□14.72%. The shared control strategyimplemented (i.e., the exoskeleton could only move during specific periods of time was effectivein preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22□16.69% and 40.45□16.98% of the trials (for healthy subjects and

  17. Control of an Ambulatory Exoskeleton with a Brain-Machine Interface for Spinal Cord Injury Gait Rehabilitation.

    Science.gov (United States)

    López-Larraz, Eduardo; Trincado-Alonso, Fernando; Rajasekaran, Vijaykumar; Pérez-Nombela, Soraya; Del-Ama, Antonio J; Aranda, Joan; Minguez, Javier; Gil-Agudo, Angel; Montesano, Luis

    2016-01-01

    The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton-without any weight or balance support-for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and

  18. Invasive brain-machine interfaces: a survey of paralyzed patients’ attitudes, knowledge and methods of information retrieval

    Science.gov (United States)

    Lahr, Jacob; Schwartz, Christina; Heimbach, Bernhard; Aertsen, Ad; Rickert, Jörn; Ball, Tonio

    2015-08-01

    Objective. Brain-machine interfaces (BMI) are an emerging therapeutic option that can allow paralyzed patients to gain control over assistive technology devices (ATDs). BMI approaches can be broadly classified into invasive (based on intracranially implanted electrodes) and noninvasive (based on skin electrodes or extracorporeal sensors). Invasive BMIs have a favorable signal-to-noise ratio, and thus allow for the extraction of more information than noninvasive BMIs, but they are also associated with the risks related to neurosurgical device implantation. Current noninvasive BMI approaches are typically concerned, among other issues, with long setup times and/or intensive training. Recent studies have investigated the attitudes of paralyzed patients eligible for BMIs, particularly patients affected by amyotrophic lateral sclerosis (ALS). These studies indicate that paralyzed patients are indeed interested in BMIs. Little is known, however, about the degree of knowledge among paralyzed patients concerning BMI approaches or about how patients retrieve information on ATDs. Furthermore, it is not yet clear if paralyzed patients would accept intracranial implantation of BMI electrodes with the premise of decoding improvements, and what the attitudes of a broader range of patients with diseases such as stroke or spinal cord injury are towards this new kind of treatment. Approach. Using a questionnaire, we surveyed 131 paralyzed patients for their opinions on invasive BMIs and their attitude toward invasive BMI treatment options. Main results. The majority of the patients knew about and had a positive attitude toward invasive BMI approaches. The group of ALS patients was especially open to the concept of BMIs. The acceptance of invasive BMI technology depended on the improvements expected from the technology. Furthermore, the survey revealed that for paralyzed patients, the Internet is an important source of information on ATDs. Significance. Websites tailored to

  19. A confidence metric for using neurobiological feedback in actor-critic reinforcement learning based brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Noeline Wilhelmina Prins

    2014-05-01

    Full Text Available Brain-Machine Interfaces (BMIs can be used to restore function in people living with paralysis. Current BMIs require extensive calibration that increase the set-up times and external inputs for decoder training that may be difficult to produce in paralyzed individuals. Both these factors have presented challenges in transitioning the technology from research environments to activities of daily living (ADL. For BMIs to be seamlessly used in ADL, these issues should be handled with minimal external input thus reducing the need for a technician/caregiver to calibrate the system. Reinforcement Learning (RL based BMIs are a good tool to be used when there is no external training signal and can provide an adaptive modality to train BMI decoders. However, RL based BMIs are sensitive to the feedback provided to adapt the BMI. In actor-critic BMIs, this feedback is provided by the critic and the overall system performance is limited by the critic accuracy. In this work, we developed an adaptive BMI that could handle inaccuracies in the critic feedback in an effort to produce more accurate RL based BMIs. We developed a confidence measure, which indicated how appropriate the feedback is for updating the decoding parameters of the actor. The results show that with the new update formulation, the critic accuracy is no longer a limiting factor for the overall performance. We tested and validated the system on three different data sets: synthetic data generated by an Izhikevich neural spiking model, synthetic data with a Gaussian noise distribution, and data collected from a non-human primate engaged in a reaching task. All results indicated that the system with the critic confidence built in always outperformed the system without the critic confidence. Results of this study suggest the potential application of the technique in developing an autonomous BMI that does not need an external signal for training or extensive calibration.

  20. Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces

    Science.gov (United States)

    Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.; Boahen, Kwabena

    2013-06-01

    Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

  1. Ten-dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions, and limitations

    Science.gov (United States)

    Wodlinger, B.; Downey, J. E.; Tyler-Kabara, E. C.; Schwartz, A. B.; Boninger, M. L.; Collinger, J. L.

    2015-02-01

    Objective. In a previous study we demonstrated continuous translation, orientation and one-dimensional grasping control of a prosthetic limb (seven degrees of freedom) by a human subject with tetraplegia using a brain-machine interface (BMI). The current study, in the same subject, immediately followed the previous work and expanded the scope of the control signal by also extracting hand-shape commands from the two 96-channel intracortical electrode arrays implanted in the subject’s left motor cortex. Approach. Four new control signals, dictating prosthetic hand shape, replaced the one-dimensional grasping in the previous study, allowing the subject to control the prosthetic limb with ten degrees of freedom (three-dimensional (3D) translation, 3D orientation, four-dimensional hand shaping) simultaneously. Main results. Robust neural tuning to hand shaping was found, leading to ten-dimensional (10D) performance well above chance levels in all tests. Neural unit preferred directions were broadly distributed through the 10D space, with the majority of units significantly tuned to all ten dimensions, instead of being restricted to isolated domains (e.g. translation, orientation or hand shape). The addition of hand shaping emphasized object-interaction behavior. A fundamental component of BMIs is the calibration used to associate neural activity to intended movement. We found that the presence of an object during calibration enhanced successful shaping of the prosthetic hand as it closed around the object during grasping. Significance. Our results show that individual motor cortical neurons encode many parameters of movement, that object interaction is an important factor when extracting these signals, and that high-dimensional operation of prosthetic devices can be achieved with simple decoding algorithms. ClinicalTrials.gov Identifier: NCT01364480.

  2. Three-dimensional, automated, real-time video system for tracking limb motion in brain-machine interface studies.

    Science.gov (United States)

    Peikon, Ian D; Fitzsimmons, Nathan A; Lebedev, Mikhail A; Nicolelis, Miguel A L

    2009-06-15

    Collection and analysis of limb kinematic data are essential components of the study of biological motion, including research into biomechanics, kinesiology, neurophysiology and brain-machine interfaces (BMIs). In particular, BMI research requires advanced, real-time systems capable of sampling limb kinematics with minimal contact to the subject's body. To answer this demand, we have developed an automated video tracking system for real-time tracking of multiple body parts in freely behaving primates. The system employs high-contrast markers painted on the animal's joints to continuously track the three-dimensional positions of their limbs during activity. Two-dimensional coordinates captured by each video camera are combined and converted to three-dimensional coordinates using a quadratic fitting algorithm. Real-time operation of the system is accomplished using direct memory access (DMA). The system tracks the markers at a rate of 52 frames per second (fps) in real-time and up to 100fps if video recordings are captured to be later analyzed off-line. The system has been tested in several BMI primate experiments, in which limb position was sampled simultaneously with chronic recordings of the extracellular activity of hundreds of cortical cells. During these recordings, multiple computational models were employed to extract a series of kinematic parameters from neuronal ensemble activity in real-time. The system operated reliably under these experimental conditions and was able to compensate for marker occlusions that occurred during natural movements. We propose that this system could also be extended to applications that include other classes of biological motion.

  3. A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control design.

    Directory of Open Access Journals (Sweden)

    Maryam M Shanechi

    Full Text Available Real-time brain-machine interfaces (BMI have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.

  4. Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

    2017-08-01

    This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

  5. Monte Carlo point process estimation of electromyographic envelopes from motor cortical spikes for brain-machine interfaces

    Science.gov (United States)

    Liao, Yuxi; She, Xiwei; Wang, Yiwen; Zhang, Shaomin; Zhang, Qiaosheng; Zheng, Xiaoxiang; Principe, Jose C.

    2015-12-01

    Objective. Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true. Approach. In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat’s motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties. Main results. Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average. Significance. These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.

  6. Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface

    Science.gov (United States)

    Sachs, Nicholas A.; Ruiz-Torres, Ricardo; Perreault, Eric J.; Miller, Lee E.

    2016-02-01

    Objective. It is quite remarkable that brain machine interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. Approach. We have developed a system that uses two independent Wiener filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The decoder combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. Main results. We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor’s proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. Significance. We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the

  7. Brain-computer interfaces

    DEFF Research Database (Denmark)

    Treder, Matthias S.; Miklody, Daniel; Blankertz, Benjamin

    quality measure'. We were able to show that for stimuli close to the perceptual threshold, there was sometimes a discrepancy between overt responses and brain responses, shedding light on subjects using different response criteria (e.g., more liberal or more conservative). To conclude, brain-computer...... of perceptual and cognitive biases. Furthermore, subjects can only report on stimuli if they have a clear percept of them. On the other hand, the electroencephalogram (EEG), the electrical brain activity measured with electrodes on the scalp, is a more direct measure. It allows us to tap into the ongoing neural...... auditory processing stream. In particular, it can tap brain processes that are pre-conscious or even unconscious, such as the earliest brain responses to sounds stimuli in primary auditory cortex. In a series of studies, we used a machine learning approach to show that the EEG can accurately reflect...

  8. Who Needs to Fit In? Who Gets to Stand Out? Communication Technologies Including Brain-Machine Interfaces Revealed from the Perspectives of Special Education School Teachers through an Ableism Lens

    Science.gov (United States)

    Diep, Lucy; Wolbring, Gregor

    2013-01-01

    Some new and envisioned technologies such as brain machine interfaces (BMI) that are being developed initially for people with disabilities, but whose use can also be expanded to the general public have the potential to change body ability expectations of disabled and non-disabled people beyond the species-typical. The ways in which this dynamic…

  9. Brain-computer interface

    DEFF Research Database (Denmark)

    2014-01-01

    A computer-implemented method of providing an interface between a user and a processing unit, the method comprising : presenting one or more stimuli to a user, each stimulus varying at a respective stimulation frequency, each stimulation frequency being associated with a respective user......-selectable input; receiving at least one signal indicative of brain activity of the user; and determining, from the received signal, which of the one or more stimuli the user attends to and selecting the user-selectable input associated with the stimulation frequency of the determined stimuli as being a user...

  10. Human-machine interface in mobile teleoperators

    International Nuclear Information System (INIS)

    Draper, J.V.

    1985-01-01

    In this document the following point has been made: human-machine interface is not ideal, and may be improved upon: telepresence is ideal but not required; current interfaces degrade normal human inputs/outputs; available and developing technology can improve interfaces

  11. The Muscle Sensor for on-site neuroscience lectures to pave the way for a better understanding of brain-machine-interface research.

    Science.gov (United States)

    Koizumi, Amane; Nagata, Osamu; Togawa, Morio; Sazi, Toshiyuki

    2014-01-01

    Neuroscience is an expanding field of science to investigate enigmas of brain and human body function. However, the majority of the public have never had the chance to learn the basics of neuroscience and new knowledge from advanced neuroscience research through hands-on experience. Here, we report that we produced the Muscle Sensor, a simplified electromyography, to promote educational understanding in neuroscience. The Muscle Sensor can detect myoelectric potentials which are filtered and processed as 3-V pulse signals to shine a light bulb and emit beep sounds. With this educational tool, we delivered "On-Site Neuroscience Lectures" in Japanese junior-high schools to facilitate hands-on experience of neuroscientific electrophysiology and to connect their text-book knowledge to advanced neuroscience researches. On-site neuroscience lectures with the Muscle Sensor pave the way for a better understanding of the basics of neuroscience and the latest topics such as how brain-machine-interface technology could help patients with disabilities such as spinal cord injuries. Copyright © 2013 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  12. A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems

    Science.gov (United States)

    Tahernezhad-Javazm, Farajollah; Azimirad, Vahid; Shoaran, Maryam

    2018-04-01

    Objective. Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. Approach. The paper is divided into two main parts. In the first part, a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, sensory motor rhythm-BMI and event-related potentials-BMI. Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. Main results. In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, linear discriminant analysis and support vector machines with respect to CVA evaluation metric, and naive Bayes with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers, Bagg-DT (bagging decision tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (bagging logistic regression) and AdaBoost (adaptive boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective invasive weed optimization (IWO) and bi-objective nondominated sorting IWO algorithms demonstrated the best performances. Significance. We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event-related potentials) as well as their optimization methods

  13. Brain-state dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation

    Directory of Open Access Journals (Sweden)

    Daniel eBrauchle

    2015-10-01

    Full Text Available While robot-assisted arm and hand training after stroke allows for intensive task-oriented practice, it has provided only limited additional benefit over dose-matched physiotherapy up to now. These rehabilitation devices are possibly too supportive during the exercises. Neurophysiological signals might be one way of avoiding slacking and providing robotic support only when the brain is particularly responsive to peripheral input.We tested the feasibility of three-dimensional robotic assistance for reach-to-grasp movements with a multi-joint exoskeleton during motor imagery-related desynchronization of sensorimotor oscillations in the β-band only. We also registered task-related network changes of cortical functional connectivity by electroencephalography via the imaginary part of the coherence function.Healthy subjects and stroke survivors showed similar patterns – but different aptitudes – of controlling the robotic movement. All participants in this pilot study with nine healthy subjects and two stroke patients achieved their maximum performance during the early stages of the task. Robotic control was significantly higher and less variable when proprioceptive feedback was provided in addition to visual feedback, i.e. when the orthosis was actually attached to the subject’s arm during the task. A distributed cortical network of task-related coherent activity in the θ-band showed significant differences between healthy subjects and stroke patients as well as between early and late periods of the task.Brain-robot interfaces may successfully link three-dimensional robotic training to the participants’ efforts and allow for task-oriented practice of activities of daily living with a physiologically controlled multi-joint exoskeleton. Changes of cortical physiology during the task might also help to make subject-specific adjustments of task difficulty and guide adjunct interventions to facilitate motor learning for functional restoration.

  14. Robust Brain-Computer Interfaces

    NARCIS (Netherlands)

    Reuderink, B.

    2011-01-01

    A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing the traditional pathway of peripheral nerves and muscles. Current BCIs aimed at patients require that the user invests weeks, or even months, to learn the skill to intentionally modify their brain

  15. A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Xiong, Tao; Richardson, Andrew G; Lucas, Timothy H; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D; Van der Spiegel, Jan

    2016-07-18

    Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.

  16. Design and optimization of an EEG-based brain machine interface (BMI to an upper-limb exoskeleton for stroke survivors

    Directory of Open Access Journals (Sweden)

    Nikunj Arunkumar Bhagat

    2016-03-01

    Full Text Available This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG-based brain machine interface (BMI. Intent was inferred from movement related cortical potentials (MRCPs measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II, to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: 1 an adaptive time window was used for extracting features during BMI calibration; 2 training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and 3 BMI predictions were gated by residual electromyography (EMG activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR = 62.7 +/- 21.4 % on day 4 and 67.1 +/- 14.6 % on day 5. The overall false positive rate (FPR across subjects was 27.74 +/- 37.46 % on day 4 and 27.5 +/- 35.64 % on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10 %. On average, motor intent was detected -367 +/- 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration.

  17. Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors

    Science.gov (United States)

    Bhagat, Nikunj A.; Venkatakrishnan, Anusha; Abibullaev, Berdakh; Artz, Edward J.; Yozbatiran, Nuray; Blank, Amy A.; French, James; Karmonik, Christof; Grossman, Robert G.; O'Malley, Marcia K.; Francisco, Gerard E.; Contreras-Vidal, Jose L.

    2016-01-01

    This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: (1) an adaptive time window was used for extracting features during BMI calibration; (2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and (3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 ± 21.4% on day 4 and 67.1 ± 14.6% on day 5. The overall false positive rate (FPR) across subjects was 27.74 ± 37.46% on day 4 and 27.5 ± 35.64% on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10%). On average, motor intent was detected −367 ± 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration. PMID:27065787

  18. Man machine interface and its implementation

    International Nuclear Information System (INIS)

    Hills, B.G.; Boettcher, D.B.; Reed, R.

    1992-01-01

    Sizewell B is the latest nuclear power station to be constructed in the United Kingdom: its Man-Machine Interfaces are therefore, by definition, the state-of-the-art. This paper discusses the principal Man-Machine Interfaces used in the operation of the station, and the systems that implement them. The Man-Machine Interface facilities discussed are: in the Main Control Room, which is used for normal operation and shutdown of the plant: in the Auxiliary Shutdown Room, which allows shutdown of the reactor if evacuation of the main Control Room is necessary: and in the Technical Support Centre, which is used for remote monitoring of the plant. The Man-Machine Interfaces that are described are parts of a station-wide group of interlinked computer systems called the Data Processing and Control System. This system collects data from the plant and displays it to the operators via discrete devices and on graphical computer displays. It also acquires control inputs from the operators via switches, which are then used to provide remote manual control, modulating control and sequence control. The computer system that handles the plant process data and alarm information displays uses a windowing interface with keyboard and trackerball navigation to allow easy retrieval and viewing of information. It is this system that is the main topic of this paper. (author)

  19. Man-machine interface requirements - advanced technology

    Science.gov (United States)

    Remington, R. W.; Wiener, E. L.

    1984-01-01

    Research issues and areas are identified where increased understanding of the human operator and the interaction between the operator and the avionics could lead to improvements in the performance of current and proposed helicopters. Both current and advanced helicopter systems and avionics are considered. Areas critical to man-machine interface requirements include: (1) artificial intelligence; (2) visual displays; (3) voice technology; (4) cockpit integration; and (5) pilot work loads and performance.

  20. Man-machine interface for the MFTF

    International Nuclear Information System (INIS)

    Speckert, G.C.

    1979-01-01

    In any complex system, the interesting problems occur at the interface of dissimilar subsystems. Control of the Mirror Fusion Test Facility (MFTF) begins with the US Congress, which controls the dollars, which control the people, who control the nine top-level minicomputers, which control the 65 microprocessors, which control the hardware that controls the physics experiment. There are many interesting boundaries across which control must pass, and the one that this paper addresses is the man-machine one. For the MFTF, the man-machine interface consists of a system of seven control consoles, each allowing one operator to communicate with one minicomputer. These consoles are arranged in a hierarchical manner, and both hardware and software were designed in a top-down fashion. This paper describes the requirements and the design of the console system as a whole, as well as the design and operation of the hardware and software of each console, and examines the possible form of a future man-machine interface

  1. Man-machine interface for the MFTF

    Energy Technology Data Exchange (ETDEWEB)

    Speckert, G.C.

    1979-11-09

    In any complex system, the interesting problems occur at the interface of dissimilar subsystems. Control of the Mirror Fusion Test Facility (MFTF) begins with the US Congress, which controls the dollars, which control the people, who control the nine top-level minicomputers, which control the 65 microprocessors, which control the hardware that controls the physics experiment. There are many interesting boundaries across which control must pass, and the one that this paper addresses is the man-machine one. For the MFTF, the man-machine interface consists of a system of seven control consoles, each allowing one operator to communicate with one minicomputer. These consoles are arranged in a hierarchical manner, and both hardware and software were designed in a top-down fashion. This paper describes the requirements and the design of the console system as a whole, as well as the design and operation of the hardware and software of each console, and examines the possible form of a future man-machine interface.

  2. Flexible human machine interface for process diagnostics

    International Nuclear Information System (INIS)

    Reifman, J.; Graham, G.E.; Wei, T.Y.C.; Brown, K.R.; Chin, R.Y.

    1996-01-01

    A flexible human machine interface to design and display graphical and textual process diagnostic information is presented. The system operates on different computer hardware platforms, including PCs under MS Windows and UNIX Workstations under X-Windows, in a client-server architecture. The interface system is customized for specific process applications in a graphical user interface development environment by overlaying the image of the process piping and instrumentation diagram with display objects that are highlighted in color during diagnostic display. Customization of the system is presented for Commonwealth Edison's Braidwood PWR Chemical and Volume Control System with transients simulated by a full-scale operator-training simulator and diagnosed by a computer-based system

  3. Academic Training: The LHC machine /experiment interface

    CERN Multimedia

    Françoise Benz

    2005-01-01

    2004-2005 ACADEMIC TRAINING PROGRAMME LECTURE SERIES 18, 19, 20, 21 & 22 April from 11.00 to 12.00 hrs - Main Auditorium, bldg. 500 The LHC machine /experiment interface S. TAPPROGGE, Univ. of Mainz, D, R. ASSMANN, CERN-AB E. TSESMELIS and D. MACINA, CERN-TS This series of lectures will cover some of the major issues at the boundary between the LHC machine and the experiments: 1) The physics motivation and expectations of the experiments regarding the machine operation. This will include an overview of the LHC physics programme (in pp and PbPb collisions), of the experimental signatures (from high pT objects to leading nucleons) and of the expected trigger rates as well as the data sets needed for specific measurements. Furthermore, issues related to various modes of operation of the machine (e.g. bunch spacings of 25 ns. vs. 75 ns.) and special requirements of the detectors for their commissioning will be described. 2) The LHC machine aspects: introduction of the main LHC parameters and discu...

  4. Academic Training: The LHC machine /experiment interface

    CERN Multimedia

    Françoise Benz

    2005-01-01

    2004-2005 ACADEMIC TRAINING PROGRAMME LECTURE SERIES 18, 19, 20, 21 & 22 April from 11.00 to 12.00 hrs - Main Auditorium, bldg. 500 The LHC machine /experiment interface S. TAPPROGGE, Univ. of Mainz, D, R. ASSMANN, CERN-AB E. TSESMELIS and D. MACINA, CERN-TS This series of lectures will cover some of the major issues at the boundary between the LHC machine and the experiments: 1) The physics motivation and expectations of the experiments regarding the machine operation. This will include an overview of the LHC physics programme (in pp and PbPb collisions), of the experimental signatures (from high pT objects to leading nucleons) and of the expected trigger rates as well as the data sets needed for specific measurements. Furthermore, issues related to various modes of operation of the machine (e.g. bunch spacings of 25 ns. vs. 75 ns.) and special requirements of the detectors for their commissioning will be described. 2) The LHC machine aspects: introduction of the main LHC parameters and disc...

  5. Man machine interface based on speech recognition

    International Nuclear Information System (INIS)

    Jorge, Carlos A.F.; Aghina, Mauricio A.C.; Mol, Antonio C.A.; Pereira, Claudio M.N.A.

    2007-01-01

    This work reports the development of a Man Machine Interface based on speech recognition. The system must recognize spoken commands, and execute the desired tasks, without manual interventions of operators. The range of applications goes from the execution of commands in an industrial plant's control room, to navigation and interaction in virtual environments. Results are reported for isolated word recognition, the isolated words corresponding to the spoken commands. For the pre-processing stage, relevant parameters are extracted from the speech signals, using the cepstral analysis technique, that are used for isolated word recognition, and corresponds to the inputs of an artificial neural network, that performs recognition tasks. (author)

  6. Human-machine interface software package

    International Nuclear Information System (INIS)

    Liu, D.K.; Zhang, C.Z.

    1992-01-01

    The Man-Machine Interface software Package (MMISP) is designed to configure the console software of PLS 60 Mev LINAC control system. The control system of PLS 60 Mev LINAC is a distributed control system which includes the main computer (Intel 310) four local station, and two sets of industrial level console computer. The MMISP provides the operator with the display page editor, various I/O configuration such as digital signals In/Out, analog signal In/Out, waveform TV graphic display, and interactive with operator through graphic picture display, voice explanation, and touch panel. This paper describes its function and application. (author)

  7. Brain Computer Interfaces, a Review

    Directory of Open Access Journals (Sweden)

    Luis Fernando Nicolas-Alonso

    2012-01-01

    Full Text Available A brain-computer interface (BCI is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

  8. Brain Computer Interfaces, a Review

    Science.gov (United States)

    Nicolas-Alonso, Luis Fernando; Gomez-Gil, Jaime

    2012-01-01

    A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices. PMID:22438708

  9. Man-machine interface versus full automation

    International Nuclear Information System (INIS)

    Hatton, V.

    1984-01-01

    As accelerators grow in size and complexity of operation there is an increasing economical as well as an operational incentive for the controls and operations teams to use computers to help the man-machine interface. At first the computer network replaced the traditional controls racks filled with knobs, buttons and digital displays of voltages and potentiometer readings. The computer system provided the operator with the extension of his hands and eyes. It was quickly found that much more could be achieved. Where previously it was necessary for the human operator to decide the order of the actions to be executed by the computer as a result of a visual indication of malfunctioning of the accelerator, now the operation is becoming more and more under the direct control of the computer system. Expert knowledge is programmed into the system to help the non-specialist make decision and to safeguard the equipment. Machine physics concepts have been incorporated and critical machine parameters can be optimised easily by the physicists or operators without any detailed knowledge of the intervening medium or of the equipment being controlled. As confidence grows and reliability improves, more and more automation can be added. How far can this process of automation replace the skilled operator. Can the accelerators of tomorrow be run like the ever increasing robotic assembly plants of today. How is the role of the operator changing in this new environment

  10. The LHC machine-experiment interface

    CERN Multimedia

    CERN. Geneva; Tsesmelis, Emmanuel; Brüning, Oliver Sim

    2002-01-01

    This series of three lectures will provide an overview of issues arising at the interface between the LHC machine and the experiments, which are required for guiding the interaction between the collider and the experiments when operation of the LHC commences. A basic description of the LHC Collider and its operating parameters, such as its energy, currents, bunch structure and luminosity, as well as variations on these parameters, will be given. Furthermore, the optics foreseen for the experimental insertions, the sources and intensities of beam losses and the running-in scenarios for the various phases of operation will be discussed. A second module will cover the specific requirements and expectations of each experiment in terms of the layout of experimental areas, the matters related to radiation monitoring and shielding, the design of the beam pipe and the vacuum system, alignment issues and the measurement of the total cross-section and absolute luminosity by the experiments. Finally an analysis of infor...

  11. Machine Learning Interface for Medical Image Analysis.

    Science.gov (United States)

    Zhang, Yi C; Kagen, Alexander C

    2017-10-01

    TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Although TensorFlow usage is well established with computer vision datasets, the TensorFlow interface with DICOM formats for medical imaging remains to be established. Our goal is to extend the TensorFlow API to accept raw DICOM images as input; 1513 DaTscan DICOM images were obtained from the Parkinson's Progression Markers Initiative (PPMI) database. DICOM pixel intensities were extracted and shaped into tensors, or n-dimensional arrays, to populate the training, validation, and test input datasets for machine learning. A simple neural network was constructed in TensorFlow to classify images into normal or Parkinson's disease groups. Training was executed over 1000 iterations for each cross-validation set. The gradient descent optimization and Adagrad optimization algorithms were used to minimize cross-entropy between the predicted and ground-truth labels. Cross-validation was performed ten times to produce a mean accuracy of 0.938 ± 0.047 (95 % CI 0.908-0.967). The mean sensitivity was 0.974 ± 0.043 (95 % CI 0.947-1.00) and mean specificity was 0.822 ± 0.207 (95 % CI 0.694-0.950). We extended the TensorFlow API to enable DICOM compatibility in the context of DaTscan image analysis. We implemented a neural network classifier that produces diagnostic accuracies on par with excellent results from previous machine learning models. These results indicate the potential role of TensorFlow as a useful adjunct diagnostic tool in the clinical setting.

  12. Graphic man-machine interface applied to nuclear reactor designs

    International Nuclear Information System (INIS)

    Pereira, Claudio M.N.A; Mol, Antonio Carlos A.

    1999-01-01

    The Man-Machine Interfaces have been of interest of many researchers in the area of nuclear human factors engineering, principally applied to monitoring systems. The clarity of information provides best adaptation of the men to the machine. This work proposes the development of a Graphic Man-Machine Interface applied to nuclear reactor designs as a tool to optimize them. Here is present a prototype of a graphic man-machine interface for the Hammer code developed for PC under the Windows environment. The results of its application are commented. (author)

  13. Navigation with a passive brain based interface

    NARCIS (Netherlands)

    Erp, J.B.F. van; Werkhoven, P.J.; Thurlings, M.E.; Brouwer, A.-M.

    2009-01-01

    In this paper, we describe a Brain Computer Interface (BCI) for navigation. The system is based on detecting brain signals that are elicited by tactile stimulation on the torso indicating the desired direction.

  14. The Mind and the Machine. On the Conceptual and Moral Implications of Brain-Machine Interaction.

    Science.gov (United States)

    Schermer, Maartje

    2009-12-01

    Brain-machine interfaces are a growing field of research and application. The increasing possibilities to connect the human brain to electronic devices and computer software can be put to use in medicine, the military, and entertainment. Concrete technologies include cochlear implants, Deep Brain Stimulation, neurofeedback and neuroprosthesis. The expectations for the near and further future are high, though it is difficult to separate hope from hype. The focus in this paper is on the effects that these new technologies may have on our 'symbolic order'-on the ways in which popular categories and concepts may change or be reinterpreted. First, the blurring distinction between man and machine and the idea of the cyborg are discussed. It is argued that the morally relevant difference is that between persons and non-persons, which does not necessarily coincide with the distinction between man and machine. The concept of the person remains useful. It may, however, become more difficult to assess the limits of the human body. Next, the distinction between body and mind is discussed. The mind is increasingly seen as a function of the brain, and thus understood in bodily and mechanical terms. This raises questions concerning concepts of free will and moral responsibility that may have far reaching consequences in the field of law, where some have argued for a revision of our criminal justice system, from retributivist to consequentialist. Even without such a (unlikely and unwarranted) revision occurring, brain-machine interactions raise many interesting questions regarding distribution and attribution of responsibility.

  15. Introduction to machine learning for brain imaging.

    Science.gov (United States)

    Lemm, Steven; Blankertz, Benjamin; Dickhaus, Thorsten; Müller, Klaus-Robert

    2011-05-15

    Machine learning and pattern recognition algorithms have in the past years developed to become a working horse in brain imaging and the computational neurosciences, as they are instrumental for mining vast amounts of neural data of ever increasing measurement precision and detecting minuscule signals from an overwhelming noise floor. They provide the means to decode and characterize task relevant brain states and to distinguish them from non-informative brain signals. While undoubtedly this machinery has helped to gain novel biological insights, it also holds the danger of potential unintentional abuse. Ideally machine learning techniques should be usable for any non-expert, however, unfortunately they are typically not. Overfitting and other pitfalls may occur and lead to spurious and nonsensical interpretation. The goal of this review is therefore to provide an accessible and clear introduction to the strengths and also the inherent dangers of machine learning usage in the neurosciences. Copyright © 2010 Elsevier Inc. All rights reserved.

  16. A Human Machine Interface for EVA

    Science.gov (United States)

    Hartmann, L.

    EVA astronauts work in a challenging environment that includes high rate of muscle fatigue, haptic and proprioception impairment, lack of dexterity and interaction with robotic equipment. Currently they are heavily dependent on support from on-board crew and ground station staff for information and robotics operation. They are limited to the operation of simple controls on the suit exterior and external robot controls that are difficult to operate because of the heavy gloves that are part of the EVA suit. A wearable human machine interface (HMI) inside the suit provides a powerful alternative for robot teleoperation, procedure checklist access, generic equipment operation via virtual control panels and general information retrieval and presentation. The HMI proposed here includes speech input and output, a simple 6 degree of freedom (dof) pointing device and a heads up display (HUD). The essential characteristic of this interface is that it offers an alternative to the standard keyboard and mouse interface of a desktop computer. The astronaut's speech is used as input to command mode changes, execute arbitrary computer commands and generate text. The HMI can respond with speech also in order to confirm selections, provide status and feedback and present text output. A candidate 6 dof pointing device is Measurand's Shapetape, a flexible "tape" substrate to which is attached an optic fiber with embedded sensors. Measurement of the modulation of the light passing through the fiber can be used to compute the shape of the tape and, in particular, the position and orientation of the end of the Shapetape. It can be used to provide any kind of 3d geometric information including robot teleoperation control. The HUD can overlay graphical information onto the astronaut's visual field including robot joint torques, end effector configuration, procedure checklists and virtual control panels. With suitable tracking information about the position and orientation of the EVA suit

  17. Man-machine interfaces analysis system based on computer simulation

    International Nuclear Information System (INIS)

    Chen Xiaoming; Gao Zuying; Zhou Zhiwei; Zhao Bingquan

    2004-01-01

    The paper depicts a software assessment system, Dynamic Interaction Analysis Support (DIAS), based on computer simulation technology for man-machine interfaces (MMI) of a control room. It employs a computer to simulate the operation procedures of operations on man-machine interfaces in a control room, provides quantified assessment, and at the same time carries out analysis on operational error rate of operators by means of techniques for human error rate prediction. The problems of placing man-machine interfaces in a control room and of arranging instruments can be detected from simulation results. DIAS system can provide good technical supports to the design and improvement of man-machine interfaces of the main control room of a nuclear power plant

  18. Legal Aspects of Brain-Computer Interfaces

    Czech Academy of Sciences Publication Activity Database

    Krausová, Alžběta

    2014-01-01

    Roč. 8, č. 2 (2014) ISSN 1802-5951 Institutional support: RVO:68378122 Keywords : brain-computer interface * human rights * right to privacy, Subject RIV: AG - Legal Sciences http://mujlt.law.muni.cz/index.php

  19. New human machine interface for VR-1 training reactor

    International Nuclear Information System (INIS)

    Kropik, M.; Matejka, K.; Sklenka, L.; Chab, V.

    2002-01-01

    The contribution describes a new human machine interface that was installed at the VR-1 training reactor. The human machine interface update was completed in the summer 2001. The human machine interface enables to operate the training reactor. The interface was designed with respect to functional, ergonomic and aesthetic requirements. The interface is based on a personal computer equipped with two displays. One display enables alphanumeric communication between a reactor operator and the control and safety system of the nuclear reactor. Messages appear from the control system, the operator can write commands and send them there. The second display is a graphical one. It is possible to represent there the status of the reactor, principle parameters (as power, period), control rods' positions, the course of the reactor power. Furthermore, it is possible to set parameters, to show the active core configuration, to perform reactivity calculations, etc. The software for the new human machine interface was produced in the InTouch developing environment of the WonderWare Company. It is possible to switch the language of the interface between Czech and English because of many foreign students and visitors at the reactor. The former operator's desk was completely removed and superseded with a new one. Besides of the computer and the two displays, there are control buttons, indicators and individual numerical displays of instrumentation there. Utilised components guarantee high quality of the new equipment. Microcomputer based communication units with proper software were developed to connect the contemporary control and safety system with the personal computer of the human machine interface and the individual displays. New human machine interface at the VR-1 training reactor improves the safety and comfort of the reactor utilisation, facilitates experiments and training, and provides better support of foreign visitors.(author)

  20. Wearable ear EEG for brain interfacing

    Science.gov (United States)

    Schroeder, Eric D.; Walker, Nicholas; Danko, Amanda S.

    2017-02-01

    Brain-computer interfaces (BCIs) measuring electrical activity via electroencephalogram (EEG) have evolved beyond clinical applications to become wireless consumer products. Typically marketed for meditation and neu- rotherapy, these devices are limited in scope and currently too obtrusive to be a ubiquitous wearable. Stemming from recent advancements made in hearing aid technology, wearables have been shrinking to the point that the necessary sensors, circuitry, and batteries can be fit into a small in-ear wearable device. In this work, an ear-EEG device is created with a novel system for artifact removal and signal interpretation. The small, compact, cost-effective, and discreet device is demonstrated against existing consumer electronics in this space for its signal quality, comfort, and usability. A custom mobile application is developed to process raw EEG from each device and display interpreted data to the user. Artifact removal and signal classification is accomplished via a combination of support matrix machines (SMMs) and soft thresholding of relevant statistical properties.

  1. Proton storage ring: man/machine interface

    International Nuclear Information System (INIS)

    Lander, R.F.; Clout, P.N.

    1985-01-01

    The human interface of the Proton Storage Ring Control System at Los Alamos is described in some detail, together with the software environment in which operator interaction programs are written. Some examples of operator interaction programs are given

  2. Human-machine Interface for Presentation Robot

    Czech Academy of Sciences Publication Activity Database

    Krejsa, Jiří; Ondroušek, V.

    2012-01-01

    Roč. 6, č. 2 (2012), s. 17-21 ISSN 1897-8649 Institutional research plan: CEZ:AV0Z20760514 Keywords : human-robot interface * mobile robot * presentation robot Subject RIV: JD - Computer Applications, Robotics

  3. Experiencing Brain-Computer Interface Control

    NARCIS (Netherlands)

    van de Laar, B.L.A.

    2016-01-01

    Brain-Computer Interfaces (BCIs) are systems that extract information from the user’s brain activity and employ it in some way in an interactive system. While historically BCIs were mainly catered towards paralyzed or otherwise physically handicapped users, the last couple of years applications with

  4. Very forward calorimeters readout and machine interface

    Indian Academy of Sciences (India)

    for the instrumentation of the forward region of the future detector at the international linear collider. ... The LumiCal can be used for the measurement of integrated luminosity based on Bhabha scattering process with a relative precision of 10−4. The BeamCal can be used to ... the machine control system. The purpose of ...

  5. Human machine interface for research reactor instrumentation and control system

    International Nuclear Information System (INIS)

    Mohd Sabri Minhat; Mohd Idris Taib; Izhar Abu Hussin; Zareen Khan Abdul Jalil Khan; Nurfarhana Ayuni Joha

    2010-01-01

    Most present design of Human Machine Interface for Research Reactor Instrumentation and Control System is modular-based, comprise of several cabinets such as Reactor Protection System, Control Console, Information Console as well as Communication Console. The safety, engineering and human factor will be concerned for the design. Redundancy and separation of signal and power supply are the main factor for safety consideration. The design of Operator Interface absolutely takes consideration of human and environmental factors. Physical parameters, experiences, trainability and long-established habit patterns are very important for user interface, instead of the Aesthetic and Operator-Interface Geometry. Physical design for New Instrumentation and Control System of RTP are proposed base on the state-of- the-art Human Machine Interface design. (author)

  6. The Properties of Intelligent Human-Machine Interface

    Directory of Open Access Journals (Sweden)

    Alexander Alfimtsev

    2012-04-01

    Full Text Available Intelligent human-machine interfaces based on multimodal interaction are developed separately in different application areas. No unified opinion exists about the issue of what properties should these interfaces have to provide an intuitive and natural interaction. Having carried out an analytical survey of the papers that deal with intelligent interfaces a set of properties are presented, which are necessary for intelligent interface between an information system and a human: absolute response, justification, training, personification, adaptiveness, collectivity, security, hidden persistence, portability, filtering.

  7. Nano Trek Beyond: Driving Nanocars/Molecular Machines at Interfaces.

    Science.gov (United States)

    Ariga, Katsuhiko; Mori, Taizo; Nakanishi, Waka

    2018-03-09

    In 2016, the Nobel Prize in Chemistry was awarded for pioneering work on molecular machines. Half a year later, in Toulouse, the first molecular car race, a "nanocar race", was held by using the tip of a scanning tunneling microscope as an electrical remote control. In this Focus Review, we discuss the current state-of-the-art in research on molecular machines at interfaces. In the first section, we briefly explain the science behind the nanocar race, followed by a selection of recent examples of controlling molecules on surfaces. Finally, motion synchronization and the functions of molecular machines at liquid interfaces are discussed. This new concept of molecular tuning at interfaces is also introduced as a method for the continuous modification and optimization of molecular structure for target functions. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  8. Man-machine interface builders at the Advanced Photon Source

    International Nuclear Information System (INIS)

    Anderson, M.D.

    1991-01-01

    Argonne National Laboratory is constructing a 7-GeV Advanced Photon Source for use as a synchrotron radiation source in basic and applied research. The controls and computing environment for this accelerator complex includes graphical operator interfaces to the machine based on Motif, X11, and PHIGS/PEX. Construction and operation of the control system for this accelerator relies upon interactive interface builder and diagram/editor type tools, as well as a run-time environment for the constructed displays which communicate with the physical machine via network connections. This paper discusses our experience with several commercial CUI builders, the inadequacies found in these, motivation for the development of an application- specific builder, and design and implementation strategies employed in the development of our own Man-Machine Interface builder. 5 refs

  9. The ELETTRA man-machine interface

    International Nuclear Information System (INIS)

    Potepan, F.; Surace, G.; Mignacco, M.

    1992-01-01

    ELETTRA is a third generation Synchrotron Light Source under construction in Trieste (Italy), with beam energies between 1.5 and 2 GeV. Two networks connect three layers of computers in a fully distributed architecture. An ergonomic and unified approach in the realization of the human interface for the ELETTRA storage ring has led to the adoption of artificial reality criteria for the definition of the system synoptic representation and user interaction. Users can navigate inside a graphic database of the whole system and interactively edit specific virtual control panels to operate on the controlled equipment. UNIX workstations with extended graphic capabilities as operator consoles are used in the implementation of the PSI (Programmable Synoptic Interface), that was developed on top of X11 and PHIGS standards. (author)

  10. Competing and collaborating brains: multi-brain computer interfacing

    NARCIS (Netherlands)

    Nijholt, Antinus; Hassanieu, Aboul Ella; Azar, Ahmad Taher

    2015-01-01

    In this chapter we survey the possibilities of brain-computer interface applications that assume two or more users, where at least one of the users’ brain activity is used as input to the application. Such ‘applications’ were already explored by artists who introduced artistic EEG applications in

  11. Brain-Computer Interfaces in Medicine

    Science.gov (United States)

    Shih, Jerry J.; Krusienski, Dean J.; Wolpaw, Jonathan R.

    2012-01-01

    Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function. PMID:22325364

  12. A new man-machine-interface at BESSY

    International Nuclear Information System (INIS)

    Mueller, R.; Doll, H.D.; Donasch, I.J.; Marxen, H.; Pause, H.

    1991-01-01

    A UIMS (user interface management system) has been developed, that is completely based on non-proprietary software. Central part of the UIMS are processes (mapper) that act as universal X-clients for each specified X-server. Mapper (graphic server) and applications (graphic clients) exchange requests by an event driven interface. The communication protocol is free from any graphical information. The most powerful mapper client is a form interpreter, that can be programmed to act as an equipment access server. Mapper and form interpreter allow to compose control panels and synoptic views of the machine with statements in a simple and comprehensible UIDL (user interface definition language)

  13. Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review

    Directory of Open Access Journals (Sweden)

    Usman Ghafoor

    2017-10-01

    Full Text Available For those individuals with upper-extremity amputation, a daily normal living activity is no longer possible or it requires additional effort and time. With the aim of restoring their sensory and motor functions, theoretical and technological investigations have been carried out in the field of neuroprosthetic systems. For transmission of sensory feedback, several interfacing modalities including indirect (non-invasive, direct-to-peripheral-nerve (invasive, and cortical stimulation have been applied. Peripheral nerve interfaces demonstrate an edge over the cortical interfaces due to the sensitivity in attaining cortical brain signals. The peripheral nerve interfaces are highly dependent on interface designs and are required to be biocompatible with the nerves to achieve prolonged stability and longevity. Another criterion is the selection of nerves that allows minimal invasiveness and damages as well as high selectivity for a large number of nerve fascicles. In this paper, we review the nerve-machine interface modalities noted above with more focus on peripheral nerve interfaces, which are responsible for provision of sensory feedback. The invasive interfaces for recording and stimulation of electro-neurographic signals include intra-fascicular, regenerative-type interfaces that provide multiple contact channels to a group of axons inside the nerve and the extra-neural-cuff-type interfaces that enable interaction with many axons around the periphery of the nerve. Section Current Prosthetic Technology summarizes the advancements made to date in the field of neuroprosthetics toward the achievement of a bidirectional nerve-machine interface with more focus on sensory feedback. In the Discussion section, the authors propose a hybrid interface technique for achieving better selectivity and long-term stability using the available nerve interfacing techniques.

  14. Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review

    Science.gov (United States)

    Ghafoor, Usman; Kim, Sohee; Hong, Keum-Shik

    2017-01-01

    For those individuals with upper-extremity amputation, a daily normal living activity is no longer possible or it requires additional effort and time. With the aim of restoring their sensory and motor functions, theoretical and technological investigations have been carried out in the field of neuroprosthetic systems. For transmission of sensory feedback, several interfacing modalities including indirect (non-invasive), direct-to-peripheral-nerve (invasive), and cortical stimulation have been applied. Peripheral nerve interfaces demonstrate an edge over the cortical interfaces due to the sensitivity in attaining cortical brain signals. The peripheral nerve interfaces are highly dependent on interface designs and are required to be biocompatible with the nerves to achieve prolonged stability and longevity. Another criterion is the selection of nerves that allows minimal invasiveness and damages as well as high selectivity for a large number of nerve fascicles. In this paper, we review the nerve-machine interface modalities noted above with more focus on peripheral nerve interfaces, which are responsible for provision of sensory feedback. The invasive interfaces for recording and stimulation of electro-neurographic signals include intra-fascicular, regenerative-type interfaces that provide multiple contact channels to a group of axons inside the nerve and the extra-neural-cuff-type interfaces that enable interaction with many axons around the periphery of the nerve. Section Current Prosthetic Technology summarizes the advancements made to date in the field of neuroprosthetics toward the achievement of a bidirectional nerve-machine interface with more focus on sensory feedback. In the Discussion section, the authors propose a hybrid interface technique for achieving better selectivity and long-term stability using the available nerve interfacing techniques. PMID:29163122

  15. Optimal Achievable Encoding for Brain Machine Interface

    Science.gov (United States)

    2017-12-22

    dictionary-based encoding approach to translate a visual image into sequential patterns of electrical stimulation in real time , in a manner that...including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and...networks, and by applying linear decoding to complete recorded populations of retinal ganglion cells for the first time . Third, we developed a greedy

  16. The CANDU man-machine interface and simulator training

    International Nuclear Information System (INIS)

    Hinchley, E.M.; Yanofsky, N.

    1982-09-01

    The most significant features of the man-machine interface for CANDU power stations are the extensive use of computer-driven colour graphics displays and the small number of manual controls. The man-machine interface in CANDU stations is designed to present the operator with concise, easy-to-understand information. Future developments in the use of computers in safety shutdown systems, and the use of data highway technologies in plant regulating systems will present special requirements and new opportunities in the application of human factors engineering to the control room. Good man-machine interaction depends on operator training as much as on control room design. In Canada computerized training simulators, which indicate plant response to operator action, are being introducted for operator training. Such simulators support training in normal operation of all plant systems and also in the fault management tasks following malfunctions

  17. Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future.

    Science.gov (United States)

    Huggins, Jane E; Guger, Christoph; Ziat, Mounia; Zander, Thorsten O; Taylor, Denise; Tangermann, Michael; Soria-Frisch, Aureli; Simeral, John; Scherer, Reinhold; Rupp, Rüdiger; Ruffini, Giulio; Robinson, Douglas K R; Ramsey, Nick F; Nijholt, Anton; Müller-Putz, Gernot; McFarland, Dennis J; Mattia, Donatella; Lance, Brent J; Kindermans, Pieter-Jan; Iturrate, Iñaki; Herff, Christian; Gupta, Disha; Do, An H; Collinger, Jennifer L; Chavarriaga, Ricardo; Chase, Steven M; Bleichner, Martin G; Batista, Aaron; Anderson, Charles W; Aarnoutse, Erik J

    2017-01-01

    The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

  18. The APS intranet as a man-machine interface

    International Nuclear Information System (INIS)

    Ciarlette, D.; Gerig, R.; McDowell, W.

    1997-01-01

    The Advanced Photon Source at Argonne National Laboratory has implemented a number of methods for people to interact with the accelerator systems. The accelerator operators use Sun workstations running MEDM and WCL to interface interactively with the accelerator, however, many people need to view information rather than interact with the machine. One of the most common interfaces for viewing information at the Advanced Photon Source is the World Wide Web. Information such as operations logbook entries, machine status updates, and displays of archived and current data are easily available to APS personnel. This interface between people and the accelerator has proven to be quite useful. Because the Intranet is operating-system independent and inherently unidirectional, ensuring the prevention of unauthorized or accidental control of the accelerators is straightforward

  19. [The current state of the brain-computer interface problem].

    Science.gov (United States)

    Shurkhay, V A; Aleksandrova, E V; Potapov, A A; Goryainov, S A

    2015-01-01

    It was only 40 years ago that the first PC appeared. Over this period, rather short in historical terms, we have witnessed the revolutionary changes in lives of individuals and the entire society. Computer technologies are tightly connected with any field, either directly or indirectly. We can currently claim that computers are manifold superior to a human mind in terms of a number of parameters; however, machines lack the key feature: they are incapable of independent thinking (like a human). However, the key to successful development of humankind is collaboration between the brain and the computer rather than competition. Such collaboration when a computer broadens, supplements, or replaces some brain functions is known as the brain-computer interface. Our review focuses on real-life implementation of this collaboration.

  20. The Brain-Computer Interface Cycle

    NARCIS (Netherlands)

    Gerven, Marcel; Farquhar, Jason; Schaefer, Rebecca; Vlek, Rutger; Geuze, Jeroen; Nijholt, Antinus; Ramsay, Nick; Haselager, Pim; Vuurpijl, Louis; Gielen, Stan; Desain, Peter

    2009-01-01

    Brain–computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of

  1. Brain-computer interfaces in neurological rehabilitation.

    Science.gov (United States)

    Daly, Janis J; Wolpaw, Jonathan R

    2008-11-01

    Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.

  2. Human Machine Interfaces for Teleoperators and Virtual Environments

    Science.gov (United States)

    Durlach, Nathaniel I. (Compiler); Sheridan, Thomas B. (Compiler); Ellis, Stephen R. (Compiler)

    1991-01-01

    In Mar. 1990, a meeting organized around the general theme of teleoperation research into virtual environment display technology was conducted. This is a collection of conference-related fragments that will give a glimpse of the potential of the following fields and how they interplay: sensorimotor performance; human-machine interfaces; teleoperation; virtual environments; performance measurement and evaluation methods; and design principles and predictive models.

  3. How artificial intelligence can help [man-machine interface

    International Nuclear Information System (INIS)

    Elm, W.C.

    1988-01-01

    The operator is ultimately responsible for the safe and economical operation of the plant, and must evaluate the accuracy of any system-recommended action or other output. Decision support systems offer a means to improve the man-machine interface by explicitly supporting operator problem solving, rather than complicating decision-making by the need to request an explanation of the rationale behind an expert system's advice during a high stress situation. (author)

  4. Brain Computer Interface on Track to Home

    OpenAIRE

    Miralles, Felip; Vargiu, Eloisa; Dauwalder, Stefan; Solà, Marc; Müller-Putz, Gernot; Wriessnegger, Selina C.; Pinegger, Andreas; Kübler, Andrea; Halder, Sebastian; Käthner, Ivo; Martin, Suzanne; Daly, Jean; Armstrong, Elaine; Guger, Christoph; Hintermüller, Christoph

    2015-01-01

    The novel BackHome system offers individuals with disabilities a range of useful services available via brain-computer interfaces (BCIs), to help restore their independence. This is the time such technology is ready to be deployed in the real world, that is, at the target end users' home. This has been achieved by the development of practical electrodes, easy to use software, and delivering telemonitoring and home support capabilities which have been conceived, implemented, and tested within ...

  5. Neuromechanism study of insect-machine interface: flight control by neural electrical stimulation.

    Directory of Open Access Journals (Sweden)

    Huixia Zhao

    Full Text Available The insect-machine interface (IMI is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L. via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe, ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee-machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control.

  6. Neuromechanism study of insect-machine interface: flight control by neural electrical stimulation.

    Science.gov (United States)

    Zhao, Huixia; Zheng, Nenggan; Ribi, Willi A; Zheng, Huoqing; Xue, Lei; Gong, Fan; Zheng, Xiaoxiang; Hu, Fuliang

    2014-01-01

    The insect-machine interface (IMI) is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L.) via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe), ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee-machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control.

  7. Neuromechanism Study of Insect–Machine Interface: Flight Control by Neural Electrical Stimulation

    Science.gov (United States)

    Zhao, Huixia; Zheng, Nenggan; Ribi, Willi A.; Zheng, Huoqing; Xue, Lei; Gong, Fan; Zheng, Xiaoxiang; Hu, Fuliang

    2014-01-01

    The insect–machine interface (IMI) is a novel approach developed for man-made air vehicles, which directly controls insect flight by either neuromuscular or neural stimulation. In our previous study of IMI, we induced flight initiation and cessation reproducibly in restrained honeybees (Apis mellifera L.) via electrical stimulation of the bilateral optic lobes. To explore the neuromechanism underlying IMI, we applied electrical stimulation to seven subregions of the honeybee brain with the aid of a new method for localizing brain regions. Results showed that the success rate for initiating honeybee flight decreased in the order: α-lobe (or β-lobe), ellipsoid body, lobula, medulla and antennal lobe. Based on a comparison with other neurobiological studies in honeybees, we propose that there is a cluster of descending neurons in the honeybee brain that transmits neural excitation from stimulated brain areas to the thoracic ganglia, leading to flight behavior. This neural circuit may involve the higher-order integration center, the primary visual processing center and the suboesophageal ganglion, which is also associated with a possible learning and memory pathway. By pharmacologically manipulating the electrically stimulated honeybee brain, we have shown that octopamine, rather than dopamine, serotonin and acetylcholine, plays a part in the circuit underlying electrically elicited honeybee flight. Our study presents a new brain stimulation protocol for the honeybee–machine interface and has solved one of the questions with regard to understanding which functional divisions of the insect brain participate in flight control. It will support further studies to uncover the involved neurons inside specific brain areas and to test the hypothesized involvement of a visual learning and memory pathway in IMI flight control. PMID:25409523

  8. Brain computer interface for operating a robot

    Science.gov (United States)

    Nisar, Humaira; Balasubramaniam, Hari Chand; Malik, Aamir Saeed

    2013-10-01

    A Brain-Computer Interface (BCI) is a hardware/software based system that translates the Electroencephalogram (EEG) signals produced by the brain activity to control computers and other external devices. In this paper, we will present a non-invasive BCI system that reads the EEG signals from a trained brain activity using a neuro-signal acquisition headset and translates it into computer readable form; to control the motion of a robot. The robot performs the actions that are instructed to it in real time. We have used the cognitive states like Push, Pull to control the motion of the robot. The sensitivity and specificity of the system is above 90 percent. Subjective results show a mixed trend of the difficulty level of the training activities. The quantitative EEG data analysis complements the subjective results. This technology may become very useful for the rehabilitation of disabled and elderly people.

  9. A man-machine interface for simulation purposes

    International Nuclear Information System (INIS)

    Galan, J.M.; Almeida, J.M.; Duque, J.M.

    1990-01-01

    This paper describes a man-machine interface, to link any simulator program to a control panel made up of an array of color graphic screens. This task is performed by a communications program which extracts the data from the simulator program, sorts the data, and sends it, through the network, to graphics programs running in workstations. There, the data is displayed on color graphic screens. A graphics package was specifically designed to assist the user handling of complex circuits, and the formatting and display of data on the screens. (author)

  10. A brain computer interface-based explorer.

    Science.gov (United States)

    Bai, Lijuan; Yu, Tianyou; Li, Yuanqing

    2015-04-15

    In recent years, various applications of brain computer interfaces (BCIs) have been studied. In this paper, we present a hybrid BCI combining P300 and motor imagery to operate an explorer. Our system is mainly composed of a BCI mouse, a BCI speller and an explorer. Through this system, the user can access his computer and manipulate (open, close, copy, paste, and delete) files such as documents, pictures, music, movies and so on. The system has been tested with five subjects, and the experimental results show that the explorer can be successfully operated according to subjects' intentions. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Brain-computer interface using P300 and virtual reality: A gaming approach for treating ADHD

    DEFF Research Database (Denmark)

    Rohani, Darius Adam; Sørensen, Helge Bjarup Dissing; Puthusserypady, Sadasivan

    2014-01-01

    This paper presents a novel brain-computer interface (BCI) system aiming at the rehabilitation of attention-deficit/hyperactive disorder in children. It uses the P300 potential in a series of feedback games to improve the subjects' attention. We applied a support vector machine (SVM) using temporal...

  12. Outline of human machine interface at Rokkasho reprocessing plant

    International Nuclear Information System (INIS)

    Niioka, T.; Miyazaki, T.; Fujita, D.; Esashika, A.; Yoshida, Y.; Nakamura, W.; Tochigi, T.; Yoshimoto, A.; Yokoi, M.

    2006-01-01

    The Japan Nuclear Fuel Limited (JNFL) has been performing the active tests since the end of March, 2006, for its Rokkasho Reprocessing Plant using the spent fuels retrieved from the Light Water Reactors. At the early stage of the tests relatively low burn-up fuels have been used, and the burn-up will be increased at later stages until the start of commercial operation planned next year. The plant is operated from the main control room in the Control Building, where two types of operator consoles are located for plant monitoring and operation. The Operator Interface Station (OIS) driven by computer systems is chiefly used for instrumentation and control for production activities during normal operation. In addition to this, safety panels composed of hardware circuits are installed for nuclear safety functions such as criticality safety management, explosion protection, and confinement of radioactive materials. This paper outlines the Human Machine Interface features applied to the Rokkasho Reprocessing Plant. (authors)

  13. The brain-computer interface cycle.

    Science.gov (United States)

    van Gerven, Marcel; Farquhar, Jason; Schaefer, Rebecca; Vlek, Rutger; Geuze, Jeroen; Nijholt, Anton; Ramsey, Nick; Haselager, Pim; Vuurpijl, Louis; Gielen, Stan; Desain, Peter

    2009-08-01

    Brain-computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues and state-of-the-art results. Moreover, we will develop a vision on how recently obtained results may contribute to new insights in neurocognition and, in particular, in the neural representation of perceived stimuli, intended actions and emotions. Now is the right time to explore what can be gained by embracing real-time, online BCI and by adding it to the set of experimental tools already available to the cognitive neuroscientist. We close by pointing out some unresolved issues and present our view on how BCI could become an important new tool for probing human cognition.

  14. Brain-Computer Interface Spellers: A Review.

    Science.gov (United States)

    Rezeika, Aya; Benda, Mihaly; Stawicki, Piotr; Gembler, Felix; Saboor, Abdul; Volosyak, Ivan

    2018-03-30

    A Brain-Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

  15. Spectral Transfer Learning using Information Geometry for a User-Independent Brain-Computer Interface

    OpenAIRE

    Nicholas Roy Waytowich; Nicholas Roy Waytowich; Vernon Lawhern; Vernon Lawhern; Addison Bohannon; Addison Bohannon; Kenneth Ball; Brent Lance

    2016-01-01

    Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry and recreation. However, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter- individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this p...

  16. Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

    OpenAIRE

    Waytowich, Nicholas R.; Lawhern, Vernon J.; Bohannon, Addison W.; Ball, Kenneth R.; Lance, Brent J.

    2016-01-01

    Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this p...

  17. Visualization tool for human-machine interface designers

    Science.gov (United States)

    Prevost, Michael P.; Banda, Carolyn P.

    1991-06-01

    As modern human-machine systems continue to grow in capabilities and complexity, system operators are faced with integrating and managing increased quantities of information. Since many information components are highly related to each other, optimizing the spatial and temporal aspects of presenting information to the operator has become a formidable task for the human-machine interface (HMI) designer. The authors describe a tool in an early stage of development, the Information Source Layout Editor (ISLE). This tool is to be used for information presentation design and analysis; it uses human factors guidelines to assist the HMI designer in the spatial layout of the information required by machine operators to perform their tasks effectively. These human factors guidelines address such areas as the functional and physical relatedness of information sources. By representing these relationships with metaphors such as spring tension, attractors, and repellers, the tool can help designers visualize the complex constraint space and interacting effects of moving displays to various alternate locations. The tool contains techniques for visualizing the relative 'goodness' of a configuration, as well as mechanisms such as optimization vectors to provide guidance toward a more optimal design. Also available is a rule-based design checker to determine compliance with selected human factors guidelines.

  18. Brain Computer Interfaces for Enhanced Interaction with Mobile Robot Agents

    Science.gov (United States)

    2016-07-27

    SECURITY CLASSIFICATION OF: Brain Computer Interfaces (BCIs) show great potential in allowing humans to interact with computational environments in a...Distribution Unlimited UU UU UU UU 27-07-2016 17-Sep-2013 16-Sep-2014 Final Report: Brain Computer Interfaces for Enhanced Interactions with Mobile Robot...published in peer-reviewed journals: Number of Papers published in non peer-reviewed journals: Final Report: Brain Computer Interfaces for Enhanced

  19. Multimodal 2D Brain Computer Interface.

    Science.gov (United States)

    Almajidy, Rand K; Boudria, Yacine; Hofmann, Ulrich G; Besio, Walter; Mankodiya, Kunal

    2015-08-01

    In this work we used multimodal, non-invasive brain signal recording systems, namely Near Infrared Spectroscopy (NIRS), disc electrode electroencephalography (EEG) and tripolar concentric ring electrodes (TCRE) electroencephalography (tEEG). 7 healthy subjects participated in our experiments to control a 2-D Brain Computer Interface (BCI). Four motor imagery task were performed, imagery motion of the left hand, the right hand, both hands and both feet. The signal slope (SS) of the change in oxygenated hemoglobin concentration measured by NIRS was used for feature extraction while the power spectrum density (PSD) of both EEG and tEEG in the frequency band 8-30Hz was used for feature extraction. Linear Discriminant Analysis (LDA) was used to classify different combinations of the aforementioned features. The highest classification accuracy (85.2%) was achieved by using features from all the three brain signals recording modules. The improvement in classification accuracy was highly significant (p = 0.0033) when using the multimodal signals features as compared to pure EEG features.

  20. fNIRS-based brain-computer interfaces: a review

    Directory of Open Access Journals (Sweden)

    Noman eNaseer

    2015-01-01

    Full Text Available A brain-computer interface (BCI is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis, multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine, hidden Markov model, artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.

  1. Adaptive Training and Collective Decision Support Based on Man-Machine Interface

    Science.gov (United States)

    2016-03-02

    Based on Man -machine Interface The views, opinions and/or findings contained in this report are those of the author(s) and should not contrued as an...ADDRESS (ES) U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 adaptive training, EEG, man -machine interface...non peer-reviewed journals: Final Report: Adaptive Training and Collective Decision Support Based on Man -machine Interface Report Title The existence of

  2. Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future.

    Science.gov (United States)

    Huggins, Jane E; Guger, Christoph; Allison, Brendan; Anderson, Charles W; Batista, Aaron; Brouwer, Anne-Marie A-M; Brunner, Clemens; Chavarriaga, Ricardo; Fried-Oken, Melanie; Gunduz, Aysegul; Gupta, Disha; Kübler, Andrea; Leeb, Robert; Lotte, Fabien; Miller, Lee E; Müller-Putz, Gernot; Rutkowski, Tomasz; Tangermann, Michael; Thompson, David Edward

    2014-01-01

    The Fifth International Brain-Computer Interface (BCI) Meeting met June 3-7 th , 2013 at the Asilomar Conference Grounds, Pacific Grove, California. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and certification, types of brain activity to use for BCI, recording methods, the effects of plasticity, special interest topics in BCIs applications, and future BCI directions. BCI research is well established and transitioning to practical use to benefit people with physical impairments. At the same time, new applications are being explored, both for people with physical impairments and beyond. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and high-lighting important issues for future research and development.

  3. Design Control Systems of Human Machine Interface in the NTVS-2894 Seat Grinder Machine to Increase the Productivity

    Science.gov (United States)

    Ardi, S.; Ardyansyah, D.

    2018-02-01

    In the Manufacturing of automotive spare parts, increased sales of vehicles is resulted in increased demand for production of engine valve of the customer. To meet customer demand, we carry out improvement and overhaul of the NTVS-2894 seat grinder machine on a machining line. NTVS-2894 seat grinder machine has been decreased machine productivity, the amount of trouble, and the amount of downtime. To overcome these problems on overhaul the NTVS-2984 seat grinder machine include mechanical and programs, is to do the design and manufacture of HMI (Human Machine Interface) GP-4501T program. Because of the time prior to the overhaul, NTVS-2894 seat grinder machine does not have a backup HMI (Human Machine Interface) program. The goal of the design and manufacture in this program is to improve the achievement of production, and allows an operator to operate beside it easier to troubleshoot the NTVS-2894 seat grinder machine thereby reducing downtime on the NTVS-2894 seat grinder machine. The results after the design are HMI program successfully made it back, machine productivity increased by 34.8%, the amount of trouble, and downtime decreased 40% decrease from 3,160 minutes to 1,700 minutes. The implication of our design, it could facilitate the operator in operating machine and the technician easer to maintain and do the troubleshooting the machine problems.

  4. Using human brain activity to guide machine learning.

    Science.gov (United States)

    Fong, Ruth C; Scheirer, Walter J; Cox, David D

    2018-03-29

    Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

  5. Development of an evaluation technique for human-machine interface

    International Nuclear Information System (INIS)

    Min, Dae Hwan; Koo, Sang Hui; Ahn, Won Yeong; Ryu, Yeong Shin

    1997-07-01

    The purpose of this study is two-fold : firstly to establish an evaluation technique for HMI(Human Machine Interface) in NPPs(Nuclear Power Plants) and secondly to develop an architecture of a support system which can be used for the evaluation of HMI. In order to establish an evaluation technique, this study conducted literature review on basic theories of cognitive science studies and summarized the cognitive characteristics of humans. This study also surveyed evaluation techniques of HMI in general, and reviewed studies on the evaluation of HMI in NPPs. On the basis of this survey, the study established a procedure for the evaluation of HMI in NPPs in Korea and laid a foundation for empirical verification

  6. Development of an evaluation technique for human-machine interface

    Energy Technology Data Exchange (ETDEWEB)

    Min, Dae Hwan; Koo, Sang Hui; Ahn, Won Yeong; Ryu, Yeong Shin [Korea Univ., Seoul (Korea, Republic of)

    1997-07-15

    The purpose of this study is two-fold : firstly to establish an evaluation technique for HMI(Human Machine Interface) in NPPs(Nuclear Power Plants) and secondly to develop an architecture of a support system which can be used for the evaluation of HMI. In order to establish an evaluation technique, this study conducted literature review on basic theories of cognitive science studies and summarized the cognitive characteristics of humans. This study also surveyed evaluation techniques of HMI in general, and reviewed studies on the evaluation of HMI in NPPs. On the basis of this survey, the study established a procedure for the evaluation of HMI in NPPs in Korea and laid a foundation for empirical verification.

  7. The Engineering Design of Man-Machine Interface for RTS

    International Nuclear Information System (INIS)

    Yenn, T.-C.

    2002-01-01

    The purpose of this paper is to present the engineering design of the advanced Man-Machine Interface (MMI) of the Integrated system for Radwaste Treatment and Storage (RTS) facility in Institute of Nuclear Energy Research (INER) Taiwan, ROC. To build the RTS, a multi-function radwaste facility with a total storage of about 10,000 drums, is a five-year project starting in 2000 including intermediate activity waste treatment and combustible waste storage. The completed engineering design of the MMI is based on proven technologies and digital control systems, enhancing the radwaste management efficiency and reliability of operator's performance as well as assuring the dose exposure of personnel meeting the regulation standard. Over past few years, INER has accumulated extensive experience in the area of radwaste treatment and storage. Therefore, we are confident that we will complete this project with fulfillment of the requirements of RTS

  8. Brain-Computer Interfacing Embedded in Intelligent and Affective Systems

    NARCIS (Netherlands)

    Nijholt, Antinus

    In this talk we survey recent research views on non-traditional brain-computer interfaces (BCI). That is, interfaces that can process brain activity input, but that are designed for the ‘general population’, rather than for clinical purposes. Control of applications can be made more robust by fusing

  9. Body machine interfaces for neuromotor rehabilitation: a case study.

    Science.gov (United States)

    Pierella, Camilla; Abdollahi, Farnaz; Farshchiansadegh, Ali; Pedersen, Jessica; Chen, David; Mussa-Ivaldi, Ferdinando A; Casadio, Maura

    2014-01-01

    High-level spinal cord injury (SCI) survivors face every day two related problems: recovering motor skills and regaining functional independence. Body machine interfaces (BoMIs) empower people with sever motor disabilities with the ability to control an external device, but they also offer the opportunity to focus concurrently on achieving rehabilitative goals. In this study we developed a portable, and low-cost BoMI that addresses both problems. The BoMI remaps the user's residual upper body mobility to the two coordinates of a cursor on a computer monitor. By controlling the cursor, the user can perform functional tasks, such as entering text and playing games. This framework also allows the mapping between the body and the cursor space to be modified, gradually challenging the user to exercise more impaired movements. With this approach, we were able to change the behavior of our SCI subject, who initially used almost exclusively his less impaired degrees of freedom - on the left side - for controlling the BoMI. At the end of the few practice sessions he had restored symmetry between left and right side of the body, with an increase of mobility and strength of all the degrees of freedom involved in the control of the interface. This is the first proof of concept that our BoMI can be used to control assistive devices and reach specific rehabilitative goals simultaneously.

  10. On robust parameter estimation in brain-computer interfacing

    Science.gov (United States)

    Samek, Wojciech; Nakajima, Shinichi; Kawanabe, Motoaki; Müller, Klaus-Robert

    2017-12-01

    Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain-computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.

  11. Touch-based Brain Computer Interfaces: State of the art

    NARCIS (Netherlands)

    Erp, J.B.F. van; Brouwer, A.M.

    2014-01-01

    Brain Computer Interfaces (BCIs) rely on the user's brain activity to control equipment or computer devices. Many BCIs are based on imagined movement (called active BCIs) or the fact that brain patterns differ in reaction to relevant or attended stimuli in comparison to irrelevant or unattended

  12. Towards psychologically adaptive brain-computer interfaces

    Science.gov (United States)

    Myrden, A.; Chau, T.

    2016-12-01

    Objective. Brain-computer interface (BCI) performance is sensitive to short-term changes in psychological states such as fatigue, frustration, and attention. This paper explores the design of a BCI that can adapt to these short-term changes. Approach. Eleven able-bodied individuals participated in a study during which they used a mental task-based EEG-BCI to play a simple maze navigation game while self-reporting their perceived levels of fatigue, frustration, and attention. In an offline analysis, a regression algorithm was trained to predict changes in these states, yielding Pearson correlation coefficients in excess of 0.45 between the self-reported and predicted states. Two means of fusing the resultant mental state predictions with mental task classification were investigated. First, single-trial mental state predictions were used to predict correct classification by the BCI during each trial. Second, an adaptive BCI was designed that retrained a new classifier for each testing sample using only those training samples for which predicted mental state was similar to that predicted for the current testing sample. Main results. Mental state-based prediction of BCI reliability exceeded chance levels. The adaptive BCI exhibited significant, but practically modest, increases in classification accuracy for five of 11 participants and no significant difference for the remaining six despite a smaller average training set size. Significance. Collectively, these findings indicate that adaptation to psychological state may allow the design of more accurate BCIs.

  13. Brain Computer Interface on Track to Home.

    Science.gov (United States)

    Miralles, Felip; Vargiu, Eloisa; Dauwalder, Stefan; Solà, Marc; Müller-Putz, Gernot; Wriessnegger, Selina C; Pinegger, Andreas; Kübler, Andrea; Halder, Sebastian; Käthner, Ivo; Martin, Suzanne; Daly, Jean; Armstrong, Elaine; Guger, Christoph; Hintermüller, Christoph; Lowish, Hannah

    2015-01-01

    The novel BackHome system offers individuals with disabilities a range of useful services available via brain-computer interfaces (BCIs), to help restore their independence. This is the time such technology is ready to be deployed in the real world, that is, at the target end users' home. This has been achieved by the development of practical electrodes, easy to use software, and delivering telemonitoring and home support capabilities which have been conceived, implemented, and tested within a user-centred design approach. The final BackHome system is the result of a 3-year long process involving extensive user engagement to maximize effectiveness, reliability, robustness, and ease of use of a home based BCI system. The system is comprised of ergonomic and hassle-free BCI equipment; one-click software services for Smart Home control, cognitive stimulation, and web browsing; and remote telemonitoring and home support tools to enable independent home use for nonexpert caregivers and users. BackHome aims to successfully bring BCIs to the home of people with limited mobility to restore their independence and ultimately improve their quality of life.

  14. Brain Computer Interface on Track to Home

    Directory of Open Access Journals (Sweden)

    Felip Miralles

    2015-01-01

    Full Text Available The novel BackHome system offers individuals with disabilities a range of useful services available via brain-computer interfaces (BCIs, to help restore their independence. This is the time such technology is ready to be deployed in the real world, that is, at the target end users’ home. This has been achieved by the development of practical electrodes, easy to use software, and delivering telemonitoring and home support capabilities which have been conceived, implemented, and tested within a user-centred design approach. The final BackHome system is the result of a 3-year long process involving extensive user engagement to maximize effectiveness, reliability, robustness, and ease of use of a home based BCI system. The system is comprised of ergonomic and hassle-free BCI equipment; one-click software services for Smart Home control, cognitive stimulation, and web browsing; and remote telemonitoring and home support tools to enable independent home use for nonexpert caregivers and users. BackHome aims to successfully bring BCIs to the home of people with limited mobility to restore their independence and ultimately improve their quality of life.

  15. Papers from the Fifth International Brain-Computer Interface Meeting

    Science.gov (United States)

    Huggins, Jane E.; Wolpaw, Jonathan R.

    2014-06-01

    Brain-computer interfaces (BCIs), also known as brain-machine interfaces (BMIs), translate brain activity into new outputs that replace, restore, enhance, supplement or improve natural brain outputs. BCI research and development has grown rapidly for the past two decades. It is beginning to provide useful communication and control capacities to people with severe neuromuscular disabilities; and it is expanding into new areas such as neurorehabilitation that may greatly increase its clinical impact. At the same time, significant challenges remain, particularly in regard to translating laboratory advances into clinical use. The papers in this special section report some of the work presented at the Fifth International BCI Meeting held on 3-7 June 2013 at the Asilomar Conference Center in Pacific Grove, California, USA. Like its predecessors over the past 15 years, this meeting was supported by the National Institutes of Health, the National Science Foundation, and a variety of other governmental and private sponsors [1]. This fifth meeting was organized and managed by a program committee of BCI researchers from throughout the world [2]. It retained the distinctive retreat-style format developed by the Wadsworth Center researchers who organized and managed the first four meetings. The 301 attendees came from 165 research groups in 29 countries; 37% were students or postdoctoral fellows. Of more than 200 extended abstracts submitted for peer review, 25 were selected for oral presentation [3], and 181 were presented as posters [4] and published in the open-access conference proceedings [5]. The meeting featured 19 highly interactive workshops [6] covering the broad spectrum of BCI research and development, as well as many demonstrations of BCI systems and associated technology. Like the first four meetings, this one included attendees and embraced topics from across the broad spectrum of disciplines essential to effective BCI research and development, including

  16. Man--machine interface issues for space nuclear power systems

    International Nuclear Information System (INIS)

    Nelson, W.R.; Haugset, K.

    1991-01-01

    The deployment of nuclear reactors in space necessitates an entirely new set of guidelines for the design of the man--machine interface (MMI) when compared to earth-based applications such as commerical nuclear power plants. Although the design objectives of earth- and space-based nuclear power systems are the same, that is, to produce electrical power, the differences in the application environments mean that the operator's role will be significantly different for space-based systems. This paper explores the issues associated with establishing the necessary MMI guidelines for space nuclear power systems. The generic human performance requirements for space-based systems are described, and the operator roles that are utilized for the operation of current and advanced earth-based reactors are briefly summarized. The development of a prototype advanced control room, the Integrated Surveillance and Control System (ISACS) at the Organization for Economic Cooperation and Development (OECD) Halden Reactor Project is introduced. Finally, preliminary ideas for the use of the ISACS system as a test bed for establishing MMI guidelines for space nuclear systems are presented

  17. Human Machine Interfaces for Teleoperators and Virtual Environments Conference

    Science.gov (United States)

    1990-01-01

    In a teleoperator system the human operator senses, moves within, and operates upon a remote or hazardous environment by means of a slave mechanism (a mechanism often referred to as a teleoperator). In a virtual environment system the interactive human machine interface is retained but the slave mechanism and its environment are replaced by a computer simulation. Video is replaced by computer graphics. The auditory and force sensations imparted to the human operator are similarly computer generated. In contrast to a teleoperator system, where the purpose is to extend the operator's sensorimotor system in a manner that facilitates exploration and manipulation of the physical environment, in a virtual environment system, the purpose is to train, inform, alter, or study the human operator to modify the state of the computer and the information environment. A major application in which the human operator is the target is that of flight simulation. Although flight simulators have been around for more than a decade, they had little impact outside aviation presumably because the application was so specialized and so expensive.

  18. Affective Man-Machine Interface: Unveiling Human Emotions through Biosignals

    Science.gov (United States)

    van den Broek, Egon L.; Lisý, Viliam; Janssen, Joris H.; Westerink, Joyce H. D. M.; Schut, Marleen H.; Tuinenbreijer, Kees

    As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological proce-sses, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals.

  19. Framework for man-machine interface design evaluation system considering cognitive factor

    International Nuclear Information System (INIS)

    Itoh, Toru; Sasaki, Kazunori; Yoshikawa, Hidekazu; Takahashi, Makoto; Furuta, Tomihiko.

    1994-01-01

    It is necessary to improve human reliability in order to gain a higher reliability of the total plant system taking an account of development of plant automation and improvement of machine reliability. Therefore, the role of the man-machine system will come to be important. Accordingly, the evaluation of the man-machine system design information is desired in order to solve the mismatch problem between plant information presented by the man-machine system and information required by the operator comprehensively. This paper discusses required functions and software framework for the man-machine interface design evaluation system. The man-machine interface design evaluation system has features to extract the potential matters which are inherent on the design information of man-machine system by simulating the operator behavior, the plant system and the man-machine system, considering the operator's cognitive performance and time dependency. (author)

  20. Non-invasive brain-to-brain interface (BBI: establishing functional links between two brains.

    Directory of Open Access Journals (Sweden)

    Seung-Schik Yoo

    Full Text Available Transcranial focused ultrasound (FUS is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI. In conjunction with the use of brain-to-computer interface (BCI techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat, thus creating a brain-to-brain interface (BBI. The implementation was aimed to non-invasively translate the human volunteer's intention to stimulate a rat's brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer's intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0±3.0% accuracy, with a time delay of 1.59±1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.

  1. Applications of Brain–Machine Interface Systems in Stroke Recovery and Rehabilitation

    Science.gov (United States)

    Francisco, Gerard E.; Contreras-Vidal, Jose L.

    2014-01-01

    Stroke is a leading cause of disability, significantly impacting the quality of life (QOL) in survivors, and rehabilitation remains the mainstay of treatment in these patients. Recent engineering and technological advances such as brain-machine interfaces (BMI) and robotic rehabilitative devices are promising to enhance stroke neu-rorehabilitation, to accelerate functional recovery and improve QOL. This review discusses the recent applications of BMI and robotic-assisted rehabilitation in stroke patients. We present the framework for integrated BMI and robotic-assisted therapies, and discuss their potential therapeutic, assistive and diagnostic functions in stroke rehabilitation. Finally, we conclude with an outlook on the potential challenges and future directions of these neurotechnologies, and their impact on clinical rehabilitation. PMID:25110624

  2. Different protein-protein interface patterns predicted by different machine learning methods.

    Science.gov (United States)

    Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi

    2017-11-22

    Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

  3. Effects of digital human-machine interface characteristics on human error in nuclear power plants

    International Nuclear Information System (INIS)

    Li Pengcheng; Zhang Li; Dai Licao; Huang Weigang

    2011-01-01

    In order to identify the effects of digital human-machine interface characteristics on human error in nuclear power plants, the new characteristics of digital human-machine interface are identified by comparing with the traditional analog control systems in the aspects of the information display, user interface interaction and management, control systems, alarm systems and procedures system, and the negative effects of digital human-machine interface characteristics on human error are identified by field research and interviewing with operators such as increased cognitive load and workload, mode confusion, loss of situation awareness. As to the adverse effects related above, the corresponding prevention and control measures of human errors are provided to support the prevention and minimization of human errors and the optimization of human-machine interface design. (authors)

  4. A tactile P300 brain-computer interface

    NARCIS (Netherlands)

    Brouwer, A.M.; Erp, J.B.F. van

    2010-01-01

    De werking van de eerste Brain-Computer-Interface gebaseerd op tactiele EEG response wordt gedemonstreerd en het effect van het aantal gebruikte vibro-tactiele tactoren en stimulus-timing parameters wordt onderzocht

  5. A nonadhesive solid-gel electrode for a non-invasive brain–machine interface

    Directory of Open Access Journals (Sweden)

    Shigeru eToyama

    2012-07-01

    Full Text Available A non-invasive brain–machine interface (BMI or brain-computer interface (BCI is a technology for helping individuals with disabilities and utilizes neurophysiological signals from the brain to control external machines or computers without requiring surgery. However, when applying EEG methodology, users must place EEG electrodes on the scalp each time, and the development of easy-to-use electrodes for clinical use is required. In this study, we developed a conductive nonadhesive solid-gel electrode for practical non-invasive BMIs. We performed basic material testing, including examining the volume resistivity, viscoelasticity, and moisture-retention properties of the solid gel. Then, we compared the performance of the solid gel, a conventional paste, and an in-house metal pin-based electrode using impedance measurements and P300-BMI testing. The solid gel was observed to be conductive (volume resistivity 13.2 Ωcm and soft (complex modulus 105.4 kPa, and it remained wet for a prolonged period (>10 hours in a dry environment. Impedance measurements revealed that the impedance of the solid-gel-based and conventional paste-based electrodes was superior to that of the pin-based electrode. The EEG measurement suggested that the signals obtained with the solid-gel electrode were comparable to those with the conventional paste-based electrode. Moreover, the P300-BMI study suggested that systems using the solid-gel or pin-based electrodes were effective. One of the advantages of the solid gel is that it does not require cleaning after use, whereas the conventional paste adheres to the hair, which requires washing. Furthermore, the solid-gel electrode was not painful compared with a metal-pin electrode. Taken together, the results suggest that the solid-gel electrode worked well for practical BMIs and could be useful for bedridden patients such as those with amyotrophic lateral sclerosis.

  6. Ecological Design of Cooperative Human-Machine Interfaces for Safety of Intelligent Transport Systems

    Directory of Open Access Journals (Sweden)

    Orekhov Aleksandr

    2016-01-01

    Full Text Available The paper describes research results in the domain of cooperative intelligent transport systems. The requirements for human-machine interface considering safety issue of for intelligent transport systems (ITSare analyzed. Profiling of the requirements to cooperative human-machine interface (CHMI for such systems including requirements to usability and safety is based on a set of standards for ITSs. An approach and design technique of cooperative human-machine interface for ITSs are suggested. The architecture of cloud-based CHMI for intelligent transport systems has been developed. The prototype of software system CHMI4ITSis described.

  7. Participation in the ABWR Man-Machine interface design. Applicability to the Spanish Electrical Sector

    International Nuclear Information System (INIS)

    Rodriguez, C.; Manrique Martin, A.; Nunez, J.

    1997-01-01

    Project coordinated by DTN within the advanced reactor programme. Participation in the design activities for the Advanced Boiling Water Reactor (ABWR) man-machine interface was divided into two phases: Phase I: Preparation of drawings for designing, developing and assessing the advanced control room Phase II: Application of these drawings in design activities Participation in this programme has led to the following possible future applications to the electrical sector: 1. Design and implementation of man-machine interfaces 2. Human factor criteria 3. Assessment of man-machine interfaces 4. Functional specification, computerised operating procedures 5. Computerised alarm prototypes. (Author)

  8. Quantitative Machine Learning Analysis of Brain MRI Morphology throughout Aging.

    Science.gov (United States)

    Shamir, Lior; Long, Joe

    2016-01-01

    While cognition is clearly affected by aging, it is unclear whether the process of brain aging is driven solely by accumulation of environmental damage, or involves biological pathways. We applied quantitative image analysis to profile the alteration of brain tissues during aging. A dataset of 463 brain MRI images taken from a cohort of 416 subjects was analyzed using a large set of low-level numerical image content descriptors computed from the entire brain MRI images. The correlation between the numerical image content descriptors and the age was computed, and the alterations of the brain tissues during aging were quantified and profiled using machine learning. The comprehensive set of global image content descriptors provides high Pearson correlation of ~0.9822 with the chronological age, indicating that the machine learning analysis of global features is sensitive to the age of the subjects. Profiling of the predicted age shows several periods of mild changes, separated by shorter periods of more rapid alterations. The periods with the most rapid changes were around the age of 55, and around the age of 65. The results show that the process of brain aging of is not linear, and exhibit short periods of rapid aging separated by periods of milder change. These results are in agreement with patterns observed in cognitive decline, mental health status, and general human aging, suggesting that brain aging might not be driven solely by accumulation of environmental damage. Code and data used in the experiments are publicly available.

  9. Sequenced subjective accents for brain-computer interfaces

    Science.gov (United States)

    Vlek, R. J.; Schaefer, R. S.; Gielen, C. C. A. M.; Farquhar, J. D. R.; Desain, P.

    2011-06-01

    Subjective accenting is a cognitive process in which identical auditory pulses at an isochronous rate turn into the percept of an accenting pattern. This process can be voluntarily controlled, making it a candidate for communication from human user to machine in a brain-computer interface (BCI) system. In this study we investigated whether subjective accenting is a feasible paradigm for BCI and how its time-structured nature can be exploited for optimal decoding from non-invasive EEG data. Ten subjects perceived and imagined different metric patterns (two-, three- and four-beat) superimposed on a steady metronome. With an offline classification paradigm, we classified imagined accented from non-accented beats on a single trial (0.5 s) level with an average accuracy of 60.4% over all subjects. We show that decoding of imagined accents is also possible with a classifier trained on perception data. Cyclic patterns of accents and non-accents were successfully decoded with a sequence classification algorithm. Classification performances were compared by means of bit rate. Performance in the best scenario translates into an average bit rate of 4.4 bits min-1 over subjects, which makes subjective accenting a promising paradigm for an online auditory BCI.

  10. Brain-Computer Interfaces Applying Our Minds to Human-computer Interaction

    CERN Document Server

    Tan, Desney S

    2010-01-01

    For generations, humans have fantasized about the ability to create devices that can see into a person's mind and thoughts, or to communicate and interact with machines through thought alone. Such ideas have long captured the imagination of humankind in the form of ancient myths and modern science fiction stories. Recent advances in cognitive neuroscience and brain imaging technologies have started to turn these myths into a reality, and are providing us with the ability to interface directly with the human brain. This ability is made possible through the use of sensors that monitor physical p

  11. Measuring Emotion Regulation with Single Dry Electrode Brain Computer Interface

    NARCIS (Netherlands)

    van der Wal, C.N.; Irrmischer, M.; Guo, Y.; Friston, K.; Faisal, A.; Hill, S.; Peng, H.

    2015-01-01

    Wireless brain computer interfaces (BCI’s) are promising for new intelligent applications in which emotions are detected by measuring brain activity. Applications, such as serious games and video game therapy, are measuring and using the user’s emotional state in order to determine the intensity

  12. Multidimensional control using a mobile-phone based brain-muscle-computer interface.

    Science.gov (United States)

    Vernon, Scott; Joshi, Sanjay S

    2011-01-01

    Many well-known brain-computer interfaces measure signals at the brain, and then rely on the brain's ability to learn via operant conditioning in order to control objects in the environment. In our lab, we have been developing brain-muscle-computer interfaces, which measure signals at a single muscle and then rely on the brain's ability to learn neuromuscular skills via operant conditioning. Here, we report a new mobile-phone based brain-muscle-computer interface prototype for severely paralyzed persons, based on previous results from our group showing that humans may actively create specified power levels in two separate frequency bands of a single sEMG signal. Electromyographic activity on the surface of a single face muscle (Auricularis superior) is recorded with a standard electrode. This analog electrical signal is imported into an Android-based mobile phone. User-modulated power in two separate frequency band serves as two separate and simultaneous control channels for machine control. After signal processing, the Android phone sends commands to external devices via Bluetooth. Users are trained to use the device via biofeedback, with simple cursor-to-target activities on the phone screen.

  13. Engineering brain-computer interfaces: past, present and future.

    Science.gov (United States)

    Hughes, M A

    2014-06-01

    Electricity governs the function of both nervous systems and computers. Whilst ions move in polar fluids to depolarize neuronal membranes, electrons move in the solid-state lattices of microelectronic semiconductors. Joining these two systems together, to create an iono-electric brain-computer interface, is an immense challenge. However, such interfaces offer (and in select clinical contexts have already delivered) a method of overcoming disability caused by neurological or musculoskeletal pathology. To fulfill their theoretical promise, several specific challenges demand consideration. Rate-limiting steps cover a diverse range of disciplines including microelectronics, neuro-informatics, engineering, and materials science. As those who work at the tangible interface between brain and outside world, neurosurgeons are well placed to contribute to, and inform, this cutting edge area of translational research. This article explores the historical background, status quo, and future of brain-computer interfaces; and outlines the challenges to progress and opportunities available to the clinical neurosciences community.

  14. Intelligent Human Machine Interface Design for Advanced Product Life Cycle Management Systems

    OpenAIRE

    Ahmed, Zeeshan

    2010-01-01

    Designing and implementing an intelligent and user friendly human machine interface for any kind of software or hardware oriented application is always be a challenging task for the designers and developers because it is very difficult to understand the psychology of the user, nature of the work and best suit of the environment. This research paper is basically about to propose an intelligent, flexible and user friendly machine interface for Product Life Cycle Management products or PDM Syste...

  15. Design of Human – Machine Interface and Altering of Pelvic Obliquity with RGR Trainer

    OpenAIRE

    Pietrusinski, Maciej; Unluhisarcikli, Ozer; Mavroidis, Constantinos; Cajigas, Iahn; Bonato, Paolo

    2011-01-01

    The Robotic Gait Rehabilitation (RGR) Trainer targets secondary gait deviations in stroke survivors undergoing rehabilitation. Using an impedance control strategy and a linear electromagnetic actuator, the device generates a force field to control pelvic obliquity through a Human-Machine Interface (i.e. a lower body exoskeleton). Herein we describe the design of the RGR Trainer Human-Machine Interface (HMI) and we demonstrate the system’s ability to alter the pattern of movement of the pelvis...

  16. Knowledge-based support for design and operational use of human-machine interfaces

    International Nuclear Information System (INIS)

    Johannsen, G.

    1994-01-01

    The possibilities for knowledge support of different human user classes, namely operators, operational engineers and designers of human-machine interfaces, are discussed. Several human-machine interface functionalities are briefly explained. The paper deals with such questions as which type of knowledge is needed for design and operation, how to represent it, where to get it from, how to process it, and how to consider and use it. The relationships between design and operational use are thereby emphasised. (author)

  17. Brain-Computer Interfaces : Beyond Medical Applications

    NARCIS (Netherlands)

    Erp, J.B.F. van; Lotte, F.; Tangermann, M.

    2012-01-01

    Brain-computer interaction has already moved from assistive care to applications such as gaming. Improvements in usability, hardware, signal processing, and system integration should yield applications in other nonmedical areas.

  18. A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection.

    Science.gov (United States)

    Lo, Chi-Chun; Chien, Tsung-Yi; Chen, Yu-Chun; Tsai, Shang-Ho; Fang, Wai-Chi; Lin, Bor-Shyh

    2016-02-06

    Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.

  19. Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience

    NARCIS (Netherlands)

    Jensen, O.; Bahramisharif, A.; Oostenveld, R.; Klanke, S.; Hadjipapas, A.; Okazaki, Y.O.; Gerven, M.A.J. van

    2011-01-01

    Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain-computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for

  20. Applying machine learning to build a website interface adaptation system

    OpenAIRE

    MATESHUK EGOR; CHERNYSHEV ALEXANDER

    2015-01-01

    In this article we present the architecture and model of a website interface optimization system. We describe how we use clustering and genetic algorithms to automatically select a website interface with the highest conversion from website visitor to website user. In particular, we describe an algorithm for streamed clustering, which allows for real-time analysis of high traffic website users.

  1. Brain-computer interfaces for arts

    NARCIS (Netherlands)

    Gürkök, Hayrettin; Nijholt, Antinus; D' Mello, S.; Pantic, Maja

    2013-01-01

    We experience positive emotions when our hedonic needs, such as virtuosity or relatedness, are satisfied. Creating art is one way of satisfying these needs, so artistic computer applications can be considered as ‘affective’. Artistic braincomputer interfaces (BCIs), which allow people to create art

  2. Design for the human-machine interface of a digitalized reactor control-room

    International Nuclear Information System (INIS)

    Qu Ronghong; Zhang Liangju; Li Duo; Yu Hui

    2005-01-01

    Digitalized technology is implemented in the instrumentation and control system of an in-construction research reactor, which advances information display in both contents and styles in a nuclear reactor control-room, and greatly improves human-machine interface. In the design for a digitalized nuclear reactor control-room there are a series of new problems and technologies should be considered seriously. This paper mainly introduces the design for the digitalized control-room of the research nuclear reactor and covered topics include design principle of human-machine interface, organization and classification of interface graphics, technologies and principles based on human factors engineering and implemented in the graphics design. (authors)

  3. Operator-machine interface at a large laser-fusion facility

    International Nuclear Information System (INIS)

    Sutton, J.G.; Howell, J.A.

    1982-01-01

    The operator-machine interface at the Antares Laser Facility provides the operator with a means of controlling the laser system and obtaining operational and performance information. The goal of this interface is to provide an operator with access to the control system in a comfortable way, and to facilitate meeting operational requirements. We describe the philosophy and requirements behind this interface, the hardware used in building it, and the software environment

  4. Playing with your Brain : Brain-Computer Interfaces and Games

    NARCIS (Netherlands)

    Nijholt, Anton; Tan, Desney; Bernhaupt, Regina; Tscheligi, Manfred

    2007-01-01

    In this workshop we investigate a possible role of brain-computer interaction in computer games and entertainment computing. The assumption is that brain activity, whether it is consciously controlled and directed by the user or just recorded in order to obtain information about the user’s affective

  5. Playing with your Brain: Brain-Computer Interfaces and Games

    NARCIS (Netherlands)

    Nijholt, Antinus; Tan, Desney; Bernhaupt, R.; Tscheligi, M.

    2007-01-01

    In this workshop we investigate a possible role of brain-computer interaction in computer games and entertainment computing. The assumption is that brain activity, whether it is consciously controlled and directed by the user or just recorded in order to obtain information about the user’s affective

  6. Gaze-and-brain-controlled interfaces for human-computer and human-robot interaction

    Directory of Open Access Journals (Sweden)

    Shishkin S. L.

    2017-09-01

    Full Text Available Background. Human-machine interaction technology has greatly evolved during the last decades, but manual and speech modalities remain single output channels with their typical constraints imposed by the motor system’s information transfer limits. Will brain-computer interfaces (BCIs and gaze-based control be able to convey human commands or even intentions to machines in the near future? We provide an overview of basic approaches in this new area of applied cognitive research. Objective. We test the hypothesis that the use of communication paradigms and a combination of eye tracking with unobtrusive forms of registering brain activity can improve human-machine interaction. Methods and Results. Three groups of ongoing experiments at the Kurchatov Institute are reported. First, we discuss the communicative nature of human-robot interaction, and approaches to building a more e cient technology. Specifically, “communicative” patterns of interaction can be based on joint attention paradigms from developmental psychology, including a mutual “eye-to-eye” exchange of looks between human and robot. Further, we provide an example of “eye mouse” superiority over the computer mouse, here in emulating the task of selecting a moving robot from a swarm. Finally, we demonstrate a passive, noninvasive BCI that uses EEG correlates of expectation. This may become an important lter to separate intentional gaze dwells from non-intentional ones. Conclusion. The current noninvasive BCIs are not well suited for human-robot interaction, and their performance, when they are employed by healthy users, is critically dependent on the impact of the gaze on selection of spatial locations. The new approaches discussed show a high potential for creating alternative output pathways for the human brain. When support from passive BCIs becomes mature, the hybrid technology of the eye-brain-computer (EBCI interface will have a chance to enable natural, fluent, and the

  7. A Function-Behavior-State Approach to Designing Human Machine Interface for Nuclear Power Plant Operators

    Science.gov (United States)

    Lin, Y.; Zhang, W. J.

    2005-02-01

    This paper presents an approach to human-machine interface design for control room operators of nuclear power plants. The first step in designing an interface for a particular application is to determine information content that needs to be displayed. The design methodology for this step is called the interface design framework (called framework ). Several frameworks have been proposed for applications at varying levels, including process plants. However, none is based on the design and manufacture of a plant system for which the interface is designed. This paper presents an interface design framework which originates from design theory and methodology for general technical systems. Specifically, the framework is based on a set of core concepts of a function-behavior-state model originally proposed by the artificial intelligence research community and widely applied in the design research community. Benefits of this new framework include the provision of a model-based fault diagnosis facility, and the seamless integration of the design (manufacture, maintenance) of plants and the design of human-machine interfaces. The missing linkage between design and operation of a plant was one of the causes of the Three Mile Island nuclear reactor incident. A simulated plant system is presented to explain how to apply this framework in designing an interface. The resulting human-machine interface is discussed; specifically, several fault diagnosis examples are elaborated to demonstrate how this interface could support operators' fault diagnosis in an unanticipated situation.

  8. Attempt on construction of human friendly man-machine interface. Study and apply about human communication; Human friendly na man machine interface kochiku no kokoromi. Ningen no communication no kento to sono oyo

    Energy Technology Data Exchange (ETDEWEB)

    Tatsuno, J. [Tokyo University of Agriculture, Tokyo (Japan); Kokubo, Y.; Matsumura, I.; Kobayashi, H. [Hosei University, Tokyo (Japan)

    1998-04-01

    This paper describes an attempt on a construction way of human friendly man-machine interface. At first, we do a simple experiment to find out the characteristic of human verbal communication. From the experimental results, we get some rules in case in human verbal communication. We construct the man-machine interface which is based on these rules. Through teaching process, we examine our verbal communication interface comparing with conventional interfaces. From this comparison, we recognize that the verbal communication interface is valid to construct the user-friendly man-machine interface. 12 refs., 9 figs., 2 tabs.

  9. Shaping of neuronal activity through a Brain Computer Interface

    OpenAIRE

    Valero-Aguayo, Luis; Silva-Sauer, Leandro; Velasco-Alvarez, Ricardo; Ron-Angevin, Ricardo

    2014-01-01

    Neuronal responses are human actions which can be measured by an EEG, and which imply changes in waves when neurons are synchronized. This activity could be changed by principles of behaviour analysis. This research tests the efficacy of the behaviour shaping procedure to progressively change neuronal activity, so that those brain responses are adapted according to the differential reinforcement of visual feedback. The Brain Computer Interface (BCI) enables us to record the EEG in real ti...

  10. Guest editorial: Brain/neuronal computer games interfaces and interaction

    OpenAIRE

    Coyle, D.; Principe, J.; Lotte, F.; Nijholt, Antinus

    2013-01-01

    Nowadays brainwave or electroencephalogram (EEG) controlled games controllers are adding new options to satisfy the continual demand for new ways to interact with games, following trends such as the Nintendo® Wii, Microsoft® Kinect and Playstation® Move which are based on accelerometers and motion capture. EEG-based brain-computer games interaction are controlled through brain-computer interface (BCI) technology which requires sophisticated signal processing to produce a low communication ban...

  11. Soft drink effects on sensorimotor rhythm brain computer interface performance and resting-state spectral power.

    Science.gov (United States)

    Mundahl, John; Jianjun Meng; He, Jeffrey; Bin He

    2016-08-01

    Brain-computer interface (BCI) systems allow users to directly control computers and other machines by modulating their brain waves. In the present study, we investigated the effect of soft drinks on resting state (RS) EEG signals and BCI control. Eight healthy human volunteers each participated in three sessions of BCI cursor tasks and resting state EEG. During each session, the subjects drank an unlabeled soft drink with either sugar, caffeine, or neither ingredient. A comparison of resting state spectral power shows a substantial decrease in alpha and beta power after caffeine consumption relative to control. Despite attenuation of the frequency range used for the control signal, caffeine average BCI performance was the same as control. Our work provides a useful characterization of caffeine, the world's most popular stimulant, on brain signal frequencies and their effect on BCI performance.

  12. The Future of Brain-Computer Interfacing (keynote paper)

    NARCIS (Netherlands)

    Nijholt, Antinus

    In this paper we survey some early applications and research on brain-computer interfacing. We emphasize and revalue the role the views on artistic and playful applications have played. In previous years various road maps for BCI research appeared. The interest in medical applications has guided BCI

  13. An associative Brain-Computer-Interface for acute stroke patients

    DEFF Research Database (Denmark)

    Mrachacz-Kersting, Natalie; Stevenson, Andrew James Thomas; Aliakbaryhosseinabadi, Susan

    2016-01-01

    An efficient innovative Brain-Computer-Interface system that empowers chronic stroke patients to control an artificial activation of their lower limb muscle through task specific motor intent has been tested in the past. In the current study it was applied to acute stroke patients. The system...

  14. Brain-Computer Interface Games: Towards a Framework

    NARCIS (Netherlands)

    Gürkök, Hayrettin; Nijholt, Antinus; Poel, Mannes; Nakatsu, Ryohei; Rauterberg, Matthias; Ciancarini, Paolo

    2015-01-01

    The brain-computer interface (BCI) community has started to consider games as potential applications, while the game community has started to consider BCI as a game controller. However, there is a discrepancy between the BCI games developed by the two communities. This not only adds to the workload

  15. Real-time brain computer interface using imaginary movements

    DEFF Research Database (Denmark)

    El-Madani, Ahmad; Sørensen, Helge Bjarup Dissing; Kjær, Troels W.

    2015-01-01

    Background: Brain Computer Interface (BCI) is the method of transforming mental thoughts and imagination into actions. A real-time BCI system can improve the quality of life of patients with severe neuromuscular disorders by enabling them to communicate with the outside world. In this paper...

  16. Social Interaction in a Cooperative Brain-computer Interface Game

    NARCIS (Netherlands)

    Obbink, Michel; Gürkök, Hayrettin; Plass - Oude Bos, D.; Hakvoort, Gido; Poel, Mannes; Nijholt, Antinus; Camurri, Antonio; Costa, Cristina

    Does using a brain-computer interface (BCI) influence the social interaction between people when playing a cooperative game? By measuring the amount of speech, utterances, instrumental gestures and empathic gestures during a cooperative game where two participants had to reach a certain goal, and

  17. Brain-computer interfacing under distraction: an evaluation study

    DEFF Research Database (Denmark)

    Brandl, Stephanie; Frølich, Laura; Höhne, Johannes

    2016-01-01

    Objective. While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach...

  18. Brain-Computer Interface Games: Towards a Framework.

    NARCIS (Netherlands)

    Gürkök, Hayrettin; Nijholt, Antinus; Poel, Mannes; Herrlich, Marc; Malaka, Rainer; Masuch, Maic

    2012-01-01

    The brain-computer interface (BCI) community started to consider games as potential applications while the games community started to consider BCI as a game controller. However, there is a discrepancy between the BCI games developed by the two communities. In this paper, we propose a preliminary BCI

  19. Tutorial: Signal Processing in Brain-Computer Interfaces

    NARCIS (Netherlands)

    Garcia Molina, G.

    2010-01-01

    Research in Electroencephalogram (EEG) based Brain-Computer Interfaces (BCIs) has been considerably expanding during the last few years. Such an expansion owes to a large extent to the multidisciplinary and challenging nature of BCI research. Signal processing undoubtedly constitutes an essential

  20. Third Workshop on Affective Brain-Computer Interfaces: introduction

    NARCIS (Netherlands)

    Mühl, C.; Chanel, G.; Allison, B.; Nijholt, Antinus

    2013-01-01

    Following the first and second workshop on affective brain-computer interfaces, held in conjunction with ACII in Amsterdam (2009) and Memphis (2011), the third workshop explores the advantages and limitations of using neurophysiological signals for the automatic recognition of affective and

  1. Control rooms and man-machine interface in nuclear power plants

    International Nuclear Information System (INIS)

    1990-08-01

    The importance of man-machine interface for ensuring safe and reliable operation of nuclear power plants has always been recognized. Since the early 1970's, the concepts of operator support and human factors have been increasingly used to better define the role of control rooms. In the late 1970's, the lessons learned from experience considerably accelerated the development of recommendations and regulatory requirements governing the resources and data available to operators in nuclear power plant control rooms, and specified the expertise required to assist them in case of need. This document summarizes the steps which have been taken and are being planned around the world to improve the man-machine interface for safe and economic power generation. It intends to present to the reader useful examples on some selected control room design and man-machine interface practices for operation and surveillance of nuclear power plants. 53 refs, 94 figs, 27 tabs

  2. Near infrared spectroscopy based brain-computer interface

    Science.gov (United States)

    Ranganatha, Sitaram; Hoshi, Yoko; Guan, Cuntai

    2005-04-01

    A brain-computer interface (BCI) provides users with an alternative output channel other than the normal output path of the brain. BCI is being given much attention recently as an alternate mode of communication and control for the disabled, such as patients suffering from Amyotrophic Lateral Sclerosis (ALS) or "locked-in". BCI may also find applications in military, education and entertainment. Most of the existing BCI systems which rely on the brain's electrical activity use scalp EEG signals. The scalp EEG is an inherently noisy and non-linear signal. The signal is detrimentally affected by various artifacts such as the EOG, EMG, ECG and so forth. EEG is cumbersome to use in practice, because of the need for applying conductive gel, and the need for the subject to be immobile. There is an urgent need for a more accessible interface that uses a more direct measure of cognitive function to control an output device. The optical response of Near Infrared Spectroscopy (NIRS) denoting brain activation can be used as an alternative to electrical signals, with the intention of developing a more practical and user-friendly BCI. In this paper, a new method of brain-computer interface (BCI) based on NIRS is proposed. Preliminary results of our experiments towards developing this system are reported.

  3. What will this do to me and my brain? Ethical issues in brain-to-brain interfacing

    Directory of Open Access Journals (Sweden)

    Elisabeth eHildt

    2015-02-01

    Full Text Available For several years now, brain-computer interfaces (BCIs in which brain signals are used to navigate a computer, a prostheses or a technical device, have been developed in various experimental contexts (Wolpaw & Wolpaw 2012; Grübler & Hildt 2014. Researchers have recently taken the next step and run experiments based on connections between two brains. These so-called brain-to-brain interfaces (abbreviation: BBIs or BTBIs involve not only a BCI component deriving information from a brain and sending it to a computer, but also a computer-brain interface (CBI component delivering information to another brain. What results is technology-mediated brain-to-brain communication (B2B communication, i.e. direct communication between two brains that does not involve any activity of the peripheral nervous system. In what follows, ethical issues that arise in neural interfacing will be discussed after a short introduction to recent BBI experiments. In this, the focus will be on the implications BBIs may have on the individual at the CBI side of the BBI, i.e. on the recipient.

  4. Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm

    Directory of Open Access Journals (Sweden)

    Pablo Martinez

    2007-01-01

    Full Text Available We propose a new multistage procedure for a real-time brain-machine/computer interface (BCI. The developed system allows a BCI user to navigate a small car (or any other object on the computer screen in real time, in any of the four directions, and to stop it if necessary. Extensive experiments with five young healthy subjects confirmed the high performance of the proposed online BCI system. The modular structure, high speed, and the optimal frequency band characteristics of the BCI platform are features which allow an extension to a substantially higher number of commands in the near future.

  5. Control of a mobile robot through brain computer interface

    Directory of Open Access Journals (Sweden)

    Robinson Jimenez Moreno

    2015-07-01

    Full Text Available This paper poses a control interface to command the movement of a mobile robot according to signals captured from the user's brain. These signals are acquired and interpreted by Emotiv EPOC device, a 14-electrode type sensor which captures electroencephalographic (EEG signals with high resolution, which, in turn, are sent to a computer for processing. One brain-computer interface (BCI was developed based on the Emotiv software and SDK in order to command the mobile robot from a distance. Functionality tests are performed with the sensor to discriminate shift intentions of a user group, as well as with a fuzzy controller to hold the direction in case of concentration loss. As conclusion, it was possible to obtain an efficient system for robot movements by brain commands.

  6. Survey of the problems posed by the man-machine interface, as seen from the angle of facility operators

    International Nuclear Information System (INIS)

    Heinbuch, R.

    1995-01-01

    The man-machine interface in nuclear power plants is an area very much influenced by the vigorous progress in computer technology. The paper describes the causes underlying the innovative power in this field and its impacts on the man-machine interface in nuclear power plants. The benefits brought by the advanced computer systems in the design of the man-machine interface as well as the problems posed through application in practice to safety-relevant plant systems are discussed, and examples are given showing the experience accumulated so far, and the significant changes effected in the man-machine interface. (orig.) [de

  7. Interfacing and data acquisitioning of creep testing machines

    International Nuclear Information System (INIS)

    Rana, M.A.; Ahmad, Z.; Farooq, M.A.; Ali, L.; Mushtaq, N.

    1998-05-01

    Automation of DSM-6100-CREEP TESTING MACHINES is made by using an IBM PC/XT/AT compatibles along with DAS-16 High Speed Analog I/O board. Creep test parameters namely force, temperature and LVDTs (Linear Variable Differential Transducer) left and right are calibrated. Empirical formula for each parameter is developed to convert data, which is received in the form of counts, into engineering units. LVDT controller module is designed and fabricated to handle two LVDTs for data acquisition of a creep test required for it. (author)

  8. Configuration management plan for Machine Interface Test System (MITS)

    International Nuclear Information System (INIS)

    O'Neill, C.K.

    1980-01-01

    The discipline required by this plan will apply from the establishment of a configuration baseline until completion of the final test in the MITS. The plan applies to configured items of hardware and software as well as to the specifications and drawings for these items. The plan encompasses establishment of the facility baseline, interface definition, classes of change, change control, change paper, organizational responsibilities and relationships, test configuration (as opposed to facility), and configuration data retention

  9. Some trends in man-machine interface design for industrial process plants

    DEFF Research Database (Denmark)

    Rasmussen, Jens

    1980-01-01

    . In the paper, problems related to interface design, operator training and human reliability are discussed in the light of this technological development, and an integrated approach to system design based on a consistent model or framework describing the man-machine interaction is advocated.The work presented......The demands for an efficient and reliable man-machine inter-face in industrial process plant are increasing due to the steadily growing size and complexity of installations. At the same time, computerized technology offers the possibility of powerful and effective solutions to designers...

  10. Consolidated fuel-reprocessing program:: man/machine interface development for the REMOTEX concept

    International Nuclear Information System (INIS)

    Garin, J.; Clarke, M.M.

    1981-01-01

    This paper describes ongoing research at ORNL to develop a man/machine interface system that can be used to remotely control a system composed of a transporter base and a force-reflecting, servo-controlled manipulator. A unique feature of the concept is the incorporation of totally remote operation. Thus, a major objective is the requirement that an operator have a sense of presence in the remote environment. Man/machine interface requirements for this totally remote operation remain to be developed. Therefore, a simulator is being built to optimize such requirements and the developments are discussed

  11. Development of effective tool for iterative design of human machine interfaces in nuclear power plant

    International Nuclear Information System (INIS)

    Nakagawa, Takashi; Matsuo, Satoko; Yoshikawa, Hidekazu; Wu, Wei; Kameda, Akiyuki; Fumizawa, Motoo

    2000-01-01

    The authors have developed SEAMAID, which is a Simulation-based Evaluation and Analysis support system for MAn-machine Interface Design (SEAMAID) in the domain of nuclear power plants. The SEAMAID simulated the interaction between an operator and human machine interfaces (HMI), and supports to evaluate the HMI by using the simulation results. In this paper, a case study of evaluation for conventional center control room design was conducted. The authors were confirmed that SEAMAID is a useful tool for improvements of HMI design (J.P.N.)

  12. White matter microstructure changes induced by motor skill learning utilizing a body machine interface.

    Science.gov (United States)

    Wang, Xue; Casadio, Maura; Weber, Kenneth A; Mussa-Ivaldi, Ferdinando A; Parrish, Todd B

    2014-03-01

    The purpose of this study is to identify white matter microstructure changes following bilateral upper extremity motor skill training to increase our understanding of learning-induced structural plasticity and enhance clinical strategies in physical rehabilitation. Eleven healthy subjects performed two visuo-spatial motor training tasks over 9 sessions (2-3 sessions per week). Subjects controlled a cursor with bilateral simultaneous movements of the shoulders and upper arms using a body machine interface. Before the start and within 2days of the completion of training, whole brain diffusion tensor MR imaging data were acquired. Motor training increased fractional anisotropy (FA) values in the posterior and anterior limbs of the internal capsule, the corona radiata, and the body of the corpus callosum by 4.19% on average indicating white matter microstructure changes induced by activity-dependent modulation of axon number, axon diameter, or myelin thickness. These changes may underlie the functional reorganization associated with motor skill learning. Copyright © 2013 Elsevier Inc. All rights reserved.

  13. Hacking the brain: Brain-computer interfacing technology and the ethics of neurosecurity

    NARCIS (Netherlands)

    Ienca, M.; Haselager, W.F.G.

    2016-01-01

    Brain-computer interfacing technologies are used as assistive technologies for patients as well as healthy subjects to control devices solely by brain activity. Yet the risks associated with the misuse of these technologies remain largely unexplored. Recent findings have shown that BCIs are

  14. Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience

    NARCIS (Netherlands)

    Jensen, O.; Bahramisharif, A.; Oostenveld, R.; Klanke, S.; Hadjipapas, A.; Okazaki, Y.O.; Gerven, M.A.J. van

    2011-01-01

    Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain–computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for

  15. Architecture and prototype of human-machine interface with mobile robotic device

    International Nuclear Information System (INIS)

    Dyumin, A.A.; Sorokoumov, P.S.; Chepin, E.V.; Urvanov, G.A.

    2013-01-01

    The possibility of controlling mobile robotic (MRD) device is analyzed and a prototype control system is described. It is established that, for controlling MRD, it is expedient to use a brain-computer interface. A system of interpretation of information obtained from the operator brain has been developed and used in the proposed prototype control system [ru

  16. Techniques and applications for binaural sound manipulation in human-machine interfaces

    Science.gov (United States)

    Begault, Durand R.; Wenzel, Elizabeth M.

    1992-01-01

    The implementation of binaural sound to speech and auditory sound cues (auditory icons) is addressed from both an applications and technical standpoint. Techniques overviewed include processing by means of filtering with head-related transfer functions. Application to advanced cockpit human interface systems is discussed, although the techniques are extendable to any human-machine interface. Research issues pertaining to three-dimensional sound displays under investigation at the Aerospace Human Factors Division at NASA Ames Research Center are described.

  17. General man-machine interface used in accelerators controls

    International Nuclear Information System (INIS)

    Boutheon, M.; Di Maio, F.; Pace, A.

    1992-01-01

    A large community is now using Workstations as Accelerators Computer Controls Interface, through the concepts of windows - menus - synoptics - icons. Some standards were established for the CERN-PS control systems rejuvenation. The Booster-to-PS transfer and injection process is now entirely operated with these tools. This application constitutes a global environment providing the users with the controls, analysis, visualization of a part of an accelerator. Individual commands, measurements, and specialized programs including complex treatments are available in a homogeneous frame. Some months of experience in current operation have shown that this model can be extended to the whole project. (author)

  18. Optimization of the man-machine interface for LMFBRs

    International Nuclear Information System (INIS)

    Seeman, S.E.; Colley, R.W.; Stratton, R.C.

    1982-01-01

    An effort is underway to optimize the roles of man and machine in control of Liquid Metal Fast Breeder Reactors. The work reported on here describes two systems that have been developed. The first of these, MIDAS, is a large data base system developed for use at FFTF as an aid to operators in determining how to proceed with maintenance and repairs to be carried out on plant components. This system is presently in use at FFTF. The second system, the Procedure Prompting System, is a system being developed to demonstrate a new methodology for automatically generating off-normal plant recovery instructions. The methodology for this system has been demonstrated on a model of a small subsystem of FFTF

  19. Prediction of stroke thrombolysis outcome using CT brain machine learning

    Directory of Open Access Journals (Sweden)

    Paul Bentley

    2014-01-01

    Full Text Available A critical decision-step in the emergency treatment of ischemic stroke is whether or not to administer thrombolysis — a treatment that can result in good recovery, or deterioration due to symptomatic intracranial haemorrhage (SICH. Certain imaging features based upon early computerized tomography (CT, in combination with clinical variables, have been found to predict SICH, albeit with modest accuracy. In this proof-of-concept study, we determine whether machine learning of CT images can predict which patients receiving tPA will develop SICH as opposed to showing clinical improvement with no haemorrhage. Clinical records and CT brains of 116 acute ischemic stroke patients treated with intravenous thrombolysis were collected retrospectively (including 16 who developed SICH. The sample was split into training (n = 106 and test sets (n = 10, repeatedly for 1760 different combinations. CT brain images acted as inputs into a support vector machine (SVM, along with clinical severity. Performance of the SVM was compared with established prognostication tools (SEDAN and HAT scores; original, or after adaptation to our cohort. Predictive performance, assessed as area under receiver-operating-characteristic curve (AUC, of the SVM (0.744 compared favourably with that of prognostic scores (original and adapted versions: 0.626–0.720; p < 0.01. The SVM also identified 9 out of 16 SICHs, as opposed to 1–5 using prognostic scores, assuming a 10% SICH frequency (p < 0.001. In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods.

  20. Application of machine learning on brain cancer multiclass classification

    Science.gov (United States)

    Panca, V.; Rustam, Z.

    2017-07-01

    Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

  1. A brain-computer interface to support functional recovery

    DEFF Research Database (Denmark)

    Kjaer, Troels W; Sørensen, Helge Bjarup Dissing

    2013-01-01

    Brain-computer interfaces (BCI) register changes in brain activity and utilize this to control computers. The most widely used method is based on registration of electrical signals from the cerebral cortex using extracranially placed electrodes also called electroencephalography (EEG). The features...... extracted from the EEG may, besides controlling the computer, also be fed back to the patient for instance as visual input. This facilitates a learning process. BCI allow us to utilize brain activity in the rehabilitation of patients after stroke. The activity of the cerebral cortex varies with the type...... of movement we imagine, and by letting the patient know the type of brain activity best associated with the intended movement the rehabilitation process may be faster and more efficient. The focus of BCI utilization in medicine has changed in recent years. While we previously focused on devices facilitating...

  2. Connections that Count: Brain-Computer Interface Enables the Profoundly Paralyzed to Communicate

    Science.gov (United States)

    ... Home Current Issue Past Issues Connections that Count: Brain-Computer Interface Enables the Profoundly Paralyzed to Communicate Past Issues / ... of this page please turn Javascript on. A brain-computer interface (BCI) system This brain-computer interface (BCI) system ...

  3. A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

    NARCIS (Netherlands)

    Melo, Rita; Fieldhouse, Robert; Melo, André; Correia, João D G; Cordeiro, Maria Natália D S; Gümüş, Zeynep H; Costa, Joaquim; Bonvin, Alexandre M J J; de Sousa Moreira, Irina

    2016-01-01

    Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model

  4. Interface unit and software of X-ray television automatic machine

    International Nuclear Information System (INIS)

    Molodykh, V.A.; Yamanaev, M.S.

    1983-01-01

    Description of the interface unit and specialized software of X-ray television automatic machine is presented. An algorithm for automatic defect survey, measuring of defect geometric parameters with a successive estimate of control quality in accordance with technical norms is proposed. Experimental investigation results on the quality of welded joints of steel tubes obtained using the above system are summarized

  5. Possibilities and expectations for improved man-machine interface in power system control

    Energy Technology Data Exchange (ETDEWEB)

    Asal, H; Burrow, R K; Lindstrom, K; Mocenigo, M; Schellstede, G; Schaffer, G; Serrani, A

    1992-05-01

    The paper describes the hardware, equipment and functions provided to operators for supervising and controlling HVAC power systems. It analyzes the main elements of the man-machine interface (MMI) with particular attention to the recent possibilities afforded by computer technology and full graphic screens. Alarm management and remote control operation are briefly described.

  6. Man/machine interface for a nuclear cask remote handling control station: system design requirements

    International Nuclear Information System (INIS)

    Clarke, M.M.; Kreifeldt, J.G.; Draper, J.V.

    1984-01-01

    Design requirements are presented for a control station of a proposed semi-automated facility for remote handling of nuclear waste casks. Functional and operational man/machine interface: controls, displays, software format, station architecture, and work environment. In addition, some input is given to the design of remote sensing systems in the cask handling areas. 18 references, 9 figures, 12 tables

  7. A friend man-machine interface for thermo-hydraulic simulation codes of nuclear installations

    International Nuclear Information System (INIS)

    Araujo Filho, F. de; Belchior Junior, A.; Barroso, A.C.O.; Gebrim, A.

    1994-01-01

    This work presents the development of a Man-Machine Interface to the TRAC-PF1 code, a computer program to perform best estimate analysis of transients and accidents at nuclear power plants. The results were considered satisfactory and a considerable productivity gain was achieved in the activity of preparing and analyzing simulations. (author)

  8. A comprehensive survey of brain interface technology designs.

    Science.gov (United States)

    Mason, S G; Bashashati, A; Fatourechi, M; Navarro, K F; Birch, G E

    2007-02-01

    In this work we present the first comprehensive survey of Brain Interface (BI) technology designs published prior to January 2006. Detailed results from this survey, which was based on the Brain Interface Design Framework proposed by Mason and Birch, are presented and discussed to address the following research questions: (1) which BI technologies are directly comparable, (2) what technology designs exist, (3) which application areas (users, activities and environments) have been targeted in these designs, (4) which design approaches have received little or no research and are possible opportunities for new technology, and (5) how well are designs reported. The results of this work demonstrate that meta-analysis of high-level BI design attributes is possible and informative. The survey also produced a valuable, historical cross-reference where BI technology designers can identify what types of technology have been proposed and by whom.

  9. Operation of micro and molecular machines: a new concept with its origins in interface science.

    Science.gov (United States)

    Ariga, Katsuhiko; Ishihara, Shinsuke; Izawa, Hironori; Xia, Hong; Hill, Jonathan P

    2011-03-21

    A landmark accomplishment of nanotechnology would be successful fabrication of ultrasmall machines that can work like tweezers, motors, or even computing devices. Now we must consider how operation of micro- and molecular machines might be implemented for a wide range of applications. If these machines function only under limited conditions and/or require specialized apparatus then they are useless for practical applications. Therefore, it is important to carefully consider the access of functionality of the molecular or nanoscale systems by conventional stimuli at the macroscopic level. In this perspective, we will outline the position of micro- and molecular machines in current science and technology. Most of these machines are operated by light irradiation, application of electrical or magnetic fields, chemical reactions, and thermal fluctuations, which cannot always be applied in remote machine operation. We also propose strategies for molecular machine operation using the most conventional of stimuli, that of macroscopic mechanical force, achieved through mechanical operation of molecular machines located at an air-water interface. The crucial roles of the characteristics of an interfacial environment, i.e. connection between macroscopic dimension and nanoscopic function, and contact of media with different dielectric natures, are also described.

  10. Quantitative evaluation of impedance perception characteristics of humans in the man-machine interface

    International Nuclear Information System (INIS)

    Onish, Keiichi; Kim, Young Woo; Obinata, Goro; Hase, Kazunori

    2013-01-01

    We investigated impedance perception characteristics of humans in the man-machine interface. Sensibility or operational feel about physical properties of machine dynamics is obtained through perception process. We evaluated the impedance perception characteristics of humans who are operating a mechanical system, based on extended Scheffe's subjective evaluation method in full consideration of the influence of impedance level, impedance difference, experiment order, individual difference and so on. Constant method based quantitative evaluation was adopted to investigate the influence of motion frequency and change of the impedance on human impedance perception characteristics. Experimental results indicate that humans perceive impedance of mechanical systems based on comparison process of the dynamical characteristics of the systems. The proposed method can be applied to quantify the design requirement of man-machine interface. The effectiveness of the proposed method is verified through experimental results.

  11. Quantitative evaluation of impedance perception characteristics of humans in the man-machine interface

    Energy Technology Data Exchange (ETDEWEB)

    Onish, Keiichi [Yamaha Motor Co., Shizuoka (Japan); Kim, Young Woo [Daegu Techno Park R and D Center, Seoul (Korea, Republic of); Obinata, Goro [Nagoya University, Nagoya (Japan); Hase, Kazunori [Tokyo Metropolitan University, Tokyo (Japan)

    2013-05-15

    We investigated impedance perception characteristics of humans in the man-machine interface. Sensibility or operational feel about physical properties of machine dynamics is obtained through perception process. We evaluated the impedance perception characteristics of humans who are operating a mechanical system, based on extended Scheffe's subjective evaluation method in full consideration of the influence of impedance level, impedance difference, experiment order, individual difference and so on. Constant method based quantitative evaluation was adopted to investigate the influence of motion frequency and change of the impedance on human impedance perception characteristics. Experimental results indicate that humans perceive impedance of mechanical systems based on comparison process of the dynamical characteristics of the systems. The proposed method can be applied to quantify the design requirement of man-machine interface. The effectiveness of the proposed method is verified through experimental results.

  12. Ethical aspects of brain computer interfaces: a scoping review

    OpenAIRE

    Burwell, Sasha; Sample, Matthew; Racine, Eric

    2017-01-01

    Background Brain-Computer Interface (BCI) is a set of technologies that are of increasing interest to researchers. BCI has been proposed as assistive technology for individuals who are non-communicative or paralyzed, such as those with amyotrophic lateral sclerosis or spinal cord injury. The technology has also been suggested for enhancement and entertainment uses, and there are companies currently marketing BCI devices for those purposes (e.g., gaming) as well as health-related purposes (e.g...

  13. Vibrotactile Feedback for Brain-Computer Interface Operation

    OpenAIRE

    Cincotti, Febo; Kauhanen, Laura; Aloise, Fabio; Palomäki, Tapio; Caporusso, Nicholas; Jylänki, Pasi; Mattia, Donatella; Babiloni, Fabio; Vanacker, Gerolf; Nuttin, Marnix; Marciani, Maria Grazia; Millán, José del R.

    2007-01-01

    To be correctly mastered, brain-computer interfaces (BCIs) need an uninterrupted flow of feedback to the user. This feedback is usually delivered through the visual channel. Our aim was to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and software arrangements, was specified. In three studies with 33 subjects (i...

  14. Optimizing the Usability of Brain-Computer Interfaces.

    Science.gov (United States)

    Zhang, Yin; Chase, Steve M

    2018-03-22

    Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.

  15. Using cognitive modeling to improve the man-machine interface

    International Nuclear Information System (INIS)

    Newton, R.A.; Zyduck, R.C.; Johnson, D.R.

    1982-01-01

    A group of utilities from the Westinghouse Owners Group was formed in early 1980 to examine the interface requirements and to determine how they could be implemented. The products available from the major vendors were examined early in 1980 and judged not to be completely applicable. The utility group then decided to develop its own specifications for a Safety Assessment System (SAS) and, later in 1980, contracted with a company to develop the system, prepare the software and demonstrate the system on a simulator. The resulting SAS is a state-of-the-art system targeted for implementation on pressurized water reactor nuclear units. It has been designed to provide control room operators with centralized and easily understandable information from a computer-based data and display system. This paper gives an overview of the SAS plus a detailed description of one of its functional areas - called AIDS. The AIDS portion of SAS is an advanced concept which uses cognitive modeling of the operator as the basis for its design

  16. A new workstation based man/machine interface system for the JT-60 Upgrade

    International Nuclear Information System (INIS)

    Yonekawa, I.; Shimono, M.; Totsuka, T.; Yamagishi, K.

    1992-01-01

    Development of a new man/machine interface system was stimulated by the requirements of making the JT-60 operator interface more 'friendly' on the basis of the past five-year operational experience. Eleven Sun/3 workstations and their supervisory mini-computer HIDIC V90/45 are connected through the standard network; Ethernet. The network is also connected to the existing 'ZENKEI' mini-computer system through the shared memory on the HIDIC V90/45 mini-computer. Improved software, such as automatic setting of the discharge conditions, consistency check among the related parameters and easy operation for discharge result data display, offered the 'user-friendly' environments. This new man/machine interface system leads to the efficient operation of the JT-60. (author)

  17. Brain-muscle-computer interface: mobile-phone prototype development and testing.

    Science.gov (United States)

    Vernon, Scott; Joshi, Sanjay S

    2011-07-01

    We report prototype development and testing of a new mobile-phone-based brain-muscle-computer interface for severely paralyzed persons, based on previous results from our group showing that humans may actively create specified power levels in two separate frequency bands of a single surface electromyography (sEMG) signal. EMG activity on the surface of a single face muscle site (auricularis superior) is recorded with a standard electrode. This analog electrical signal is imported into an Android-based mobile phone and digitized via an internal A/D converter. The digital signal is split, and then simultaneously filtered with two band-pass filters to extract total power within two separate frequency bands. The user-modulated power in each frequency band serves as two separate control channels for machine control. After signal processing, the Android phone sends commands to external devices via a Bluetooth interface. Users are trained to use the device via visually based operant conditioning, with simple cursor-to-target activities on the phone screen. The mobile-phone prototype interface is formally evaluated on a single advanced Spinal Muscle Atrophy subject, who has successfully used the interface in his home in evaluation trials and for remote control of a television. Development of this new device will not only guide future interface design for community use, but will also serve as an information technology bridge for in situ data collection to quantify human sEMG manipulation abilities for a relevant population.

  18. Prospect of brain machine interface in motor disabilities: the future ...

    African Journals Online (AJOL)

    Abstract. Multiple sclerosis (MS) is an autoimmune neurological disorder, which has impacted health related quality of life (HRQoL) more intensively than any other neurological disorder. The approaches to improve the health standard in MS patient are still a subject of primary importance in medical practice and seek a lot of ...

  19. Utilizing Brain-Computer Interfaces for Human-Machine Systems

    OpenAIRE

    Zander, Thorsten Oliver

    2012-01-01

    Verbindet man ein menschliches Gehirn über eine Hirn-Rechner-Schnittstelle (BCI) mit einer Maschine, so kann das resultierende Mensch-Maschine-System (MMS) Informationen über die Hirnaktivität des Nutzers ableiten. Um diese Verbindung aufbauen zu können, werden Methoden aus dem Bereich des Maschinellen Lernens und Grundlagenwissen über die Interpretation des Elektroenzephalogramms (EEG) miteinander verknüpft. Dieser Ansatz erlaubt automatisierte Analysen der Hirnaktivität in Echtzeit, deren E...

  20. An online hybrid brain-computer interface combining multiple physiological signals for webpage browse.

    Science.gov (United States)

    Long Chen; Zhongpeng Wang; Feng He; Jiajia Yang; Hongzhi Qi; Peng Zhou; Baikun Wan; Dong Ming

    2015-08-01

    The hybrid brain computer interface (hBCI) could provide higher information transfer rate than did the classical BCIs. It included more than one brain-computer or human-machine interact paradigms, such as the combination of the P300 and SSVEP paradigms. Research firstly constructed independent subsystems of three different paradigms and tested each of them with online experiments. Then we constructed a serial hybrid BCI system which combined these paradigms to achieve the functions of typing letters, moving and clicking cursor, and switching among them for the purpose of browsing webpages. Five subjects were involved in this study. They all successfully realized these functions in the online tests. The subjects could achieve an accuracy above 90% after training, which met the requirement in operating the system efficiently. The results demonstrated that it was an efficient system capable of robustness, which provided an approach for the clinic application.

  1. From assistance towards restoration with epidural brain-computer interfacing.

    Science.gov (United States)

    Gharabaghi, Alireza; Naros, Georgios; Walter, Armin; Grimm, Florian; Schuermeyer, Marc; Roth, Alexander; Bogdan, Martin; Rosenstiel, Wolfgang; Birbaumer, Niels

    2014-01-01

    Today's implanted brain-computer interfaces make direct contact with the brain or even penetrate the tissue, bearing additional risks with regard to safety and stability. What is more, these approaches aim to control prosthetic devices as assistive tools and do not yet strive to become rehabilitative tools for restoring lost motor function. We introduced a less invasive, implantable interface by applying epidural electrocorticography in a chronic stroke survivor with a persistent motor deficit. He was trained to modulate his natural motor-related oscillatory brain activity by receiving online feedback. Epidural recordings of field potentials in the beta-frequency band projecting onto the anatomical hand knob proved most successful in discriminating between the attempt to move the paralyzed hand and to rest. These spectral features allowed for fast and reliable control of the feedback device in an online closed-loop paradigm. Only seven training sessions were required to significantly improve maximum wrist extension. For patients suffering from severe motor deficits, epidural implants may decode and train the brain activity generated during attempts to move with high spatial resolution, thus facilitating specific and high-intensity practice even in the absence of motor control. This would thus transform them from pure assistive devices to restorative tools in the context of reinforcement learning and neurorehabilitation.

  2. Brain-Computer Interfaces Revolutionizing Human-Computer Interaction

    CERN Document Server

    Graimann, Bernhard; Allison, Brendan

    2010-01-01

    A brain-computer interface (BCI) establishes a direct output channel between the human brain and external devices. BCIs infer user intent via direct measures of brain activity and thus enable communication and control without movement. This book, authored by experts in the field, provides an accessible introduction to the neurophysiological and signal-processing background required for BCI, presents state-of-the-art non-invasive and invasive approaches, gives an overview of current hardware and software solutions, and reviews the most interesting as well as new, emerging BCI applications. The book is intended not only for students and young researchers, but also for newcomers and other readers from diverse backgrounds keen to learn about this vital scientific endeavour.

  3. Brain-computer interface for alertness estimation and improving

    Science.gov (United States)

    Hramov, Alexander; Maksimenko, Vladimir; Hramova, Marina

    2018-02-01

    Using wavelet analysis of the signals of electrical brain activity (EEG), we study the processes of neural activity, associated with perception of visual stimuli. We demonstrate that the brain can process visual stimuli in two scenarios: (i) perception is characterized by destruction of the alpha-waves and increase in the high-frequency (beta) activity, (ii) the beta-rhythm is not well pronounced, while the alpha-wave energy remains unchanged. The special experiments show that the motivation factor initiates the first scenario, explained by the increasing alertness. Based on the obtained results we build the brain-computer interface and demonstrate how the degree of the alertness can be estimated and controlled in real experiment.

  4. Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

    Science.gov (United States)

    Neftci, Emre O.; Pedroni, Bruno U.; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert

    2016-01-01

    Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware. PMID:27445650

  5. A multi-purpose brain-computer interface output device.

    Science.gov (United States)

    Thompson, David E; Huggins, Jane E

    2011-10-01

    While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as stand-alone communication and control systems, rather than as interfaces to existing systems built for these purposes. An individual communication and control system may be powerful or flexible, but no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCls could instead be used as an interface to these existing AT devices and products, which are designed for improving access and agency of people with disabilities and are highly configurable to individual user needs. However, interfacing with each AT device and program requires significant time and effort on the part of researchers and clinicians. This work presents the Multi-Purpose BCI Output Device (MBOD), a tool to help researchers and clinicians provide BCI control of many forms of AT in a plug-and-play fashion, i.e., without the installation of drivers or software on the AT device, and a proof-of-concept of the practicality of such an approach. The MBOD was designed to meet the goals of target device compatibility, BCI input device compatibility, convenience, and intuitive command structure. The MBOD was successfully used to interface a BCI with multiple AT devices (including two wheelchair seating systems), as well as computers running Windows (XP and 7), Mac and Ubuntu Linux operating systems.

  6. A Multi-purpose Brain-Computer Interface Output Device

    Science.gov (United States)

    Thompson, David E; Huggins, Jane E

    2012-01-01

    While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as standalone communication and control systems, rather than as interfaces to existing systems built for these purposes. While an individual communication and control system may be powerful or flexible, no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCIs could instead be used as an interface to these existing AT devices and products, which are designed for improving access and agency of people with disabilities and are highly configurable to individual user needs. However, interfacing with each AT device and program requires significant time and effort on the part of researchers and clinicians. This work presents the Multi-Purpose BCI Output Device (MBOD), a tool to help researchers and clinicians provide BCI control of many forms of AT in a plug-and-play fashion, i.e. without the installation of drivers or software on the AT device, and a proof-of-concept of the practicality of such an approach. The MBOD was designed to meet the goals of target device compatibility, BCI input device compatibility, convenience, and intuitive command structure. The MBOD was successfully used to interface a BCI with multiple AT devices (including two wheelchair seating systems), as well as computers running Windows (XP and 7), Mac and Ubuntu Linux operating systems. PMID:22208120

  7. Activities of IAEA related to human interface in man-machine system

    International Nuclear Information System (INIS)

    Nishiwaki, Yasushi

    1988-01-01

    The present paper outlines some activities of IAEA related to human interface in man-machine systems. It has been recognized for quite some time that in large and complex man-machine interactive systems human errors can contribute substantially to failures of these systems, and that the improvement in the human interface in man-machine systems is essential for the safety of the plant. Many important surveys have been made in some member countries. These studies and operational experience have shown that it is possible to substantially reduce this adverse impact of human errors in nuclear power plant operations by the application of human factors technology. This technology. This technology includes: (1) selection of people with the requisite skills and knowledge and providing them with job-relevant training, (2) maintenance of the necessary job qualifications for each person in the plant, (3) design of man-machine interfaces which are fully compatible with the capabilities and limitations of the people in the system, and (4) design of job operations, including written materials, to facilitate required quality of human performance. A review is made of education/training, operator support systems, human error data collection, analysis of safety significant events and future activities. (Nogami, K.)

  8. Flexible software architecture for user-interface and machine control in laboratory automation.

    Science.gov (United States)

    Arutunian, E B; Meldrum, D R; Friedman, N A; Moody, S E

    1998-10-01

    We describe a modular, layered software architecture for automated laboratory instruments. The design consists of a sophisticated user interface, a machine controller and multiple individual hardware subsystems, each interacting through a client-server architecture built entirely on top of open Internet standards. In our implementation, the user-interface components are built as Java applets that are downloaded from a server integrated into the machine controller. The user-interface client can thereby provide laboratory personnel with a familiar environment for experiment design through a standard World Wide Web browser. Data management and security are seamlessly integrated at the machine-controller layer using QNX, a real-time operating system. This layer also controls hardware subsystems through a second client-server interface. This architecture has proven flexible and relatively easy to implement and allows users to operate laboratory automation instruments remotely through an Internet connection. The software architecture was implemented and demonstrated on the Acapella, an automated fluid-sample-processing system that is under development at the University of Washington.

  9. Integrating robotic action with biologic perception: A brain-machine symbiosis theory

    Science.gov (United States)

    Mahmoudi, Babak

    In patients with motor disability the natural cyclic flow of information between the brain and external environment is disrupted by their limb impairment. Brain-Machine Interfaces (BMIs) aim to provide new communication channels between the brain and environment by direct translation of brain's internal states into actions. For enabling the user in a wide range of daily life activities, the challenge is designing neural decoders that autonomously adapt to different tasks, environments, and to changes in the pattern of neural activity. In this dissertation, a novel decoding framework for BMIs is developed in which a computational agent autonomously learns how to translate neural states into action based on maximization of a measure of shared goal between user and the agent. Since the agent and brain share the same goal, a symbiotic relationship between them will evolve therefore this decoding paradigm is called a Brain-Machine Symbiosis (BMS) framework. A decoding agent was implemented within the BMS framework based on the Actor-Critic method of Reinforcement Learning. The rule of the Actor as a neural decoder was to find mapping between the neural representation of motor states in the primary motor cortex (MI) and robot actions in order to solve reaching tasks. The Actor learned the optimal control policy using an evaluative feedback that was estimated by the Critic directly from the user's neural activity of the Nucleus Accumbens (NAcc). Through a series of computational neuroscience studies in a cohort of rats it was demonstrated that NAcc could provide a useful evaluative feedback by predicting the increase or decrease in the probability of earning reward based on the environmental conditions. Using a closed-loop BMI simulator it was demonstrated the Actor-Critic decoding architecture was able to adapt to different tasks as well as changes in the pattern of neural activity. The custom design of a dual micro-wire array enabled simultaneous implantation of MI and

  10. Light Stimulation Properties to Influence Brain Activity: A Brain-CoMputer Interface application

    NARCIS (Netherlands)

    Bieger, J.; Garcia Molina, G.

    2010-01-01

    Brain-Computer Interfaces (BCIs) enable people to control appliances without involving the normal output pathways of peripheral nervesand muscles. A particularly promising type of BCI is based on the Steady-State Visual Evoked Potential (SSVEP). Users can selectcommands by focusing their attention

  11. Design of Human-Machine Interface and altering of pelvic obliquity with RGR Trainer.

    Science.gov (United States)

    Pietrusinski, Maciej; Unluhisarcikli, Ozer; Mavroidis, Constantinos; Cajigas, Iahn; Bonato, Paolo

    2011-01-01

    The Robotic Gait Rehabilitation (RGR) Trainer targets secondary gait deviations in stroke survivors undergoing rehabilitation. Using an impedance control strategy and a linear electromagnetic actuator, the device generates a force field to control pelvic obliquity through a Human-Machine Interface (i.e. a lower body exoskeleton). Herein we describe the design of the RGR Trainer Human-Machine Interface (HMI) and we demonstrate the system's ability to alter the pattern of movement of the pelvis during gait in a healthy subject. Results are shown for experiments during which we induced hip-hiking - in healthy subjects. Our findings indicate that the RGR Trainer has the ability of affecting pelvic obliquity during gait. Furthermore, we provide preliminary evidence of short-term retention of the modified pelvic obliquity pattern induced by the RGR Trainer. © 2011 IEEE

  12. Design of Human – Machine Interface and Altering of Pelvic Obliquity with RGR Trainer

    Science.gov (United States)

    Pietrusinski, Maciej; Unluhisarcikli, Ozer; Mavroidis, Constantinos; Cajigas, Iahn; Bonato, Paolo

    2012-01-01

    The Robotic Gait Rehabilitation (RGR) Trainer targets secondary gait deviations in stroke survivors undergoing rehabilitation. Using an impedance control strategy and a linear electromagnetic actuator, the device generates a force field to control pelvic obliquity through a Human-Machine Interface (i.e. a lower body exoskeleton). Herein we describe the design of the RGR Trainer Human-Machine Interface (HMI) and we demonstrate the system’s ability to alter the pattern of movement of the pelvis during gait in a healthy subject. Results are shown for experiments during which we induced hip-hiking – in healthy subjects. Our findings indicate that the RGR Trainer has the ability of affecting pelvic obliquity during gait. Furthermore, we provide preliminary evidence of short-term retention of the modified pelvic obliquity pattern induced by the RGR Trainer. PMID:22275693

  13. All printed touchless human-machine interface based on only five functional materials

    Science.gov (United States)

    Scheipl, G.; Zirkl, M.; Sawatdee, A.; Helbig, U.; Krause, M.; Kraker, E.; Andersson Ersman, P.; Nilsson, D.; Platt, D.; Bodö, P.; Bauer, S.; Domann, G.; Mogessie, A.; Hartmann, Paul; Stadlober, B.

    2012-02-01

    We demonstrate the printing of a complex smart integrated system using only five functional inks: the fluoropolymer P(VDF:TrFE) (Poly(vinylidene fluoride trifluoroethylene) sensor ink, the conductive polymer PEDOT:PSS (poly(3,4 ethylenedioxythiophene):poly(styrene sulfonic acid) ink, a conductive carbon paste, a polymeric electrolyte and SU8 for separation. The result is a touchless human-machine interface, including piezo- and pyroelectric sensor pixels (sensitive to pressure changes and impinging infrared light), transistors for impedance matching and signal conditioning, and an electrochromic display. Applications may not only emerge in human-machine interfaces, but also in transient temperature or pressure sensing used in safety technology, in artificial skins and in disposable sensor labels.

  14. Human Reliability and the Current Dilemma in Human-Machine Interface Design Strategies

    International Nuclear Information System (INIS)

    Passalacqua, Roberto; Yamada, Fumiaki

    2002-01-01

    Since human error dominates the probability of failures of still-existing human-requiring systems (as the Monju reactor), the human-machine interface needs to be improved. Several rationales may lead to the conclusion that 'humans' should limit themselves to monitor the 'machine'. For example, this is the trend in the aviation industry: newest aircrafts are designed to be able to return to a safe state by the use of control systems, which do not need human intervention. Thus, the dilemma whether we really need operators (for example in the nuclear industry) might arise. However, social-technical approaches in recent human error analyses are pointing out the so-called 'organizational errors' and the importance of a human-machine interface harmonization. Typically plant's operators are a 'redundant' safety system with a much lower reliability (than the machine): organizational factors and harmonization requirements suggest designing the human-machine interface in a way that allows improvement of operator's reliability. In addition, taxonomy studies of accident databases have also proved that operators' training should promote processes of decision-making. This is accomplished in the latest trends of PSA technology by introducing the concept of a 'Safety Monitor' that is a computer-based tool that uses a level 1 PSA model of the plant. Operators and maintenance schedulers of the Monju FBR will be able to perform real-time estimations of the plant risk level. The main benefits are risk awareness and improvements in decision-making by operators. Also scheduled maintenance can be approached in a more rational (safe and economic) way. (authors)

  15. Comparison of four classification methods for brain-computer interface

    Czech Academy of Sciences Publication Activity Database

    Frolov, A.; Húsek, Dušan; Bobrov, P.

    2011-01-01

    Roč. 21, č. 2 (2011), s. 101-115 ISSN 1210-0552 R&D Projects: GA MŠk(CZ) 1M0567; GA ČR GA201/05/0079; GA ČR GAP202/10/0262 Institutional research plan: CEZ:AV0Z10300504 Keywords : brain computer interface * motor imagery * visual imagery * EEG pattern classification * Bayesian classification * Common Spatial Patterns * Common Tensor Discriminant Analysis Subject RIV: IN - Informatics, Computer Science Impact factor: 0.646, year: 2011

  16. Brain-computer interface after nervous system injury.

    Science.gov (United States)

    Burns, Alexis; Adeli, Hojjat; Buford, John A

    2014-12-01

    Brain-computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson's disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders. © The Author(s) 2014.

  17. A brain-computer interface controlled mail client.

    Science.gov (United States)

    Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Wang, Cong

    2013-01-01

    In this paper, we propose a brain-computer interface (BCI) based mail client. This system is controlled by hybrid features extracted from scalp-recorded electroencephalographic (EEG). We emulate the computer mouse by the motor imagery-based mu rhythm and the P300 potential. Furthermore, an adaptive P300 speller is included to provide text input function. With this BCI mail client, users can receive, read, write mails, as well as attach files in mail writing. The system has been tested on 3 subjects. Experimental results show that mail communication with this system is feasible.

  18. Towards Modern Inclusive Factories: A Methodology for the Development of Smart Adaptive Human-Machine Interfaces

    OpenAIRE

    Villani, Valeria; Sabattini, Lorenzo; Czerniak, Julia N.; Mertens, Alexander; Vogel-Heuser, Birgit; Fantuzzi, Cesare

    2017-01-01

    Modern manufacturing systems typically require high degrees of flexibility, in terms of ability to customize the production lines to the constantly changing market requests. For this purpose, manufacturing systems are required to be able to cope with changes in the types of products, and in the size of the production batches. As a consequence, the human-machine interfaces (HMIs) are typically very complex, and include a wide range of possible operational modes and commands. This generally imp...

  19. Materials and optimized designs for human-machine interfaces via epidermal electronics.

    Science.gov (United States)

    Jeong, Jae-Woong; Yeo, Woon-Hong; Akhtar, Aadeel; Norton, James J S; Kwack, Young-Jin; Li, Shuo; Jung, Sung-Young; Su, Yewang; Lee, Woosik; Xia, Jing; Cheng, Huanyu; Huang, Yonggang; Choi, Woon-Seop; Bretl, Timothy; Rogers, John A

    2013-12-17

    Thin, soft, and elastic electronics with physical properties well matched to the epidermis can be conformally and robustly integrated with the skin. Materials and optimized designs for such devices are presented for surface electromyography (sEMG). The findings enable sEMG from wide ranging areas of the body. The measurements have quality sufficient for advanced forms of human-machine interface. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. US Liquid Metal Fast Breeder Reactor man-machine interface program

    International Nuclear Information System (INIS)

    Vaurio, J.K.; Change, S.A.

    1982-01-01

    The US LMFBR Man-Machine Interface Program is supportive to and an integral part of the LMFBR Safety Program. This paper describes the goal and objectives of the program, and the necessary research and development efforts with a logical structure for the orderly and timely implementation of the prgoram. Current status and near-term and long-term priority activities are also summarized

  1. A Tool for Assessing the Text Legibility of Digital Human Machine Interfaces

    Energy Technology Data Exchange (ETDEWEB)

    Roger Lew; Ronald L. Boring; Thomas A. Ulrich

    2015-08-01

    A tool intended to aid qualified professionals in the assessment of the legibility of text presented on a digital display is described. The assessment of legibility is primarily for the purposes of designing and analyzing human machine interfaces in accordance with NUREG-0700 and MIL-STD 1472G. The tool addresses shortcomings of existing guidelines by providing more accurate metrics of text legibility with greater sensitivity to design alternatives.

  2. Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

    International Nuclear Information System (INIS)

    Arjona, Cristian; Pentácolo, José; Gareis, Iván; Atum, Yanina; Gentiletti, Gerardo; Acevedo, Rubén; Rufiner, Leonardo

    2011-01-01

    The Brain Computer Interface (BCI) translates brain activity into computer commands. To increase the performance of the BCI, to decode the user intentions it is necessary to get better the feature extraction and classification techniques. In this article the performance of a three linear discriminant analysis (LDA) classifiers ensemble is studied. The system based on ensemble can theoretically achieved better classification results than the individual counterpart, regarding individual classifier generation algorithm and the procedures for combine their outputs. Classic algorithms based on ensembles such as bagging and boosting are discussed here. For the application on BCI, it was concluded that the generated results using ER and AUC as performance index do not give enough information to establish which configuration is better.

  3. Brain-computer interfaces current trends and applications

    CERN Document Server

    Azar, Ahmad

    2015-01-01

    The success of a BCI system depends as much on the system itself as on the user’s ability to produce distinctive EEG activity. BCI systems can be divided into two groups according to the placement of the electrodes used to detect and measure neurons firing in the brain. These groups are: invasive systems, electrodes are inserted directly into the cortex are used for single cell or multi unit recording, and electrocorticography (EcoG), electrodes are placed on the surface of the cortex (or dura); noninvasive systems, they are placed on the scalp and use electroencephalography (EEG) or magnetoencephalography (MEG) to detect neuron activity. The book is basically divided into three parts. The first part of the book covers the basic concepts and overviews of Brain Computer Interface. The second part describes new theoretical developments of BCI systems. The third part covers views on real applications of BCI systems.

  4. Designing a hands-on brain computer interface laboratory course.

    Science.gov (United States)

    Khalighinejad, Bahar; Long, Laura Kathleen; Mesgarani, Nima

    2016-08-01

    Devices and systems that interact with the brain have become a growing field of research and development in recent years. Engineering students are well positioned to contribute to both hardware development and signal analysis techniques in this field. However, this area has been left out of most engineering curricula. We developed an electroencephalography (EEG) based brain computer interface (BCI) laboratory course to educate students through hands-on experiments. The course is offered jointly by the Biomedical Engineering, Electrical Engineering, and Computer Science Departments of Columbia University in the City of New York and is open to senior undergraduate and graduate students. The course provides an effective introduction to the experimental design, neuroscience concepts, data analysis techniques, and technical skills required in the field of BCI.

  5. Brain-computer interfaces for EEG neurofeedback: peculiarities and solutions.

    Science.gov (United States)

    Huster, René J; Mokom, Zacharais N; Enriquez-Geppert, Stefanie; Herrmann, Christoph S

    2014-01-01

    Neurofeedback training procedures designed to alter a person's brain activity have been in use for nearly four decades now and represent one of the earliest applications of brain-computer interfaces (BCI). The majority of studies using neurofeedback technology relies on recordings of the electroencephalogram (EEG) and applies neurofeedback in clinical contexts, exploring its potential as treatment for psychopathological syndromes. This clinical focus significantly affects the technology behind neurofeedback BCIs. For example, in contrast to other BCI applications, neurofeedback BCIs usually rely on EEG-derived features with only a minimum of additional processing steps being employed. Here, we highlight the peculiarities of EEG-based neurofeedback BCIs and consider their relevance for software implementations. Having reviewed already existing packages for the implementation of BCIs, we introduce our own solution which specifically considers the relevance of multi-subject handling for experimental and clinical trials, for example by implementing ready-to-use solutions for pseudo-/sham-neurofeedback. © 2013.

  6. Software and man-machine interface considerations for a nuclear plant computer replacement and upgrade project

    International Nuclear Information System (INIS)

    Diamond, G.; Robinson, E.

    1984-01-01

    Some of the key software functions and Man-Machine Interface considerations in a computer replacement and upgrade project for a nuclear power plant are described. The project involves the installation of two separate computer systems: an Emergency Response Facilities Computer System (ERFCS) and a Plant Process Computer System (PPCS). These systems employ state-of-the-art computer hardware and software. The ERFCS is a new system intended to provide enhanced functions to meet NRC post-TMI guidelines. The PPCS is intended to replace and upgrade an existing obsolete plant computer system. A general overview of the hardware and software aspects of the replacement and upgrade is presented. The work done to develop the upgraded Man-Machine Interface is described. For the ERFCS, a detailed discussion is presented of the work done to develop logic to evaluate the readiness and performance of safety systems and their supporting functions. The Man-Machine Interface considerations of reporting readiness and performance to the operator are discussed. Finally, the considerations involved in the implementation of this logic in real-time software are discussed.. For the PPCS, a detailed discussion is presented of some new features

  7. Human-Machine interface for off normal and emergency situations in nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    Kwon, Kee Choon [Korea Atomic Energy Research Institute, Taejeon (Korea)

    2000-01-01

    Many nuclear power plants (NPPs) have reported that a high percentage of all major failures in the plants are caused by human errors. Therefore, there has been much focus on elimination of human errors, enhancement of human performance, and general improvement of human machine interface (HMI). Both the utility management and the regulators are demanding improvement in this area. The International Atomic Energy Agency (IAEA) Specialists' Meeting on 'Human-Machine Interface for Off Normal and Emergency Situations in Nuclear Power Plants' was co-organized by the Korea Atomic Energy Research Institute (KAERI) and the Korea Power Engineering Company, INC (KOPEC), and took place in Taejeon, Republic of Korea, 1999 October 26-28. Fifty eight participants, representing nine member countries reviewed recent developments and discussed directions for future efforts in the Human-Machine Interface for Off Normal and Emergency Situations in NPPs. Twenty papers were presented, covering a wide spectrum of technical and scientific subjects including recent experience and benefits from Operational Experience with HMI, Development of HMI System, Licensing Issues for HMI and Future Development and Trends. (Author)

  8. Human-machine interface for a VR-based medical imaging environment

    Science.gov (United States)

    Krapichler, Christian; Haubner, Michael; Loesch, Andreas; Lang, Manfred K.; Englmeier, Karl-Hans

    1997-05-01

    Modern 3D scanning techniques like magnetic resonance imaging (MRI) or computed tomography (CT) produce high- quality images of the human anatomy. Virtual environments open new ways to display and to analyze those tomograms. Compared with today's inspection of 2D image sequences, physicians are empowered to recognize spatial coherencies and examine pathological regions more facile, diagnosis and therapy planning can be accelerated. For that purpose a powerful human-machine interface is required, which offers a variety of tools and features to enable both exploration and manipulation of the 3D data. Man-machine communication has to be intuitive and efficacious to avoid long accustoming times and to enhance familiarity with and acceptance of the interface. Hence, interaction capabilities in virtual worlds should be comparable to those in the real work to allow utilization of our natural experiences. In this paper the integration of hand gestures and visual focus, two important aspects in modern human-computer interaction, into a medical imaging environment is shown. With the presented human- machine interface, including virtual reality displaying and interaction techniques, radiologists can be supported in their work. Further, virtual environments can even alleviate communication between specialists from different fields or in educational and training applications.

  9. Human-Machine interface for off normal and emergency situations in nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    Kwon, Kee Choon [Korea Atomic Energy Research Institute, Taejeon (Korea)

    2000-01-01

    Many nuclear power plants (NPPs) have reported that a high percentage of all major failures in the plants are caused by human errors. Therefore, there has been much focus on elimination of human errors, enhancement of human performance, and general improvement of human machine interface (HMI). Both the utility management and the regulators are demanding improvement in this area. The International Atomic Energy Agency (IAEA) Specialists' Meeting on 'Human-Machine Interface for Off Normal and Emergency Situations in Nuclear Power Plants' was co-organized by the Korea Atomic Energy Research Institute (KAERI) and the Korea Power Engineering Company, INC (KOPEC), and took place in Taejeon, Republic of Korea, 1999 October 26-28. Fifty eight participants, representing nine member countries reviewed recent developments and discussed directions for future efforts in the Human-Machine Interface for Off Normal and Emergency Situations in NPPs. Twenty papers were presented, covering a wide spectrum of technical and scientific subjects including recent experience and benefits from Operational Experience with HMI, Development of HMI System, Licensing Issues for HMI and Future Development and Trends. (Author)

  10. [Brain-Computer Interface: the First Clinical Experience in Russia].

    Science.gov (United States)

    Mokienko, O A; Lyukmanov, R Kh; Chernikova, L A; Suponeva, N A; Piradov, M A; Frolov, A A

    2016-01-01

    Motor imagery is suggested to stimulate the same plastic mechanisms in the brain as a real movement. The brain-computer interface (BCI) controls motor imagery by converting EEG during this process into the commands for an external device. This article presents the results of two-stage study of the clinical use of non-invasive BCI in the rehabilitation of patients with severe hemiparesis caused by focal brain damage. It was found that the ability to control BCI did not depend on the duration of a disease, brain lesion localization and the degree of neurological deficit. The first step of the study involved 36 patients; it showed that the efficacy of rehabilitation was higher in the group with the use of BCI (the score on the Action Research Arm Test (ARAT) improved from 1 [0; 2] to 5 [0; 16] points, p = 0.012; no significant improvement was observed in control group). The second step of the study involved 19 patients; the complex BCI-exoskeleton (i.e. with the kinesthetic feedback) was used for motor imagery trainings. The improvement of the motor function of hands was proved by ARAT (the score improved from 2 [0; 37] to 4 [1; 45:5] points, p = 0.005) and Fugl-Meyer scale (from 72 [63; 110 ] to 79 [68; 115] points, p = 0.005).

  11. A brain-computer interface to support functional recovery.

    Science.gov (United States)

    Kjaer, Troels W; Sørensen, Helge B

    2013-01-01

    Brain-computer interfaces (BCI) register changes in brain activity and utilize this to control computers. The most widely used method is based on registration of electrical signals from the cerebral cortex using extracranially placed electrodes also called electroencephalography (EEG). The features extracted from the EEG may, besides controlling the computer, also be fed back to the patient for instance as visual input. This facilitates a learning process. BCI allow us to utilize brain activity in the rehabilitation of patients after stroke. The activity of the cerebral cortex varies with the type of movement we imagine, and by letting the patient know the type of brain activity best associated with the intended movement the rehabilitation process may be faster and more efficient. The focus of BCI utilization in medicine has changed in recent years. While we previously focused on devices facilitating communication in the rather few patients with locked-in syndrome, much interest is now devoted to the therapeutic use of BCI in rehabilitation. For this latter group of patients, the device is not intended to be a lifelong assistive companion but rather a 'teacher' during the rehabilitation period. Copyright © 2013 S. Karger AG, Basel.

  12. Evolution of brain-computer interfaces: going beyond classic motor physiology

    Science.gov (United States)

    Leuthardt, Eric C.; Schalk, Gerwin; Roland, Jarod; Rouse, Adam; Moran, Daniel W.

    2010-01-01

    The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future. PMID:19569892

  13. DESIGN AND EVALUATION OF INDIVIDUAL ELEMENTS OF THE INTERFACE FOR AN AGRICULTURAL MACHINE.

    Science.gov (United States)

    Rakhra, Aadesh K; Mann, Danny D

    2018-01-29

    If a user-centered approach is not used to design information displays, the quantity and quality of information presented to the user may not match the needs of the user, or it may exceed the capability of the human operator for processing and using that information. The result may be an excessive mental workload and reduced situation awareness of the operator, which can negatively affect the machine performance and operational outcomes. The increasing use of technology in agricultural machines may expose the human operator to excessive and undesirable information if the operator's information needs and information processing capabilities are ignored. In this study, a user-centered approach was used to design specific interface elements for an agricultural air seeder. Designs of the interface elements were evaluated in a laboratory environment by developing high-fidelity prototypes. Evaluations of the user interface elements yielded significant improvement in situation awareness (up to 11%; overall mean difference = 5.0 (4.8%), 95% CI (6.4728, 3.5939), p 0.0001). Mental workload was reduced by up to 19.7% (overall mean difference = -5.2 (-7.9%), n = 30, a = 0.05). Study participants rated the overall performance of the newly designed user-centered interface elements higher in comparison to the previous designs (overall mean difference = 27.3 (189.8%), 99% CI (35.150, 19.384), p 0.0001. Copyright© by the American Society of Agricultural Engineers.

  14. A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

    Directory of Open Access Journals (Sweden)

    Rita Melo

    2016-07-01

    Full Text Available Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM, for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

  15. Encoder-decoder optimization for brain-computer interfaces.

    Science.gov (United States)

    Merel, Josh; Pianto, Donald M; Cunningham, John P; Paninski, Liam

    2015-06-01

    Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

  16. Mental workload during brain-computer interface training.

    Science.gov (United States)

    Felton, Elizabeth A; Williams, Justin C; Vanderheiden, Gregg C; Radwin, Robert G

    2012-01-01

    It is not well understood how people perceive the difficulty of performing brain-computer interface (BCI) tasks, which specific aspects of mental workload contribute the most, and whether there is a difference in perceived workload between participants who are able-bodied and disabled. This study evaluated mental workload using the NASA Task Load Index (TLX), a multi-dimensional rating procedure with six subscales: Mental Demands, Physical Demands, Temporal Demands, Performance, Effort, and Frustration. Able-bodied and motor disabled participants completed the survey after performing EEG-based BCI Fitts' law target acquisition and phrase spelling tasks. The NASA-TLX scores were similar for able-bodied and disabled participants. For example, overall workload scores (range 0-100) for 1D horizontal tasks were 48.5 (SD = 17.7) and 46.6 (SD 10.3), respectively. The TLX can be used to inform the design of BCIs that will have greater usability by evaluating subjective workload between BCI tasks, participant groups, and control modalities. Mental workload of brain-computer interfaces (BCI) can be evaluated with the NASA Task Load Index (TLX). The TLX is an effective tool for comparing subjective workload between BCI tasks, participant groups (able-bodied and disabled), and control modalities. The data can inform the design of BCIs that will have greater usability.

  17. Region based Brain Computer Interface for a home control application.

    Science.gov (United States)

    Akman Aydin, Eda; Bay, Omer Faruk; Guler, Inan

    2015-08-01

    Environment control is one of the important challenges for disabled people who suffer from neuromuscular diseases. Brain Computer Interface (BCI) provides a communication channel between the human brain and the environment without requiring any muscular activation. The most important expectation for a home control application is high accuracy and reliable control. Region-based paradigm is a stimulus paradigm based on oddball principle and requires selection of a target at two levels. This paper presents an application of region based paradigm for a smart home control application for people with neuromuscular diseases. In this study, a region based stimulus interface containing 49 commands was designed. Five non-disabled subjects were attended to the experiments. Offline analysis results of the experiments yielded 95% accuracy for five flashes. This result showed that region based paradigm can be used to select commands of a smart home control application with high accuracy in the low number of repetitions successfully. Furthermore, a statistically significant difference was not observed between the level accuracies.

  18. Encoder-decoder optimization for brain-computer interfaces.

    Directory of Open Access Journals (Sweden)

    Josh Merel

    2015-06-01

    Full Text Available Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model" and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

  19. Towards SSVEP-based, portable, responsive Brain-Computer Interface.

    Science.gov (United States)

    Kaczmarek, Piotr; Salomon, Pawel

    2015-08-01

    A Brain-Computer Interface in motion control application requires high system responsiveness and accuracy. SSVEP interface consisted of 2-8 stimuli and 2 channel EEG amplifier was presented in this paper. The observed stimulus is recognized based on a canonical correlation calculated in 1 second window, ensuring high interface responsiveness. A threshold classifier with hysteresis (T-H) was proposed for recognition purposes. Obtained results suggest that T-H classifier enables to significantly increase classifier performance (resulting in accuracy of 76%, while maintaining average false positive detection rate of stimulus different then observed one between 2-13%, depending on stimulus frequency). It was shown that the parameters of T-H classifier, maximizing true positive rate, can be estimated by gradient-based search since the single maximum was observed. Moreover the preliminary results, performed on a test group (N=4), suggest that for T-H classifier exists a certain set of parameters for which the system accuracy is similar to accuracy obtained for user-trained classifier.

  20. Brain-computer interfaces increase whole-brain signal to noise.

    Science.gov (United States)

    Papageorgiou, T Dorina; Lisinski, Jonathan M; McHenry, Monica A; White, Jason P; LaConte, Stephen M

    2013-08-13

    Brain-computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCI-based control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to measure the quality of covert task performance. We used whole-brain classifier-based real-time functional MRI to address these issues, because the method provides both classifier-based maps to examine the neural requirements of BCI and classification accuracy to quantify the quality of task performance. Subjects performed a covert counting task at fast and slow rates to control a visual interface. Compared with the same task when viewing but not controlling the interface, we observed that being in control of a BCI improved task classification of fast and slow counting states. Additional BCI control increased subjects' whole-brain signal-to-noise ratio compared with the absence of control. The neural pattern for control consisted of a positive network comprised of dorsal parietal and frontal regions and the anterior insula of the right hemisphere as well as an expansive negative network of regions. These findings suggest that real-time functional MRI can serve as a platform for exploring information processing and frontoparietal and insula network-based regulation of whole-brain task signal-to-noise ratio.

  1. Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals

    Science.gov (United States)

    Eugster, Manuel J. A.; Ruotsalo, Tuukka; Spapé, Michiel M.; Barral, Oswald; Ravaja, Niklas; Jacucci, Giulio; Kaski, Samuel

    2016-01-01

    Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user’s interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recommending information by relevance inferred directly from brain signals. In experiments, participants were asked to read Wikipedia documents about a selection of topics while their EEG was recorded. Based on the prediction of word relevance, the individual’s search intent was modeled and successfully used for retrieving new relevant documents from the whole English Wikipedia corpus. The results show that the users’ interests toward digital content can be modeled from the brain signals evoked by reading. The introduced brain-relevance paradigm enables the recommendation of information without any explicit user interaction and may be applied across diverse information-intensive applications. PMID:27929077

  2. Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors

    Directory of Open Access Journals (Sweden)

    Parth Gargava

    2017-08-01

    Full Text Available A Brain Computer Interface (BCI is developed to navigate a micro-controller based robot using Emotiv sensors. The BCI system has a pipeline of 5 stages- signal acquisition, pre-processing, feature extraction, classification and CUDA inter- facing. It shall aid in serving a prototype for physical movement of neurological patients who are unable to control or operate on their muscular movements. All stages of the pipeline are designed to process bodily actions like eye blinks to command navigation of the robot. This prototype works on features learning and classification centric techniques using support vector machine. The suggested pipeline, ensures successful navigation of a robot in four directions in real time with accuracy of 93 percent.

  3. Brain-computer interface using P300 and virtual reality: a gaming approach for treating ADHD.

    Science.gov (United States)

    Rohani, Darius Adam; Sorensen, Helge B D; Puthusserypady, Sadasivan

    2014-01-01

    This paper presents a novel brain-computer interface (BCI) system aiming at the rehabilitation of attention-deficit/hyperactive disorder in children. It uses the P300 potential in a series of feedback games to improve the subjects' attention. We applied a support vector machine (SVM) using temporal and template-based features to detect these P300 responses. In an experimental setup using five subjects, an average error below 30% was achieved. To make it more challenging the BCI system has been embedded inside an immersive 3D virtual reality (VR) classroom with simulated distractions, which was created by combining a low-cost infrared camera and an "off-axis perspective projection" algorithm. This system is intended for kids by operating with four electrodes, as well as a non-intrusive VR setting. With the promising results, and considering the simplicity of the scheme, we hope to encourage future studies to adapt the techniques presented in this study.

  4. Use of models and mockups in verifying man-machine interfaces

    International Nuclear Information System (INIS)

    Seminara, J.L.

    1985-01-01

    The objective of Human Factors Engineering is to tailor the design of facilities and equipment systems to match the capabilities and limitations of the personnel who will operate and maintain the system. This optimization of the man-machine interface is undertaken to enhance the prospects for safe, reliable, timely, and error-free human performance in meeting system objectives. To ensure the eventual success of a complex man-machine system it is important to systematically and progressively test and verify the adequacy of man-machine interfaces from initial design concepts to system operation. Human factors specialists employ a variety of methods to evaluate the quality of the human-system interface. These methods include: (1) Reviews of two-dimensional drawings using appropriately scaled transparent overlays of personnel spanning the anthropometric range, considering clothing and protective gear encumbrances (2) Use of articulated, scaled, plastic templates or manikins that are overlayed on equipment or facility drawings (3) Development of computerized manikins in computer aided design approaches (4) Use of three-dimensional scale models to better conceptualize work stations, control rooms or maintenance facilities (5) Full or half-scale mockups of system components to evaluate operator/maintainer interfaces (6) Part of full-task dynamic simulation of operator or maintainer tasks and interactive system responses (7) Laboratory and field research to establish human performance capabilities with alternative system design concepts or configurations. Of the design verification methods listed above, this paper will only consider the use of models and mockups in the design process

  5. Neuroanatomical correlates of brain-computer interface performance.

    Science.gov (United States)

    Kasahara, Kazumi; DaSalla, Charles Sayo; Honda, Manabu; Hanakawa, Takashi

    2015-04-15

    Brain-computer interfaces (BCIs) offer a potential means to replace or restore lost motor function. However, BCI performance varies considerably between users, the reasons for which are poorly understood. Here we investigated the relationship between sensorimotor rhythm (SMR)-based BCI performance and brain structure. Participants were instructed to control a computer cursor using right- and left-hand motor imagery, which primarily modulated their left- and right-hemispheric SMR powers, respectively. Although most participants were able to control the BCI with success rates significantly above chance level even at the first encounter, they also showed substantial inter-individual variability in BCI success rate. Participants also underwent T1-weighted three-dimensional structural magnetic resonance imaging (MRI). The MRI data were subjected to voxel-based morphometry using BCI success rate as an independent variable. We found that BCI performance correlated with gray matter volume of the supplementary motor area, supplementary somatosensory area, and dorsal premotor cortex. We suggest that SMR-based BCI performance is associated with development of non-primary somatosensory and motor areas. Advancing our understanding of BCI performance in relation to its neuroanatomical correlates may lead to better customization of BCIs based on individual brain structure. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. An optical brain computer interface for environmental control.

    Science.gov (United States)

    Ayaz, Hasan; Shewokis, Patricia A; Bunce, Scott; Onaral, Banu

    2011-01-01

    A brain computer interface (BCI) is a system that translates neurophysiological signals detected from the brain to supply input to a computer or to control a device. Volitional control of neural activity and its real-time detection through neuroimaging modalities are key constituents of BCI systems. The purpose of this study was to develop and test a new BCI design that utilizes intention-related cognitive activity within the dorsolateral prefrontal cortex using functional near infrared (fNIR) spectroscopy. fNIR is a noninvasive, safe, portable and affordable optical technique with which to monitor hemodynamic changes, in the brain's cerebral cortex. Because of its portability and ease of use, fNIR is amenable to deployment in ecologically valid natural working environments. We integrated a control paradigm in a computerized 3D virtual environment to augment interactivity. Ten healthy participants volunteered for a two day study in which they navigated a virtual environment with keyboard inputs, but were required to use the fNIR-BCI for interaction with virtual objects. Results showed that participants consistently utilized the fNIR-BCI with an overall success rate of 84% and volitionally increased their cerebral oxygenation level to trigger actions within the virtual environment.

  7. A Review of Hybrid Brain-Computer Interface Systems

    Directory of Open Access Journals (Sweden)

    Setare Amiri

    2013-01-01

    Full Text Available Increasing number of research activities and different types of studies in brain-computer interface (BCI systems show potential in this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns, feature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs have not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability. A new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with different brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages of each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and the information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after introducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine them, and their advantages and disadvantages.

  8. Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA

    OpenAIRE

    Deng, Li; Wang, Guohua; Yu, Suihuai

    2016-01-01

    In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive cha...

  9. Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

    Science.gov (United States)

    Davatzikos, Christos

    2016-10-01

    The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. Copyright © 2016. Published by Elsevier B.V.

  10. A Brain-Machine-Brain Interface for Rewiring of Cortical Circuitry after Traumatic Brain Injury

    Science.gov (United States)

    2015-11-01

    simplicity, with 15b representation at its internal nodes to mitigate quantization noise effects on template signal accuracy [18]. The digitized/filtered...HCF was adjustable in the range of 6.4–12.3 kHz. In the right plot, note how the Gm-C HPF effectively removed the flicker noise contribution, when the

  11. A Brain-Machine-Brain Interface for Rewiring of Cortical Circuitry after Traumatic Brain Injury

    Science.gov (United States)

    2015-11-01

    developed a paradigm for testing neurophysiological changes within pre- motor cortex (PM) of the rat (RFA, rostral forelimb area) resulting from distant...Performed anatomical studies in healthy rats using tract-tracers to compare with CCI rats undergoing ADS  Performed first CCI study in motor cortex ...Nudo “Reorganization of motor cortex after controlled cortical impact in rats and implications for functional recovery,” J Neurotrauma, vol. 27, pp

  12. Evaluasi Human Machine Interface Menggunakan Kriteria Usability Pada Sistem E-learning Perguruan Tinggi

    Directory of Open Access Journals (Sweden)

    Akhmad Qashlim

    2016-01-01

    Full Text Available Integration HMI with usability in user interface design process is a standart of the success of a website. The design process is done through the approach to the end user to find a problem solution of human machine interface phenomena. It can also generate the maximum level of satisfaction and success of implementation of the website. The purpose of this research is to evaluate HMI using usabilitycriteria to know the application of HMI concept in e-learning and provide proposals for improvements to the HMI. Questionnaire Data were processed using a descriptive analysis and methods of CFA to know the variables that are weakest and which indicators have an important role in shaping the research variables. Evaluation results indicate the application concept of HMI in the e-learning had been done but not the maximum. Data analysis of the results obtained that the main problem lies in the accessibility criteria in the meantime indicator latent variables from forming error prevention, learnability, memorability, visibility and accessibility of influential factor loading values indicated significantly (unidimensionalitas in shaping the criteria of latent variables in first-order CFA. The end result of this research is the proposal of improvement as a HMI solution in the form of principles and technicsuser interface design. This solution is focused on the development of standards for the quality of the interface in e-learning systems and not on the digital learning content presented on the e-learning system. Keywords: Descriptive analisis; Human machine interface; Usability; Confirmatory factor analisys; Elearning

  13. Integration of an intelligent systems behavior simulator and a scalable soldier-machine interface

    Science.gov (United States)

    Johnson, Tony; Manteuffel, Chris; Brewster, Benjamin; Tierney, Terry

    2007-04-01

    As the Army's Future Combat Systems (FCS) introduce emerging technologies and new force structures to the battlefield, soldiers will increasingly face new challenges in workload management. The next generation warfighter will be responsible for effectively managing robotic assets in addition to performing other missions. Studies of future battlefield operational scenarios involving the use of automation, including the specification of existing and proposed technologies, will provide significant insight into potential problem areas regarding soldier workload. The US Army Tank Automotive Research, Development, and Engineering Center (TARDEC) is currently executing an Army technology objective program to analyze and evaluate the effect of automated technologies and their associated control devices with respect to soldier workload. The Human-Robotic Interface (HRI) Intelligent Systems Behavior Simulator (ISBS) is a human performance measurement simulation system that allows modelers to develop constructive simulations of military scenarios with various deployments of interface technologies in order to evaluate operator effectiveness. One such interface is TARDEC's Scalable Soldier-Machine Interface (SMI). The scalable SMI provides a configurable machine interface application that is capable of adapting to several hardware platforms by recognizing the physical space limitations of the display device. This paper describes the integration of the ISBS and Scalable SMI applications, which will ultimately benefit both systems. The ISBS will be able to use the Scalable SMI to visualize the behaviors of virtual soldiers performing HRI tasks, such as route planning, and the scalable SMI will benefit from stimuli provided by the ISBS simulation environment. The paper describes the background of each system and details of the system integration approach.

  14. Proprioceptive feedback and brain computer interface (BCI based neuroprostheses.

    Directory of Open Access Journals (Sweden)

    Ander Ramos-Murguialday

    Full Text Available Brain computer interface (BCI technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1 motor imagery of the hand movement without any overt movement and without feedback, (2 motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3 passive (the orthosis passively opens and closes the hand without imagery and (4 active (overt movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants. Group 1 (n = 9 received contingent positive feedback (participants' sensorimotor rhythm (SMR desynchronization was directly linked to hand orthosis movements, group 2 (n = 8 contingent "negative" feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements and group 3 (n = 7 sham feedback (no link between brain oscillations and orthosis movements. We observed that proprioceptive feedback (feeling and seeing hand movements improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and

  15. Proprioceptive feedback and brain computer interface (BCI) based neuroprostheses.

    Science.gov (United States)

    Ramos-Murguialday, Ander; Schürholz, Markus; Caggiano, Vittorio; Wildgruber, Moritz; Caria, Andrea; Hammer, Eva Maria; Halder, Sebastian; Birbaumer, Niels

    2012-01-01

    Brain computer interface (BCI) technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1) motor imagery of the hand movement without any overt movement and without feedback, (2) motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3) passive (the orthosis passively opens and closes the hand without imagery) and (4) active (overt) movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants). Group 1 (n = 9) received contingent positive feedback (participants' sensorimotor rhythm (SMR) desynchronization was directly linked to hand orthosis movements), group 2 (n = 8) contingent "negative" feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements) and group 3 (n = 7) sham feedback (no link between brain oscillations and orthosis movements). We observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and passive

  16. A novel human-machine interface based on recognition of multi-channel facial bioelectric signals

    International Nuclear Information System (INIS)

    Razazadeh, Iman Mohammad; Firoozabadi, S. Mohammad; Golpayegani, S.M.R.H.; Hu, H.

    2011-01-01

    Full text: This paper presents a novel human-machine interface for disabled people to interact with assistive systems for a better quality of life. It is based on multichannel forehead bioelectric signals acquired by placing three pairs of electrodes (physical channels) on the Fron-tails and Temporalis facial muscles. The acquired signals are passes through a parallel filter bank to explore three different sub-bands related to facial electromyogram, electrooculogram and electroencephalogram. The root mean features of the bioelectric signals analyzed within non-overlapping 256 ms windows were extracted. The subtractive fuzzy c-means clustering method (SFCM) was applied to segment the feature space and generate initial fuzzy based Takagi-Sugeno rules. Then, an adaptive neuro-fuzzy inference system is exploited to tune up the premises and consequence parameters of the extracted SFCMs. rules. The average classifier discriminating ratio for eight different facial gestures (smiling, frowning, pulling up left/right lips corner, eye movement to left/right/up/down is between 93.04% and 96.99% according to different combinations and fusions of logical features. Experimental results show that the proposed interface has a high degree of accuracy and robustness for discrimination of 8 fundamental facial gestures. Some potential and further capabilities of our approach in human-machine interfaces are also discussed. (author)

  17. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

    Science.gov (United States)

    Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

    2016-01-01

    Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.

  18. Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces.

    Science.gov (United States)

    Grissmann, Sebastian; Zander, Thorsten O; Faller, Josef; Brönstrup, Jonas; Kelava, Augustin; Gramann, Klaus; Gerjets, Peter

    2017-01-01

    Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC) was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios.

  19. Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Sebastian Grissmann

    2017-07-01

    Full Text Available Most brain-computer interfaces (BCIs focus on detecting single aspects of user states (e.g., motor imagery in the electroencephalogram (EEG in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios.

  20. Ethics in published brain-computer interface research

    Science.gov (United States)

    Specker Sullivan, L.; Illes, J.

    2018-02-01

    Objective. Sophisticated signal processing has opened the doors to more research with human subjects than ever before. The increase in the use of human subjects in research comes with a need for increased human subjects protections. Approach. We quantified the presence or absence of ethics language in published reports of brain-computer interface (BCI) studies that involved human subjects and qualitatively characterized ethics statements. Main results. Reports of BCI studies with human subjects that are published in neural engineering and engineering journals are anchored in the rationale of technological improvement. Ethics language is markedly absent, omitted from 31% of studies published in neural engineering journals and 59% of studies in biomedical engineering journals. Significance. As the integration of technological tools with the capacities of the mind deepens, explicit attention to ethical issues will ensure that broad human benefit is embraced and not eclipsed by technological exclusiveness.

  1. Role of the man-machine interface in accident management strategies

    International Nuclear Information System (INIS)

    Oewre, Fridtjov

    2001-01-01

    First, this paper gives a short general review on important safety issues in the field of man-machine interaction as expressed by important nuclear safety organisations. Then follows a summary discussion on what constitutes a modern Man-Machine Interface (MMI) and what is normally meant with accident management and accident management strategies. Furthermore, the paper focuses on three major issues in the context of accident management. First, the need for reliable information in accidents and how this can be obtained by additional computer technology. Second, the use of procedures is discussed, and basic MMI aspects of computer support for procedure presentation are identified followed by a presentation of a new approach on how to computerise procedures. Third, typical information needs for characteristic end-users in accidents, such as the control room operators, technical support staff and plant emergency teams, is discussed. Some ideas on how to apply virtual reality technology in accident management is also presented

  2. Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces

    Science.gov (United States)

    Herbert, Robert; Kim, Jong-Hoon; Kim, Yun Soung; Lee, Hye Moon

    2018-01-01

    Flexible hybrid electronics (FHE), designed in wearable and implantable configurations, have enormous applications in advanced healthcare, rapid disease diagnostics, and persistent human-machine interfaces. Soft, contoured geometries and time-dynamic deformation of the targeted tissues require high flexibility and stretchability of the integrated bioelectronics. Recent progress in developing and engineering soft materials has provided a unique opportunity to design various types of mechanically compliant and deformable systems. Here, we summarize the required properties of soft materials and their characteristics for configuring sensing and substrate components in wearable and implantable devices and systems. Details of functionality and sensitivity of the recently developed FHE are discussed with the application areas in medicine, healthcare, and machine interactions. This review concludes with a discussion on limitations of current materials, key requirements for next generation materials, and new application areas. PMID:29364861

  3. Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces

    Directory of Open Access Journals (Sweden)

    Robert Herbert

    2018-01-01

    Full Text Available Flexible hybrid electronics (FHE, designed in wearable and implantable configurations, have enormous applications in advanced healthcare, rapid disease diagnostics, and persistent human-machine interfaces. Soft, contoured geometries and time-dynamic deformation of the targeted tissues require high flexibility and stretchability of the integrated bioelectronics. Recent progress in developing and engineering soft materials has provided a unique opportunity to design various types of mechanically compliant and deformable systems. Here, we summarize the required properties of soft materials and their characteristics for configuring sensing and substrate components in wearable and implantable devices and systems. Details of functionality and sensitivity of the recently developed FHE are discussed with the application areas in medicine, healthcare, and machine interactions. This review concludes with a discussion on limitations of current materials, key requirements for next generation materials, and new application areas.

  4. Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces.

    Science.gov (United States)

    Herbert, Robert; Kim, Jong-Hoon; Kim, Yun Soung; Lee, Hye Moon; Yeo, Woon-Hong

    2018-01-24

    Flexible hybrid electronics (FHE), designed in wearable and implantable configurations, have enormous applications in advanced healthcare, rapid disease diagnostics, and persistent human-machine interfaces. Soft, contoured geometries and time-dynamic deformation of the targeted tissues require high flexibility and stretchability of the integrated bioelectronics. Recent progress in developing and engineering soft materials has provided a unique opportunity to design various types of mechanically compliant and deformable systems. Here, we summarize the required properties of soft materials and their characteristics for configuring sensing and substrate components in wearable and implantable devices and systems. Details of functionality and sensitivity of the recently developed FHE are discussed with the application areas in medicine, healthcare, and machine interactions. This review concludes with a discussion on limitations of current materials, key requirements for next generation materials, and new application areas.

  5. An intelligent human-machine system based on an ecological interface design concept

    International Nuclear Information System (INIS)

    Naito, N.

    1995-01-01

    It seems both necessary and promising to develop an intelligent human-machine system, considering the objective of the human-machine system and the recent advance in cognitive engineering and artificial intelligence together with the ever-increasing importance of human factor issues in nuclear power plant operation and maintenance. It should support human operators in their knowledge-based behaviour and allow them to cope with unanticipated abnormal events, including recovery from erroneous human actions. A top-down design approach has been adopted based on cognitive work analysis, and (1) an ecological interface, (2) a cognitive model-based advisor and (3) a robust automatic sequence controller have been established. These functions have been integrated into an experimental control room. A validation test was carried out by the participation of experienced operators and engineers. The results showed the usefulness of this system in supporting the operator's supervisory plant control tasks. ((orig.))

  6. Computerized operator support system with new man-machine interface for BWR power plants

    International Nuclear Information System (INIS)

    Monta, K.; Naito, N.; Sugawara, M.; Sato, N.; Mori, N.; Tai, I.; Fukumoto, A.; Tsuchida, M.

    1984-01-01

    Improvement of the man-machine interface of nuclear power plants is an important contribution to the further enhancement of operational safety. In addition, recent advances in computer technology seem to offer the greatest opportunity to date for achieving improvement in the man-machine interface. The development of a computerized operator support system for BWRs has been undertaken since 1980 with the support of the Japanese Government. The conceptual design of this system is based on the role of the operators. The main functions are standby system management, disturbance analysis and post-trip operational guidance. The objective of the standby system management is to monitor the standby status of the engineered safety feature during normal operation to assure its proper functioning at the onset of emergency situations. The disturbance analysis system detects disturbances in the plant in their early stages and informs the plant operators about, for example, the cause of the disturbances, the plant status and possible propagations. Consequently, operators can take corrective actions to prevent unnecessary plant shutdown. The objective of the post trip operational guide is to support operators in diagnosis and corrective action after a plant trip. Its functions are to monitor the performance of the engineered safety feature, to identify the plant status and to guide the appropriate corrective action to achieve safe plant shutdown. The information from the computerized operator support system is supplied to operators through a colour CRT operator console. The authors have evaluated the performance of various new man-machine interfacing tools and proposed a new operator console design. A prototype system has been developed and verification/validation is proceeding with a BWR plant simulator. (author)

  7. Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience

    Directory of Open Access Journals (Sweden)

    Ole eJensen

    2011-05-01

    Full Text Available Large efforts are currently being made to develop and improve online analysis of brain activity which can be used e.g. for brain-computer interfacing (BCI. A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from EEG/MEG studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work.

  8. Charting the energy landscape of metal/organic interfaces via machine learning

    Science.gov (United States)

    Scherbela, Michael; Hörmann, Lukas; Jeindl, Andreas; Obersteiner, Veronika; Hofmann, Oliver T.

    2018-04-01

    The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. In this work, we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs and defects. This is achieved by training a machine learning model on a list of only 100 candidate structures that are evaluated via dispersion-corrected density functional theory (DFT) calculations. We demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally.

  9. A study on advanced man-machine interface system for autonomous nuclear power plants

    International Nuclear Information System (INIS)

    Matsuoka, Takeshi; Numano, Masayoshi; Fukuto, Junji; Sugasawa, Shinobu; Miyazaki, Keiko; Someya, Minoru; Haraki, Nobuo

    1994-01-01

    A man-machine interface(MMI) system of an autonomous nuclear power plant has an advanced function compared with that of the present nuclear power plants. The MMI has a function model of a plant state, and updates and revises this function model by itself. This paper describes the concept of autonomous nuclear power plants, a plant simulator of an autonomous power plant, a contracted function model of a plant state, three-dimensional color graphic display of a plant state, and an event-tree like expression for plant states. (author)

  10. Human reliability analysis in the man-machine interface design review

    International Nuclear Information System (INIS)

    Kim, I.S.

    2001-01-01

    Advanced, computer-based man-machine interface (MMI) is emerging as part of the new design of nuclear power plants. The impact of advanced MMI on the operator performance, and as a result, on plant safety should be thoroughly evaluated before such technology is actually adopted in the plants. This paper discusses the applicability of human reliability analysis (HRA) to support the design review process. Both the first-generation and the second-generation HRA methods are considered focusing on a couple of promising HRA methods, i.e. ATHEANA and CREAM, with the potential to assist the design review process

  11. Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain.

    Science.gov (United States)

    Li, Guangye; Zhang, Dingguo

    2016-01-01

    An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain.

  12. Controlling a virtual forehand prosthesis using an adaptive and affective Human-Machine Interface.

    Science.gov (United States)

    Rezazadeh, I Mohammad; Firoozabadi, S M P; Golpayegani, S M R Hashemi; Hu, H

    2011-01-01

    This paper presents the design of an adaptable Human-Machine Interface (HMI) for controlling virtual forearm prosthesis. Direct physical performance measures (obtained score and completion time) for the requested tasks were calculated. Furthermore, bioelectric signals from the forehead were recorded using one pair of electrodes placed on the frontal region of the subject head to extract the mental (affective) measures while performing the tasks. By employing the proposed algorithm and above measures, the proposed HMI can adapt itself to the subject's mental states, thus improving the usability of the interface. The quantitative results from 15 subjects show that the proposed HMI achieved better physical performance measures in comparison to a conventional non-adaptive myoelectric controller (p < 0.001).

  13. Man-machine interface systems for the Sizewell B Nuclear Power Station

    International Nuclear Information System (INIS)

    Boettcher, D.B.

    2004-01-01

    Sizewell B is the first nuclear power station to be built in the United Kingdom using the Pressurised Water Reactor or PWR system. The design is based on stations operating in the United States, but many changes and new features have been introduced to bring it up to date, and to meet United Kingdom practice and regulatory requirements. The Man-Machine Interfaces (MMIs) in the control rooms have been newly designed from first principles, with special attention paid to human factors and the role of the operators. The instrumentation and control (1 and C) systems which interface the MMIs to the process plant, and automate the operation of the station, use advanced technology to achieve high performance and availability. This paper describes the development of the control rooms and 1 and C systems, explaining the thinking that lay behind the principal decisions. (author)

  14. Restoring tactile and proprioceptive sensation through a brain interface.

    Science.gov (United States)

    Tabot, Gregg A; Kim, Sung Shin; Winberry, Jeremy E; Bensmaia, Sliman J

    2015-11-01

    Somatosensation plays a critical role in the dexterous manipulation of objects, in emotional communication, and in the embodiment of our limbs. For upper-limb neuroprostheses to be adopted by prospective users, prosthetic limbs will thus need to provide sensory information about the position of the limb in space and about objects grasped in the hand. One approach to restoring touch and proprioception consists of electrically stimulating neurons in somatosensory cortex in the hopes of eliciting meaningful sensations to support the dexterous use of the hands, promote their embodiment, and perhaps even restore the affective dimension of touch. In this review, we discuss the importance of touch and proprioception in everyday life, then describe approaches to providing artificial somatosensory feedback through intracortical microstimulation (ICMS). We explore the importance of biomimicry--the elicitation of naturalistic patterns of neuronal activation--and that of adaptation--the brain's ability to adapt to novel sensory input, and argue that both biomimicry and adaptation will play a critical role in the artificial restoration of somatosensation. We also propose that the documented re-organization that occurs after injury does not pose a significant obstacle to brain interfaces. While still at an early stage of development, sensory restoration is a critical step in transitioning upper-limb neuroprostheses from the laboratory to the clinic. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Ethical aspects of brain computer interfaces: a scoping review.

    Science.gov (United States)

    Burwell, Sasha; Sample, Matthew; Racine, Eric

    2017-11-09

    Brain-Computer Interface (BCI) is a set of technologies that are of increasing interest to researchers. BCI has been proposed as assistive technology for individuals who are non-communicative or paralyzed, such as those with amyotrophic lateral sclerosis or spinal cord injury. The technology has also been suggested for enhancement and entertainment uses, and there are companies currently marketing BCI devices for those purposes (e.g., gaming) as well as health-related purposes (e.g., communication). The unprecedented direct connection created by BCI between human brains and computer hardware raises various ethical, social, and legal challenges that merit further examination and discussion. To identify and characterize the key issues associated with BCI use, we performed a scoping review of biomedical ethics literature, analyzing the ethics concerns cited across multiple disciplines, including philosophy and medicine. Based on this investigation, we report that BCI research and its potential translation to therapeutic intervention generate significant ethical, legal, and social concerns, notably with regards to personhood, stigma, autonomy, privacy, research ethics, safety, responsibility, and justice. Our review of the literature determined, furthermore, that while these issues have been enumerated extensively, few concrete recommendations have been expressed. We conclude that future research should focus on remedying a lack of practical solutions to the ethical challenges of BCI, alongside the collection of empirical data on the perspectives of the public, BCI users, and BCI researchers.

  16. P300 brain computer interface: current challenges and emerging trends

    Science.gov (United States)

    Fazel-Rezai, Reza; Allison, Brendan Z.; Guger, Christoph; Sellers, Eric W.; Kleih, Sonja C.; Kübler, Andrea

    2012-01-01

    A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility. PMID:22822397

  17. User-customized brain computer interfaces using Bayesian optimization

    Science.gov (United States)

    Bashashati, Hossein; Ward, Rabab K.; Bashashati, Ali

    2016-04-01

    Objective. The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject’s brain characteristics. Approach. To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. Main Results. We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Significance. Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.

  18. BCILAB: a platform for brain-computer interface development

    Science.gov (United States)

    Kothe, Christian Andreas; Makeig, Scott

    2013-10-01

    Objective. The past two decades have seen dramatic progress in our ability to model brain signals recorded by electroencephalography, functional near-infrared spectroscopy, etc., and to derive real-time estimates of user cognitive state, response, or intent for a variety of purposes: to restore communication by the severely disabled, to effect brain-actuated control and, more recently, to augment human-computer interaction. Continuing these advances, largely achieved through increases in computational power and methods, requires software tools to streamline the creation, testing, evaluation and deployment of new data analysis methods. Approach. Here we present BCILAB, an open-source MATLAB-based toolbox built to address the need for the development and testing of brain-computer interface (BCI) methods by providing an organized collection of over 100 pre-implemented methods and method variants, an easily extensible framework for the rapid prototyping of new methods, and a highly automated framework for systematic testing and evaluation of new implementations. Main results. To validate and illustrate the use of the framework, we present two sample analyses of publicly available data sets from recent BCI competitions and from a rapid serial visual presentation task. We demonstrate the straightforward use of BCILAB to obtain results compatible with the current BCI literature. Significance. The aim of the BCILAB toolbox is to provide the BCI community a powerful toolkit for methods research and evaluation, thereby helping to accelerate the pace of innovation in the field, while complementing the existing spectrum of tools for real-time BCI experimentation, deployment and use.

  19. User-customized brain computer interfaces using Bayesian optimization.

    Science.gov (United States)

    Bashashati, Hossein; Ward, Rabab K; Bashashati, Ali

    2016-04-01

    The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject's brain characteristics. To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.

  20. On the role of cost-sensitive learning in multi-class brain-computer interfaces.

    Science.gov (United States)

    Devlaminck, Dieter; Waegeman, Willem; Wyns, Bart; Otte, Georges; Santens, Patrick

    2010-06-01

    Brain-computer interfaces (BCIs) present an alternative way of communication for people with severe disabilities. One of the shortcomings in current BCI systems, recently put forward in the fourth BCI competition, is the asynchronous detection of motor imagery versus resting state. We investigated this extension to the three-class case, in which the resting state is considered virtually lying between two motor classes, resulting in a large penalty when one motor task is misclassified into the other motor class. We particularly focus on the behavior of different machine-learning techniques and on the role of multi-class cost-sensitive learning in such a context. To this end, four different kernel methods are empirically compared, namely pairwise multi-class support vector machines (SVMs), two cost-sensitive multi-class SVMs and kernel-based ordinal regression. The experimental results illustrate that ordinal regression performs better than the other three approaches when a cost-sensitive performance measure such as the mean-squared error is considered. By contrast, multi-class cost-sensitive learning enables us to control the number of large errors made between two motor tasks.

  1. Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP.

    Science.gov (United States)

    Deng, Li; Wang, Guohua; Chen, Bo

    2015-01-01

    In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming), using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model's input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators' operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

  2. On the use of peripheral autonomic signals for binary control of body–machine interfaces

    International Nuclear Information System (INIS)

    Falk, Tiago H; Guirgis, Mirna; Power, Sarah; Blain, Stefanie; Chau, Tom

    2010-01-01

    In this work, the potential of using peripheral autonomic (PA) responses as control signals for body–machine interfaces that require no physical movement was investigated. Electrodermal activity, skin temperature, heart rate and respiration rate were collected from six participants and hidden Markov models (HMMs) were used to automatically detect when a subject was performing music imagery as opposed to being at rest. Experiments were performed under controlled silent conditions as well as in the presence of continuous and startle (e.g. door slamming) ambient noise. By developing subject-specific HMMs, music imagery was detected under silent conditions with the average sensitivity and specificity of 94.2% and 93.3%, respectively. In the presence of startle noise stimuli, the system sensitivity and specificity levels of 78.8% and 80.2% were attained, respectively. In environments corrupted by continuous ambient and startle noise, the system specificity further decreased to 75.9%. To improve the system robustness against environmental noise, a startle noise detection and compensation strategy were proposed. Once in place, performance levels were shown to be comparable to those observed in silence. The obtained results suggest that PA signals, combined with HMMs, can be useful tools for the development of body–machine interfaces that allow individuals with severe motor impairments to communicate and/or to interact with their environment

  3. SVM Classifier – a comprehensive java interface for support vector machine classification of microarray data

    Science.gov (United States)

    Pirooznia, Mehdi; Deng, Youping

    2006-01-01

    Motivation Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. Results The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1–BRCA2 samples with RBF kernel of SVM. Conclusion We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at . PMID:17217518

  4. SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

    Science.gov (United States)

    Pirooznia, Mehdi; Deng, Youping

    2006-12-12

    Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

  5. Mechanically Compliant Electronic Materials for Wearable Photovoltaics and Human-Machine Interfaces

    Science.gov (United States)

    O'Connor, Timothy Francis, III

    Applications of stretchable electronic materials for human-machine interfaces are described herein. Intrinsically stretchable organic conjugated polymers and stretchable electronic composites were used to develop stretchable organic photovoltaics (OPVs), mechanically robust wearable OPVs, and human-machine interfaces for gesture recognition, American Sign Language Translation, haptic control of robots, and touch emulation for virtual reality, augmented reality, and the transmission of touch. The stretchable and wearable OPVs comprise active layers of poly-3-alkylthiophene:phenyl-C61-butyric acid methyl ester (P3AT:PCBM) and transparent conductive electrodes of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) and devices could only be fabricated through a deep understanding of the connection between molecular structure and the co-engineering of electronic performance with mechanical resilience. The talk concludes with the use of composite piezoresistive sensors two smart glove prototypes. The first integrates stretchable strain sensors comprising a carbon-elastomer composite, a wearable microcontroller, low energy Bluetooth, and a 6-axis accelerometer/gyroscope to construct a fully functional gesture recognition glove capable of wirelessly translating American Sign Language to text on a cell phone screen. The second creates a system for the haptic control of a 3D printed robot arm, as well as the transmission of touch and temperature information.

  6. Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP

    Directory of Open Access Journals (Sweden)

    Li Deng

    2015-01-01

    Full Text Available In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming, using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model’s input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators’ operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

  7. Roadmap for Research, Development, and Demonstration of Instrumentation, Controls, and Human-Machine Interface Technologies

    International Nuclear Information System (INIS)

    Miller, Don W.; Arndt, Steven A.; Bond, Leonard J.; Dudenhoeffer, Donald D.; Hallbert, Bruce P.; Holcomb, David E.; Wood, Richard T.; Naser, Joseph A.; O'Hara, John M.; Quinn, Edward L.

    2008-01-01

    Instrumentation, controls, and human-machine interfaces are essential enabling technologies that strongly influence nuclear power plant performance and operational costs. The nuclear power industry is currently engaged in a transition from traditional analog-based instrumentation, controls, and human-machine interface (ICHMI) systems to implementations employing digital technologies. This transition has primarily occurred in an ad hoc fashion through individual system upgrades at existing plants and has been constrained by a number of concerns. Although international implementation of evolutionary nuclear power plants and the progression toward new plants in the United States have spurred design of more fully digital plant-wide ICHMI systems, the experience base in the nuclear power application domain is limited. Additionally, design and development programs by the U.S. Department of Energy (DOE) for advanced reactor concepts, such as the Generation IV Program and Next Generation Nuclear Plant (NGNP), introduce different plant conditions and unique plant configurations that increase the need for enhanced ICHMI capabilities to fully achieve programmatic goals related to economic competitiveness, safety and reliability, sustainability, and proliferation resistance and physical protection. As a result, there are challenges that need to be addressed to enable the nuclear power industry to effectively and efficiently complete the transition to safe and comprehensive use of digital technology

  8. Cognitive task analysis of nuclear power plant operators for man-machine interface design

    International Nuclear Information System (INIS)

    Itoh, J.I.; Yoshimura, S.; Ohtsuka, T.

    1990-01-01

    This paper aims to ascertain and further develop design guidelines for a man-machine interface compatible with plant operators' problem solving strategies. As the framework for this study, operator's information processing activities were modeled, based on J. Rasmussen's framework for cognitive task analysis. Two experiments were carried out. One was an experiment aimed at gaining an understanding of internal mechanisms involved in mistakes and slips which occurred in operators' responses to incidents and accidents. As a result of fifteen cases of operator performance analysis, sixty one human errors were identified. Further analysis of the errors showed that frequently occurring error mechanisms were absent-mindedness, lack of recognition of patterns in diagnosis and failed procedure formulation due to memory lapses. The other kind of experiment was carried out to identify the envelope of trajectories for the operator's search in the problem space consisting of the two dimensions of means-ends and whole-part relations while dealing with transients. Two cases of experimental sessions were conducted with the thinking-aloud method. From analyses based on verbal protocols, trajectories of operator's search were derived, covering from the whole plant level through the component level in the whole-part dimension and covering from the functional purpose level through the physical form level in the means-ends dimension. The findings obtained from these analyses serve as a basis for developing design guidelines for man-machine interfaces in control rooms of nuclear power plants

  9. Cognitive Human-Machine Interface Applied in Remote Support for Industrial Robot Systems

    Directory of Open Access Journals (Sweden)

    Tomasz Kosicki

    2013-10-01

    Full Text Available An attempt is currently being made to widely introduce industrial robots to Small-Medium Enterprises (SMEs. Since the enterprises usually employ too small number of robot units to afford specialized departments for robot maintenance, they must be provided with inexpensive and immediate support remotely. This paper evaluates whether the support can be provided by means of Cognitive Info-communication – communication in which human cognitive capabilities are extended irrespectively of geographical distances. The evaluations are given with an aid of experimental system that consists of local and remote rooms, which are physically separated – a six-degree-of-freedom NACHI SH133-03 industrial robot is situated in the local room, while the operator, who supervises the robot by means of audio-visual Cognitive Human-Machine Interface, is situated in the remote room. The results of simple experiments show that Cognitive Info-communication is not only efficient mean to provide the support remotely, but is probably also a powerful tool to enhance interaction with any data-rich environment that require good conceptual understanding of system's state and careful attention management. Furthermore, the paper discusses data presentation and reduction methods for data-rich environments, as well as introduces the concepts of Naturally Acquired Data and Cognitive Human-Machine Interfaces.

  10. A review on progress of man-machine interface system designs for Japanese PWRs

    International Nuclear Information System (INIS)

    Yoshikawa, Hidekazu; Yamamoto, Yoshihiro; Magari, Takayuki.

    1994-01-01

    Historical development of Instrumentation and Control (I and C) system designing for the PWR plants in Kansai Electric Co. Ltd is firstly reviewed with respect to the conventional PWRs in the past, brand-new PWRs (Ohi 3/4 units) and advanced PWRs (APWR) in Japan. The major features of the APWR I and C system design are extensive application of digital computer control technology and advanced man-machine interface in order to enhance safety and reliability of total I and C system and to improve human factors in nuclear power plant operation. Comparative study of the APWR's I and C system design with the EPRI's User Requirement Definitions (URD) resulted in that the current Japanese APWR I and C system design meets generally with the EPRI URD conditions except for those items mainly set by the present national regulatory guidelines. The remaining problems in the current I and C system design are discussed which include the issues on future direction of man-machine interface development. (author)

  11. Gesture-controlled interfaces for self-service machines and other applications

    Science.gov (United States)

    Cohen, Charles J. (Inventor); Beach, Glenn (Inventor); Cavell, Brook (Inventor); Foulk, Gene (Inventor); Jacobus, Charles J. (Inventor); Obermark, Jay (Inventor); Paul, George (Inventor)

    2004-01-01

    A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.

  12. Brain Computer Interface-Controlling Devices Utilizing The Alpha Brain Waves

    Directory of Open Access Journals (Sweden)

    Rohan Hundia

    2015-01-01

    Full Text Available Abstract This paper describes the development and testing of an interface system whereby one can control external devices by voluntarily controlling alpha waves that is through eye movement. Such a system may be used for the control of prosthetics robotic arms and external devices like wheelchairs using the alpha brain waves and the Mu rhythm. The response generated through the movement of the eye detecting and controlling the amplitude of the alpha brain waves is interfaced and processed to control Robotic systems and smart home control. In order to measure the response of alpha waves over different lobes of the brain initially I measured these signals over 32 regions using silver chloride plated electrodes. By the opening and the closure of the eyes and the movement in the up-down left-right directions and processing these movements measuring them over the occipital region I was able to differentiate the amplitude of the alpha waves generated due to these several movements. In the First session testing period subjects were asked to close and open their eyes and they were able to control limited movements of a Robot and a prosthetic arm. In the Second 2session the movement of the eyes was also considered left-right up-down along with the opening and closure during this time span they were able to control more dimensions of the robot several devices at the same time using different eye movements.

  13. sBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Tatsiana Malechka

    2015-02-01

    Full Text Available Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI. In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG measurements and the low information transfer rate of EEG based BCI. These disadvantages become apparent if a BCI is used to control complex devices. In this paper, a hybrid BCI is described that enables research for a Human Machine Interface (HMI that is optimally adapted to requirements of the user and the tasks to be carried out. The solution is based on the integration of a Steady-state visual evoked potential (SSVEP-BCI, an Event-related (de-synchronization (ERD/ERS-BCI, an eye tracker, an environmental observation camera, and a new EEG head cap for wearing comfort and easy preparation. The design of the new fast multimodal BCI (called sBCI system is described and first test results, obtained in experiments with six healthy subjects, are presented. The sBCI concept may also become useful for healthy people in cases where a “hands-free” handling of devices is necessary.

  14. Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA

    Directory of Open Access Journals (Sweden)

    Li Deng

    2016-01-01

    Full Text Available In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method.

  15. Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA.

    Science.gov (United States)

    Deng, Li; Wang, Guohua; Yu, Suihuai

    2016-01-01

    In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method.

  16. Characterizing water-metal interfaces and machine learning potential energy surfaces

    Science.gov (United States)

    Ryczko, Kevin

    In this thesis, we first discuss the fundamentals of ab initio electronic structure theory and density functional theory (DFT). We also discuss statistics related to computing thermodynamic averages of molecular dynamics (MD). We then use this theory to analyze and compare the structural, dynamical, and electronic properties of liquid water next to prototypical metals including platinum, graphite, and graphene. Our results are built on Born-Oppenheimer molecular dynamics (BOMD) generated using density functional theory (DFT) which explicitly include van der Waals (vdW) interactions within a first principles approach. All calculations reported use large simulation cells, allowing for an accurate treatment of the water-electrode interfaces. We have included vdW interactions through the use of the optB86b-vdW exchange correlation functional. Comparisons with the Perdew-Burke-Ernzerhof (PBE) exchange correlation functional are also shown. We find an initial peak, due to chemisorption, in the density profile of the liquid water-Pt interface not seen in the liquid water-graphite interface, liquid watergraphene interface, nor interfaces studied previously. To further investigate this chemisorption peak, we also report differences in the electronic structure of single water molecules on both Pt and graphite surfaces. We find that a covalent bond forms between the single water molecule and the platinum surface, but not between the single water molecule and the graphite surface. We also discuss the effects that defects and dopants in the graphite and graphene surfaces have on the structure and dynamics of liquid water. Lastly, we introduce artificial neural networks (ANNs), and demonstrate how they can be used to machine learn electronic structure calculations. As a proof of principle, we show the success of an ANN potential energy surfaces for a dimer molecule with a Lennard-Jones potential.

  17. A collaborative brain-computer interface for improving human performance.

    Directory of Open Access Journals (Sweden)

    Yijun Wang

    Full Text Available Electroencephalogram (EEG based brain-computer interfaces (BCI have been studied since the 1970s. Currently, the main focus of BCI research lies on the clinical use, which aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. However, the BCI technology can also be used to improve human performance for normal healthy users. Although this application has been proposed for a long time, little progress has been made in real-world practices due to technical limits of EEG. To overcome the bottleneck of low single-user BCI performance, this study proposes a collaborative paradigm to improve overall BCI performance by integrating information from multiple users. To test the feasibility of a collaborative BCI, this study quantitatively compares the classification accuracies of collaborative and single-user BCI applied to the EEG data collected from 20 subjects in a movement-planning experiment. This study also explores three different methods for fusing and analyzing EEG data from multiple subjects: (1 Event-related potentials (ERP averaging, (2 Feature concatenating, and (3 Voting. In a demonstration system using the Voting method, the classification accuracy of predicting movement directions (reaching left vs. reaching right was enhanced substantially from 66% to 80%, 88%, 93%, and 95% as the numbers of subjects increased from 1 to 5, 10, 15, and 20, respectively. Furthermore, the decision of reaching direction could be made around 100-250 ms earlier than the subject's actual motor response by decoding the ERP activities arising mainly from the posterior parietal cortex (PPC, which are related to the processing of visuomotor transmission. Taken together, these results suggest that a collaborative BCI can effectively fuse brain activities of a group of people to improve the overall performance of natural human behavior.

  18. Spectral Transfer Learning using Information Geometry for a User-Independent Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Nicholas Roy Waytowich

    2016-09-01

    Full Text Available Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI technologies to fields such as medicine, industry and recreation. However, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter- individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG, which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both offline and real-time feedback analysis during a rapid serial visual presentation task (RSVP. For detection of single-trial, event-related potentials (ERPs, the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

  19. Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface.

    Science.gov (United States)

    Waytowich, Nicholas R; Lawhern, Vernon J; Bohannon, Addison W; Ball, Kenneth R; Lance, Brent J

    2016-01-01

    Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

  20. Ensemble of Neural Network Conditional Random Fields for Self-Paced Brain Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Hossein Bashashati

    2017-07-01

    Full Text Available Classification of EEG signals in self-paced Brain Computer Interfaces (BCI is an extremely challenging task. The main difficulty stems from the fact that start time of a control task is not defined. Therefore it is imperative to exploit the characteristics of the EEG data to the extent possible. In sensory motor self-paced BCIs, while performing the mental task, the user’s brain goes through several well-defined internal state changes. Applying appropriate classifiers that can capture these state changes and exploit the temporal correlation in EEG data can enhance the performance of the BCI. In this paper, we propose an ensemble learning approach for self-paced BCIs. We use Bayesian optimization to train several different classifiers on different parts of the BCI hyper- parameter space. We call each of these classifiers Neural Network Conditional Random Field (NNCRF. NNCRF is a combination of a neural network and conditional random field (CRF. As in the standard CRF, NNCRF is able to model the correlation between adjacent EEG samples. However, NNCRF can also model the nonlinear dependencies between the input and the output, which makes it more powerful than the standard CRF. We compare the performance of our algorithm to those of three popular sequence labeling algorithms (Hidden Markov Models, Hidden Markov Support Vector Machines and CRF, and to two classical classifiers (Logistic Regression and Support Vector Machines. The classifiers are compared for the two cases: when the ensemble learning approach is not used and when it is. The data used in our studies are those from the BCI competition IV and the SM2 dataset. We show that our algorithm is considerably superior to the other approaches in terms of the Area Under the Curve (AUC of the BCI system.

  1. Performance monitoring for brain-computer-interface actions.

    Science.gov (United States)

    Schurger, Aaron; Gale, Steven; Gozel, Olivia; Blanke, Olaf

    2017-02-01

    When presented with a difficult perceptual decision, human observers are able to make metacognitive judgements of subjective certainty. Such judgements can be made independently of and prior to any overt response to a sensory stimulus, presumably via internal monitoring. Retrospective judgements about one's own task performance, on the other hand, require first that the subject perform a task and thus could potentially be made based on motor processes, proprioceptive, and other sensory feedback rather than internal monitoring. With this dichotomy in mind, we set out to study performance monitoring using a brain-computer interface (BCI), with which subjects could voluntarily perform an action - moving a cursor on a computer screen - without any movement of the body, and thus without somatosensory feedback. Real-time visual feedback was available to subjects during training, but not during the experiment where the true final position of the cursor was only revealed after the subject had estimated where s/he thought it had ended up after 6s of BCI-based cursor control. During the first half of the experiment subjects based their assessments primarily on the prior probability of the end position of the cursor on previous trials. However, during the second half of the experiment subjects' judgements moved significantly closer to the true end position of the cursor, and away from the prior. This suggests that subjects can monitor task performance when the task is performed without overt movement of the body. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Brain-computer interfacing under distraction: an evaluation study

    Science.gov (United States)

    Brandl, Stephanie; Frølich, Laura; Höhne, Johannes; Müller, Klaus-Robert; Samek, Wojciech

    2016-10-01

    Objective. While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach. This paper systematically investigates BCI performance under 6 types of distractions that mimic out-of-lab environments. Main results. We report results of 16 participants and show that the performance of the standard common spatial patterns (CSP) + regularized linear discriminant analysis classification pipeline drops significantly in this ‘simulated’ out-of-lab setting. We then investigate three methods for improving the performance: (1) artifact removal, (2) ensemble classification, and (3) a 2-step classification approach. While artifact removal does not enhance the BCI performance significantly, both ensemble classification and the 2-step classification combined with CSP significantly improve the performance compared to the standard procedure. Significance. Systematically analyzing out-of-lab scenarios is crucial when bringing BCI into everyday life. Algorithms must be adapted to overcome nonstationary environments in order to tackle real-world challenges.

  3. An auditory oddball brain-computer interface for binary choices.

    Science.gov (United States)

    Halder, S; Rea, M; Andreoni, R; Nijboer, F; Hammer, E M; Kleih, S C; Birbaumer, N; Kübler, A

    2010-04-01

    Brain-computer interfaces (BCIs) provide non-muscular communication for individuals diagnosed with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)). In the final stages of the disease, a BCI cannot rely on the visual modality. This study examined a method to achieve high accuracies using auditory stimuli only. We propose an auditory BCI based on a three-stimulus paradigm. This paradigm is similar to the standard oddball but includes an additional target (i.e. two target stimuli, one frequent stimulus). Three versions of the task were evaluated in which the target stimuli differed in loudness, pitch or direction. Twenty healthy participants achieved an average information transfer rate (ITR) of up to 2.46 bits/min and accuracies of 78.5%. Most subjects (14 of 20) achieved their best performance with targets differing in pitch. With this study, the viability of the paradigm was shown for healthy participants and will next be evaluated with individuals diagnosed with ALS or locked-in syndrome (LIS) after stroke. The here presented BCI offers communication with binary choices (yes/no) independent of vision. As it requires only little time per selection, it may constitute a reliable means of communication for patients who lost all motor function and have a short attention span. 2009 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  4. Evaluation of a Compact Hybrid Brain-Computer Interface System

    Directory of Open Access Journals (Sweden)

    Jaeyoung Shin

    2017-01-01

    Full Text Available We realized a compact hybrid brain-computer interface (BCI system by integrating a portable near-infrared spectroscopy (NIRS device with an economical electroencephalography (EEG system. The NIRS array was located on the subjects’ forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA and baseline (BL tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation.

  5. Vibrotactile Feedback for Brain-Computer Interface Operation

    Directory of Open Access Journals (Sweden)

    Febo Cincotti

    2007-01-01

    Full Text Available To be correctly mastered, brain-computer interfaces (BCIs need an uninterrupted flow of feedback to the user. This feedback is usually delivered through the visual channel. Our aim was to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and software arrangements, was specified. In three studies with 33 subjects (including 3 with spinal cord injury, we compared vibrotactile and visual feedback, addressing: (I the feasibility of subjects' training to master their EEG rhythms using tactile feedback; (II the compatibility of this form of feedback in presence of a visual distracter; (III the performance in presence of a complex visual task on the same (visual or different (tactile sensory channel. The stimulation protocol we developed supports a general usage of the tactors; preliminary experimentations. All studies indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task. In all experiments, vibrotactile feedback felt, after some training, more natural for both controls and SCI users.

  6. Quantitative analysis of task selection for brain-computer interfaces

    Science.gov (United States)

    Llera, Alberto; Gómez, Vicenç; Kappen, Hilbert J.

    2014-10-01

    Objective. To assess quantitatively the impact of task selection in the performance of brain-computer interfaces (BCI). Approach. We consider the task-pairs derived from multi-class BCI imagery movement tasks in three different datasets. We analyze for the first time the benefits of task selection on a large-scale basis (109 users) and evaluate the possibility of transferring task-pair information across days for a given subject. Main results. Selecting the subject-dependent optimal task-pair among three different imagery movement tasks results in approximately 20% potential increase in the number of users that can be expected to control a binary BCI. The improvement is observed with respect to the best task-pair fixed across subjects. The best task-pair selected for each subject individually during a first day of recordings is generally a good task-pair in subsequent days. In general, task learning from the user side has a positive influence in the generalization of the optimal task-pair, but special attention should be given to inexperienced subjects. Significance. These results add significant evidence to existing literature that advocates task selection as a necessary step towards usable BCIs. This contribution motivates further research focused on deriving adaptive methods for task selection on larger sets of mental tasks in practical online scenarios.

  7. Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

    Science.gov (United States)

    Wang, Deng; Miao, Duoqian; Blohm, Gunnar

    2012-01-01

    Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

  8. Wireless sEMG-Based Body-Machine Interface for Assistive Technology Devices.

    Science.gov (United States)

    Fall, Cheikh Latyr; Gagnon-Turcotte, Gabriel; Dube, Jean-Francois; Gagne, Jean Simon; Delisle, Yanick; Campeau-Lecours, Alexandre; Gosselin, Clement; Gosselin, Benoit

    2017-07-01

    Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO.

  9. Remote maintenance systems requirements are being developed to provide design guidelines for machine components, to define maintenance interfaces, and to quantify maintenance equipment and procedures needed

    International Nuclear Information System (INIS)

    Spampinato, P.T.; Tabor, M.A.

    1988-01-01

    Remote maintenance systems requirements are being developed to provide design guidelines for machine components, to define maintenance interfaces, and to quantify maintenance equipment and procedures needed

  10. Human reliability and the man/machine interface: what do we do after the control room review

    International Nuclear Information System (INIS)

    Folley, J.D. Jr.; Schurman, D.L.

    1983-01-01

    The nuclear industry has focused on only one aspect of the man/machine interface--human factors engineering of control rooms - to enhance nuclear power plant (NPP) safety. There are other factors that profoundly affect the reliability of the operators and maintenance personnel of NPP which, thus, affect NPP safety and availability. This paper discusses the factors of training, selection, job engineering, and work satisfaction, describing the potential effects on reactor safety of these factors at the man/machine interface. The impact of these factors on safety and plant availability is illustrated with examples of results obtained in other fields

  11. Man-machine interface in a submarine command and weapon control system: features and design experience

    Directory of Open Access Journals (Sweden)

    Johan H. Aas

    1989-01-01

    Full Text Available Important man-machine interface (MMI issues concerning a submarine command and weapon control system (CWCS such as crew organization, automation level and decision support are discussed in this paper. Generic submarine CWCS functions and operating conditions are outlined. Detailed, dynamic and real-time prototypes were used to support the MMI design. The prototypes are described and experience with detailed prototyping is discussed. Some of the main interaction principles are summarized and a restricted example of the resulting design is given. Our design experience and current work have been used to outline future perspectives of MMI design in naval CWCSs. The need for both formal and experimental approaches is emphasized.

  12. Human factor engineering analysis for computerized human machine interface design issues

    International Nuclear Information System (INIS)

    Wang Zhifang; Gu Pengfei; Zhang Jianbo

    2010-01-01

    The application of digital I and C technology in nuclear power plants is a significant improvement in terms of functional performances and flexibility, and it also poses a challenge to operation safety. Most of the new NPPs under construction are adopting advanced control room design which utilizes the computerized human machine interface (HMI) as the main operating means. Thus, it greatly changes the way the operators interact with the plant. This paper introduces the main challenges brought out by computerized technology on the human factor engineering aspect and addresses the main issues to be dealt with in the computerized HMI design process. Based on a operator task-resources-cognitive model, it states that the root cause of human errors is the mismatch between resources demand and their supply. And a task-oriented HMI design principle is discussed. (authors)

  13. Asset Analysis Method for the Cyber Security of Man Machine Interface System

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Sung Kon; Kim, Hun Hee; Shin, Yeong Cheol [Korea Hydro and Nuclear Power, Daejeon (Korea, Republic of)

    2010-10-15

    As digital MMIS (Man Machine Interface System) is applied in Nuclear Power Plant (NPP), cyber security is becoming more and more important. Regulatory guide (KINS/GT-N27) requires that implementation plan for cyber security be prepared in NPP. Regulatory guide recommends the following 4 processes: 1) an asset analysis of MMIS, 2) a vulnerability analysis of MMIS, 3) establishment of countermeasures, and 4) establishment of operational guideline for cyber security. Conventional method for the asset analysis is mainly performed with a table form for each asset. Conventional method requires a lot of efforts due to the duplication of information. This paper presents an asset analysis method using object oriented approach for the NPP

  14. Autotuning of PID controller by means of human machine interface device

    Directory of Open Access Journals (Sweden)

    Michał Awtoniuk

    2017-06-01

    Full Text Available More and more control systems are based on industry microprocessors like PLC controllers (Programmable Logic Controller. The most commonly used control algorithm is PID (Proportional-Integral-Derivative algorithm. Autotuning procedure is not available in every PLC. These controllers are typically used in cooperation with HMI (Human Machine Interface devices. In the study two procedures of autotuning of the PID controller were implemented in the HMI device: step method and relay method. Six tuning rules for step methods and one for relay method were chosen. The autotuning procedures on simulated controlled object and PLC controller without build-in autotuning were tested. The object of control was first order system plus time delay.

  15. Computer-based diagnostic monitoring to enhance the human-machine interface of complex processes

    International Nuclear Information System (INIS)

    Kim, I.S.

    1992-02-01

    There is a growing interest in introducing an automated, on-line, diagnostic monitoring function into the human-machine interfaces (HMIs) or control rooms of complex process plants. The design of such a system should be properly integrated with other HMI systems in the control room, such as the alarms system or the Safety Parameter Display System (SPDS). This paper provides a conceptual foundation for the development of a Plant-wide Diagnostic Monitoring System (PDMS), along with functional requirements for the system and other advanced HMI systems. Insights are presented into the design of an efficient and robust PDMS, which were gained from a critical review of various methodologies developed in the nuclear power industry, the chemical process industry, and the space technological community

  16. Upper limb functional electrical stimulation devices and their man-machine interfaces.

    Science.gov (United States)

    Venugopalan, L; Taylor, P N; Cobb, J E; Swain, I D

    2015-01-01

    Functional Electrical Stimulation (FES) is a technique that uses electricity to activate the nerves of a muscle that is paralysed due to hemiplegia, multiple sclerosis, Parkinson's disease or spinal cord injury (SCI). FES has been widely used to restore upper limb functions in people with hemiplegia and C5-C7 tetraplegia and has improved their ability to perform their activities of daily living (ADL). At the time of writing, a detailed literature review of the existing upper limb FES devices and their man-machine interfaces (MMI) showed that only the NESS H200 was commercially available. However, the rigid arm splint doesn't fit everyone and prevents the use of a tenodesis grip. Hence, a robust and versatile upper limb FES device that can be used by a wider group of people is required.

  17. Asset Analysis Method for the Cyber Security of Man Machine Interface System

    International Nuclear Information System (INIS)

    Kang, Sung Kon; Kim, Hun Hee; Shin, Yeong Cheol

    2010-01-01

    As digital MMIS (Man Machine Interface System) is applied in Nuclear Power Plant (NPP), cyber security is becoming more and more important. Regulatory guide (KINS/GT-N27) requires that implementation plan for cyber security be prepared in NPP. Regulatory guide recommends the following 4 processes: 1) an asset analysis of MMIS, 2) a vulnerability analysis of MMIS, 3) establishment of countermeasures, and 4) establishment of operational guideline for cyber security. Conventional method for the asset analysis is mainly performed with a table form for each asset. Conventional method requires a lot of efforts due to the duplication of information. This paper presents an asset analysis method using object oriented approach for the NPP

  18. Brain barriers and functional interfaces with sequential appearance of ABC efflux transporters during human development

    DEFF Research Database (Denmark)

    Møllgård, Kjeld; Dziegielewska, Katarzyna M.; Holst, Camilla B.

    2017-01-01

    Adult brain is protected from entry of drugs and toxins by specific mechanisms such as ABC (ATP-binding Cassette) efflux transporters. Little is known when these appear in human brain during development. Cellular distribution of three main ABC transporters (ABCC1, ABCG2, ABCB1) was determined...... at blood-brain barriers and interfaces in human embryos and fetuses in first half of gestation. Antibodies against claudin-5 and-11 and antibodies to α-fetoprotein were used to describe morphological and functional aspects of brain barriers. First exchange interfaces to be established, probably at 4...... three transporters. Results provide evidence for sequential establishment of brain exchange interfaces and spatial and temporal timetable for three main ABC transporters in early human brain....

  19. Steering a Tractor by Means of an EMG-Based Human-Machine Interface

    Directory of Open Access Journals (Sweden)

    Sergio Alonso-Garcia

    2011-07-01

    Full Text Available An electromiographic (EMG-based human-machine interface (HMI is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An EPOC, a low-cost human-computer interface (HCI from the Emotiv Company, was employed. This device, by means of 14 saline sensors, measures and processes EMG and electroencephalographic (EEG signals from the scalp of the driver. In our tests, the HMI took into account only the detection of four trained muscular events on the driver’s scalp: eyes looking to the right and jaw opened, eyes looking to the right and jaw closed, eyes looking to the left and jaw opened, and eyes looking to the left and jaw closed. The EMG-based HMI guidance was compared with manual guidance and with autonomous GPS guidance. A driver tested these three guidance systems along three different trajectories: a straight line, a step, and a circumference. The accuracy of the EMG-based HMI guidance was lower than the accuracy obtained by manual guidance, which was lower in turn than the accuracy obtained by the autonomous GPS guidance; the computed standard deviations of error to the desired trajectory in the straight line were 16 cm, 9 cm, and 4 cm, respectively. Since the standard deviation between the manual guidance and the EMG-based HMI guidance differed only 7 cm, and this difference is not relevant in agricultural steering, it can be concluded that it is possible to steer a tractor by an EMG-based HMI with almost the same accuracy as with manual steering.

  20. Brain-computer interface analysis of a dynamic visuo-motor task.

    Science.gov (United States)

    Logar, Vito; Belič, Aleš

    2011-01-01

    The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could

  1. Dust Emission Induced By Friction Modifications At Tool Chip Interface In Dry Machining In MMCp

    International Nuclear Information System (INIS)

    Kremer, Arnaud; El Mansori, Mohamed

    2011-01-01

    This paper investigates the relationship between dust emission and tribological conditions at the tool-chip interface when machining Metal Matrix composite reinforced with particles (MMCp) in dry mode. Machining generates aerosols that can easily be inhaled by workers. Aerosols may be composed of oil mist, tool material or alloying elements of workpiece material. Bar turning tests were conducted on a 2009 aluminum alloy reinforced with different level of Silicon Carbide particles (15, 25 and 35% of SiCp). Variety of PCD tools and nanostructured diamond coatings were used to analyze their performances on air pollution. A spectrometer was used to detect airborne aerosol particles in the size range between 0.3μm to 20 μm and to sort them in 15 size channels in real time. It was used to compare the effects of test parameters on dust emission. Observations of tool face and chip morphology reveal the importance of friction phenomena. It was demonstrated that level of friction modifies chip curvature and dust emission. The increase of level of reinforcement increase the chip segmentation and decrease the contact length and friction area. A ''running in'' phenomenon with important dust emission appeared with PCD tool due to the tool rake face flatness. In addition dust generation is more sensitive to edge integrity than power consumption.

  2. Dust Emission Induced By Friction Modifications At Tool Chip Interface In Dry Machining In MMCp

    Science.gov (United States)

    Kremer, Arnaud; El Mansori, Mohamed

    2011-01-01

    This paper investigates the relationship between dust emission and tribological conditions at the tool-chip interface when machining Metal Matrix composite reinforced with particles (MMCp) in dry mode. Machining generates aerosols that can easily be inhaled by workers. Aerosols may be composed of oil mist, tool material or alloying elements of workpiece material. Bar turning tests were conducted on a 2009 aluminum alloy reinforced with different level of Silicon Carbide particles (15, 25 and 35% of SiCp). Variety of PCD tools and nanostructured diamond coatings were used to analyze their performances on air pollution. A spectrometer was used to detect airborne aerosol particles in the size range between 0.3μm to 20 μm and to sort them in 15 size channels in real time. It was used to compare the effects of test parameters on dust emission. Observations of tool face and chip morphology reveal the importance of friction phenomena. It was demonstrated that level of friction modifies chip curvature and dust emission. The increase of level of reinforcement increase the chip segmentation and decrease the contact length and friction area. A "running in" phenomenon with important dust emission appeared with PCD tool due to the tool rake face flatness. In addition dust generation is more sensitive to edge integrity than power consumption.

  3. New generation of human machine interfaces for controlling UAV through depth-based gesture recognition

    Science.gov (United States)

    Mantecón, Tomás.; del Blanco, Carlos Roberto; Jaureguizar, Fernando; García, Narciso

    2014-06-01

    New forms of natural interactions between human operators and UAVs (Unmanned Aerial Vehicle) are demanded by the military industry to achieve a better balance of the UAV control and the burden of the human operator. In this work, a human machine interface (HMI) based on a novel gesture recognition system using depth imagery is proposed for the control of UAVs. Hand gesture recognition based on depth imagery is a promising approach for HMIs because it is more intuitive, natural, and non-intrusive than other alternatives using complex controllers. The proposed system is based on a Support Vector Machine (SVM) classifier that uses spatio-temporal depth descriptors as input features. The designed descriptor is based on a variation of the Local Binary Pattern (LBP) technique to efficiently work with depth video sequences. Other major consideration is the especial hand sign language used for the UAV control. A tradeoff between the use of natural hand signs and the minimization of the inter-sign interference has been established. Promising results have been achieved in a depth based database of hand gestures especially developed for the validation of the proposed system.

  4. A Perspective on Remote Handling Operations and Human Machine Interface for Remote Handling in Fusion

    International Nuclear Information System (INIS)

    Haist, B.; Hamilton, D.; Sanders, St.

    2006-01-01

    A large-scale fusion device presents many challenges to the remote handling operations team. This paper is based on unique operational experience at JET and gives a perspective on remote handling task development, logistics and resource management, as well as command, control and human-machine interface systems. Remote operations require an accurate perception of a dynamic environment, ideally providing the operators with the same unrestricted knowledge of the task scene as would be available if they were actually at the remote work location. Traditional camera based systems suffer from a limited number of viewpoints and also degrade quickly when exposed to high radiation. Virtual Reality and Augmented Reality software offer great assistance. The remote handling system required to maintain a tokamak requires a large number of different and complex pieces of equipment coordinating to perform a large array of tasks. The demands on the operator's skill in performing the tasks can escalate to a point where the efficiency and safety of operations are compromised. An operations guidance system designed to facilitate the planning, development, validation and execution of remote handling procedures is essential. Automatic planning of motion trajectories of remote handling equipment and the remote transfer of heavy loads will be routine and need to be reliable. This paper discusses the solutions developed at JET in these areas and also the trends in management and presentation of operational data as well as command, control and HMI technology development offering the potential to greatly assist remote handling in future fusion machines. (author)

  5. Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem.

    Directory of Open Access Journals (Sweden)

    Cai Wingfield

    2017-09-01

    Full Text Available There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro- and magneto-encephalography (EMEG, generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.

  6. EEG datasets for motor imagery brain-computer interface.

    Science.gov (United States)

    Cho, Hohyun; Ahn, Minkyu; Ahn, Sangtae; Kwon, Moonyoung; Jun, Sung Chan

    2017-07-01

    Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states. © The Authors 2017. Published by Oxford University Press.

  7. Brain-computer interface based on intermodulation frequency

    Science.gov (United States)

    Chen, Xiaogang; Chen, Zhikai; Gao, Shangkai; Gao, Xiaorong

    2013-12-01

    Objective. Most recent steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems have used a single frequency for each target, so that a large number of targets require a large number of stimulus frequencies and therefore a wider frequency band. However, human beings show good SSVEP responses only in a limited range of frequencies. Furthermore, this issue is especially problematic if the SSVEP-based BCI takes a PC monitor as a stimulator, which is only capable of generating a limited range of frequencies. To mitigate this issue, this study presents an innovative coding method for SSVEP-based BCI by means of intermodulation frequencies. Approach. Simultaneous modulations of stimulus luminance and color at different frequencies were utilized to induce intermodulation frequencies. Luminance flickered at relatively large frequency (10, 12, 15 Hz), while color alternated at low frequency (0.5, 1 Hz). An attractive feature of the proposed method was that it would substantially increase the number of targets at a single flickering frequency by altering color modulated frequencies. Based on this method, the BCI system presented in this study realized eight targets merely using three flickering frequencies. Main results. The online results obtained from 15 subjects (14 healthy and 1 with stroke) revealed that an average classification accuracy of 93.83% and information transfer rate (ITR) of 33.80 bit min-1 were achieved using our proposed SSVEP-based BCI system. Specifically, 5 out of the 15 subjects exhibited an ITR of 40.00 bit min-1 with a classification accuracy of 100%. Significance. These results suggested that intermodulation frequencies could be adopted as steady responses in BCI, for which our system could be used as a practical BCI system.

  8. Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

    Science.gov (United States)

    Boissoneault, Jeff; Sevel, Landrew; Letzen, Janelle; Robinson, Michael; Staud, Roland

    2017-01-01

    Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

  9. Human-machine interface (HMI) report for 241-SY-101 data acquisition system (DACS) upgrade study

    International Nuclear Information System (INIS)

    Truitt, R.W.

    1997-01-01

    This report provides an independent evaluation of information for a Windows based Human Machine Interface (HMI) to replace the existing DOS based Iconics HMI currently used in the Data Acquisition and Control System (DACS) used at Tank 241-SY-101. A fundamental reason for this evaluation is because of the difficulty of maintaining the system with obsolete, unsupported software. The DACS uses a software operator interface (Genesis for DOS HMI) that is no longer supported by its manufacturer, Iconics. In addition to its obsolescence, it is complex and difficult to train additional personnel on. The FY 1997 budget allocated $40K for phase 1 of a software/hardware upgrade that would have allowed the old DOS based system to be replaced by a current Windows based system. Unfortunately, budget constraints during FY 1997 has prompted deferral of the upgrade. The upgrade needs to be performed at the earliest possible time, before other failures render the system useless. Once completed, the upgrade could alleviate other concerns: spare pump software may be able to be incorporated into the same software as the existing pump, thereby eliminating the parallel path dilemma; and the newer, less complex software should expedite training of future personnel, and in the process, require that less technical time be required to maintain the system

  10. COSY Control Status. First results with rapid prototyped man-machine interface for accelerator control

    Energy Technology Data Exchange (ETDEWEB)

    Hacker, U [Forschungszentrum Juelich, Postfach 1913, 52425 Juelich (Germany); Haberbosch, C [Forschungszentrum Juelich, Postfach 1913, 52425 Juelich (Germany); Henn, K [Forschungszentrum Juelich, Postfach 1913, 52425 Juelich (Germany); Weinert, A [Forschungszentrum Juelich, Postfach 1913, 52425 Juelich (Germany)

    1994-12-15

    The experience gained with the COSY Control System after a six month commissioning period followed by a six month production period will be presented. The COSY Control System runs approximately 300 VME and VXI target systems using a total of about 1000 CPUs, the systems are driven by the diskless operating environment RT/OS, hosted by eight workcells. Application software is implemented using Object-Orientated programming paradigms. All accelerator components become interface functions as instances of an abstract device model class. Methods defined here present an abstract picture of the accelerator giving immediate access to device states and parameters. Operator interaction is defined by building views and controllers for the model. Higher level functions, such as defining an acceleration cycle, are easily developed and modified with the accelerator connected on-line to the model. In the first year of COSY operation the object based approach for a control system, together with a rapid prototyped man-machine interface has brought to light the potential of new functions such as on-line, real time programming on a running system yielding high programming performance. The advantages of this approach have not been, until now, fully appreciated. ((orig.))

  11. An EOG-Based Human-Machine Interface for Wheelchair Control.

    Science.gov (United States)

    Huang, Qiyun; He, Shenghong; Wang, Qihong; Gu, Zhenghui; Peng, Nengneng; Li, Kai; Zhang, Yuandong; Shao, Ming; Li, Yuanqing

    2017-07-27

    Non-manual human-machine interfaces (HMIs) have been studied for wheelchair control with the aim of helping severely paralyzed individuals regain some mobility. The challenge is to rapidly, accurately and sufficiently produce control commands, such as left and right turns, forward and backward motions, acceleration, deceleration, and stopping. In this paper, a novel electrooculogram (EOG)-based HMI is proposed for wheelchair control. Thirteen flashing buttons are presented in the graphical user interface (GUI), and each of the buttons corresponds to a command. These buttons flash on a one-by-one manner in a pre-defined sequence. The user can select a button by blinking in sync with its flashes. The algorithm detects the eye blinks from a channel of vertical EOG data and determines the user's target button based on the synchronization between the detected blinks and the button's flashes. For healthy subjects/patients with spinal cord injuries (SCIs), the proposed HMI achieved an average accuracy of 96.7%/91.7% and a response time of 3.53 s/3.67 s with 0 false positive rates (FPRs). Using only one channel of vertical EOG signals associated with eye blinks, the proposed HMI can accurately provide sufficient commands with a satisfactory response time. The proposed HMI provides a novel non-manual approach for severely paralyzed individuals to control a wheelchair. Compared with a newly established EOG-based HMI, the proposed HMI can generate more commands with higher accuracy, lower FPR and fewer electrodes.

  12. Human-machine interfaces based on EMG and EEG applied to robotic systems

    Directory of Open Access Journals (Sweden)

    Sarcinelli-Filho Mario

    2008-03-01

    Full Text Available Abstract Background Two different Human-Machine Interfaces (HMIs were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well. Results Experiments using the EMG-based HMI were carried out by eight individuals, who were asked to accomplish ten eye blinks with each eye, in order to test the eye blink detection algorithm. An average rightness rate of about 95% reached by individuals with the ability to blink both eyes allowed to conclude that the system could be used to command devices. Experiments with EEG consisted of inviting 25 people (some of them had suffered cases of meningitis and epilepsy to test the system. All of them managed to deal with the HMI in only one training session. Most of them learnt how to use such HMI in less than 15 minutes. The minimum and maximum training times observed were 3 and 50 minutes, respectively. Conclusion Such works are the initial parts of a system to help people with neuromotor diseases, including those with severe dysfunctions. The next steps are to convert a commercial wheelchair in an autonomous mobile vehicle; to implement the HMI onboard the autonomous wheelchair thus obtained to assist people with motor diseases, and to explore the potentiality of EEG signals, making the EEG-based HMI more robust and faster, aiming at using it to help individuals with severe motor dysfunctions.

  13. Two Dream Machines: Television and the Human Brain.

    Science.gov (United States)

    Deming, Caren J.

    Research into brain physiology and dream psychology have helped to illuminate the biological purposes and processes of dreaming. Physical and functional characteristics shared by dreaming and television include the perception of visual and auditory images, operation in a binary mode, and the encoding of visual information. Research is needed in…

  14. Parallelization of MCNP Monte Carlo neutron and photon transport code in parallel virtual machine and message passing interface

    International Nuclear Information System (INIS)

    Deng Li; Xie Zhongsheng

    1999-01-01

    The coupled neutron and photon transport Monte Carlo code MCNP (version 3B) has been parallelized in parallel virtual machine (PVM) and message passing interface (MPI) by modifying a previous serial code. The new code has been verified by solving sample problems. The speedup increases linearly with the number of processors and the average efficiency is up to 99% for 12-processor. (author)

  15. BRAIN-COMPUTER-INTERFACE – SUPPORTED MOTOR IMAGERY TRAININTG FOR PATIENTS WITH HEMIPARESIS

    Directory of Open Access Journals (Sweden)

    O. A. Mokienko

    2013-01-01

    Full Text Available The aim of study was to assess the feasibility of motor imagery supported brain-computer interface in patients with hemiparesis. 13 patients with central paresis of the hand and 15 healthy volunteers were learning to control EEG-based interface with feedback. No differences on interface control quality were found between patients and healthy subjects. The trainings were accompanied by the desynchronization of sensorimotor rhythm. In patients with cortical damage the source of EEG-activity was dislocated.

  16. New KF-PP-SVM classification method for EEG in brain-computer interfaces.

    Science.gov (United States)

    Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

    2014-01-01

    Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

  17. Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface.

    Science.gov (United States)

    Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

    2015-11-30

    Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain-computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually. This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns. SFBCSP estimates CSP features on multiple signals that are filtered from raw EEG data at a set of overlapping bands. The filter bands that result in significant CSP features are then selected in a supervised way by exploiting sparse regression. A support vector machine (SVM) is implemented on the selected features for MI classification. Two public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) are used to validate the proposed SFBCSP method. Experimental results demonstrate that SFBCSP help improve the classification performance of MI. The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods. The proposed SFBCSP is a potential method for improving the performance of MI-based BCI. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome.

    Science.gov (United States)

    Oken, Barry S; Orhan, Umut; Roark, Brian; Erdogmus, Deniz; Fowler, Andrew; Mooney, Aimee; Peters, Betts; Miller, Meghan; Fried-Oken, Melanie B

    2014-05-01

    Some noninvasive brain-computer interface (BCI) systems are currently available for locked-in syndrome (LIS) but none have incorporated a statistical language model during text generation. To begin to address the communication needs of individuals with LIS using a noninvasive BCI that involves rapid serial visual presentation (RSVP) of symbols and a unique classifier with electroencephalography (EEG) and language model fusion. The RSVP Keyboard was developed with several unique features. Individual letters are presented at 2.5 per second. Computer classification of letters as targets or nontargets based on EEG is performed using machine learning that incorporates a language model for letter prediction via Bayesian fusion enabling targets to be presented only 1 to 4 times. Nine participants with LIS and 9 healthy controls were enrolled. After screening, subjects first calibrated the system, and then completed a series of balanced word generation mastery tasks that were designed with 5 incremental levels of difficulty, which increased by selecting phrases for which the utility of the language model decreased naturally. Six participants with LIS and 9 controls completed the experiment. All LIS participants successfully mastered spelling at level 1 and one subject achieved level 5. Six of 9 control participants achieved level 5. Individuals who have incomplete LIS may benefit from an EEG-based BCI system, which relies on EEG classification and a statistical language model. Steps to further improve the system are discussed.

  19. Design of a 32-channel EEG system for brain control interface applications.

    Science.gov (United States)

    Wang, Ching-Sung

    2012-01-01

    This study integrates the hardware circuit design and the development support of the software interface to achieve a 32-channel EEG system for BCI applications. Since the EEG signals of human bodies are generally very weak, in addition to preventing noise interference, it also requires avoiding the waveform distortion as well as waveform offset and so on; therefore, the design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply will generate DC bias which affects the measurement signals. For this reason, this study specially designs an improved single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. For the analog circuit, a frequency band will be taken out through the filtering circuit and then the digital filtering design will be used to adjust the extracted frequency band for the target frequency band, combining with MATLAB to design man-machine interface for displaying brain wave. Finally the measured signals are compared to the traditional 32-channel EEG signals. In addition to meeting the IFCN standards, the system design also conducted measurement verification in the standard EEG isolation room in order to demonstrate the accuracy and reliability of this system design.

  20. Automated selection of brain regions for real-time fMRI brain-computer interfaces

    Science.gov (United States)

    Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

    2017-02-01

    Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.

  1. Development of nuclear power plant operator simulator for man-machine interface evaluation

    International Nuclear Information System (INIS)

    Nakagawa, Takashi; Nakatani, Yoshio; Sasaki, Kazunori; Yoshikawa, Hidekazu; Takahashi, Makoto; Furuta, Tomihiko; Hasegawa, Akira.

    1997-01-01

    The operational safety in nuclear power plants depends strongly on man machine interfaces (MMI), such as assignment of equipment on control boards and operation procedures in emergency situations. Therefore, the evaluation and analysis methods for the MMI are important. In order for the methods to be practical, the methods should be executed in each step of design and be easy for designers to use. We aim to develop SEAMAID system: a computer supported system for evaluating and analyzing the MMI by simulating the interaction between the operator and the machine. In this paper, we discuss problems of the conventional methods and the required functions of the operator simulator for the SEAMAID. The operator simulator executes not human errors but correct behavior which follows the operational procedure. The SEAMAID evaluates the MMI by finding potential human errors which could occur in the simulated interactions and points out the problematic interaction parts which could induce human errors. We construct the operator simulator by combining the human model which was proposed by Prof. Reason, and the knowledge base model based on the Petri net model. This simulator can treat frequency parameter which represents the degree of frequency of using a certain knowledge. We conduct two sample simulations in different frequency parameters in the same scenario. These simulation results show that even if the operator behaves correctly following the procedure, there are alternative task sequences. We verify that the simulated interactions are in good agreement with the actual one. Also, we propose the method to point out the problematic parts in the interactions based on the working memory consumption. (author)

  2. Development of nuclear power plant operator simulator for man-machine interface evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Nakagawa, Takashi; Nakatani, Yoshio; Sasaki, Kazunori [Mitsubishi Electric Corp., Amagasaki, Hyogo (Japan). Industrial Electronics and Systems Development Lab.; Yoshikawa, Hidekazu; Takahashi, Makoto; Furuta, Tomihiko; Hasegawa, Akira

    1997-08-01

    The operational safety in nuclear power plants depends strongly on man machine interfaces (MMI), such as assignment of equipment on control boards and operation procedures in emergency situations. Therefore, the evaluation and analysis methods for the MMI are important. In order for the methods to be practical, the methods should be executed in each step of design and be easy for designers to use. We aim to develop SEAMAID system: a computer supported system for evaluating and analyzing the MMI by simulating the interaction between the operator and the machine. In this paper, we discuss problems of the conventional methods and the required functions of the operator simulator for the SEAMAID. The operator simulator executes not human errors but correct behavior which follows the operational procedure. The SEAMAID evaluates the MMI by finding potential human errors which could occur in the simulated interactions and points out the problematic interaction parts which could induce human errors. We construct the operator simulator by combining the human model which was proposed by Prof. Reason, and the knowledge base model based on the Petri net model. This simulator can treat frequency parameter which represents the degree of frequency of using a certain knowledge. We conduct two sample simulations in different frequency parameters in the same scenario. These simulation results show that even if the operator behaves correctly following the procedure, there are alternative task sequences. We verify that the simulated interactions are in good agreement with the actual one. Also, we propose the method to point out the problematic parts in the interactions based on the working memory consumption. (author)

  3. A quantum theory of the mind-brain interface

    International Nuclear Information System (INIS)

    Stapp, H.P.

    1990-01-01

    The Heisenberg quantum mechanical conception of nature is extended and applied to the brain. Strict adherence to the principle of parsimony, and to quantum thinking, produces naturally, on the basis of an overview of brain operation compatible with the information provided by the brain sciences, a unified description of the physical and mental aspects of nature that can account in principle for the full content of felt human experience. 33 refs

  4. A quantum theory of the mind-brain interface

    Energy Technology Data Exchange (ETDEWEB)

    Stapp, H.P.

    1990-07-30

    The Heisenberg quantum mechanical conception of nature is extended and applied to the brain. Strict adherence to the principle of parsimony, and to quantum thinking, produces naturally, on the basis of an overview of brain operation compatible with the information provided by the brain sciences, a unified description of the physical and mental aspects of nature that can account in principle for the full content of felt human experience. 33 refs.

  5. Detection of User Independent Single Trial ERPs in Brain Computer Interfaces: An Adaptive Spatial Filtering Approach

    DEFF Research Database (Denmark)

    Leza, Cristina; Puthusserypady, Sadasivan

    2017-01-01

    Brain Computer Interfaces (BCIs) use brain signals to communicate with the external world. The main challenges to address are speed, accuracy and adaptability. Here, a novel algorithm for P300 based BCI spelling system is presented, specifically suited for single-trial detection of Event...

  6. Mind the Sheep! User Experience Evaluation & Brain-Computer Interface Games

    NARCIS (Netherlands)

    Gürkök, Hayrettin

    2012-01-01

    A brain-computer interface (BCI) infers our actions (e.g. a movement), intentions (e.g. preparation for a movement) and psychological states (e.g. emotion, attention) by interpreting our brain signals. It uses the inferences it makes to manipulate a computer. Although BCIs have long been used

  7. Toward affective brain-computer interfaces : exploring the neurophysiology of affect during human media interaction

    NARCIS (Netherlands)

    Mühl, C.

    2012-01-01

    Affective Brain-Computer Interfaces (aBCI), the sensing of emotions from brain activity, seems a fantasy from the realm of science fiction. But unlike faster-than-light travel or teleportation, aBCI seems almost within reach due to novel sensor technologies, the advancement of neuroscience, and the

  8. An information theory based approach for quantitative evaluation of man-machine interface complexity

    International Nuclear Information System (INIS)

    Kang, Hyun Gook

    1999-02-01

    In complex and high-risk work conditions, especially such as in nuclear power plants, human understanding of the plant is highly cognitive and thus largely dependent on the effectiveness of the man-machine interface system. In order to provide more effective and reliable operating conditions for future nuclear power plants, developing more credible and easy to use evaluation methods will afford great help in designing interface systems in a more efficient manner. In this study, in order to analyze the human-machine interactions, I propose the Human-processor Communication(HPC) model which is based on the information flow concept. It identifies the information flow around a human-processor. Information flow has two aspects: appearance and content. Based on the HPC model, I propose two kinds of measures for evaluating a user interface from the viewpoint of these two aspects of information flow. They measure the communicative complexity of each aspect. In this study, for the evaluation of the aspect of appearance, I propose three complexity measures: Operation Complexity, Transition Complexity, and Screen Complexity. Each one of these measures has its own physical meaning. Two experiments carried out in this work support the utility of these measures. The result of the quiz game experiment shows that as the complexity of task context increases, the usage of the interface system becomes more complex. The experimental results of the three example systems(digital view, LDP style view and hierarchy view) show the utility of the proposed complexity measures. In this study, for the evaluation of the aspect of content, I propose the degree of informational coincidence, R (K, P) as a measure for the usefulness of an alarm-processing system. It is designed to perform user-oriented evaluation based on the informational entropy concept. It will be especially useful inearly design phase because designers can estimate the usefulness of an alarm system by short calculations instead

  9. An information theory based approach for quantitative evaluation of man-machine interface complexity

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Hyun Gook

    1999-02-15

    In complex and high-risk work conditions, especially such as in nuclear power plants, human understanding of the plant is highly cognitive and thus largely dependent on the effectiveness of the man-machine interface system. In order to provide more effective and reliable operating conditions for future nuclear power plants, developing more credible and easy to use evaluation methods will afford great help in designing interface systems in a more efficient manner. In this study, in order to analyze the human-machine interactions, I propose the Human-processor Communication(HPC) model which is based on the information flow concept. It identifies the information flow around a human-processor. Information flow has two aspects: appearance and content. Based on the HPC model, I propose two kinds of measures for evaluating a user interface from the viewpoint of these two aspects of information flow. They measure the communicative complexity of each aspect. In this study, for the evaluation of the aspect of appearance, I propose three complexity measures: Operation Complexity, Transition Complexity, and Screen Complexity. Each one of these measures has its own physical meaning. Two experiments carried out in this work support the utility of these measures. The result of the quiz game experiment shows that as the complexity of task context increases, the usage of the interface system becomes more complex. The experimental results of the three example systems(digital view, LDP style view and hierarchy view) show the utility of the proposed complexity measures. In this study, for the evaluation of the aspect of content, I propose the degree of informational coincidence, R (K, P) as a measure for the usefulness of an alarm-processing system. It is designed to perform user-oriented evaluation based on the informational entropy concept. It will be especially useful inearly design phase because designers can estimate the usefulness of an alarm system by short calculations instead

  10. Human-Machine Interface for the Control of Multi-Function Systems Based on Electrocutaneous Menu: Application to Multi-Grasp Prosthetic Hands.

    Directory of Open Access Journals (Sweden)

    Jose Gonzalez-Vargas

    Full Text Available Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI cannot easily accommodate the system's complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions. This approach can be difficult to implement when there are many functions (necessitating many command patterns and/or the user has a considerable impairment (limited number of available signal sources. In this study, we propose a novel concept for a general-purpose HMI where the controller and the user communicate bidirectionally to select the desired function. The system first presents possible choices to the user via electro-tactile stimulation; the user then acknowledges the desired choice by generating a single command signal. Therefore, the proposed approach simplifies the user communication interface (one signal to generate, decoding (one signal to recognize, and allows selecting from a number of options. To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels. We performed experiments in healthy subjects and with one amputee to test the feasibility of the novel approach. The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces. The presented approach has a general applicability and the obtained results point out that it could be used to operate various assistive systems (e.g., prosthesis vs. wheelchair, or it could be integrated into other control schemes (e.g., myoelectric control, brain-machine interfaces in order to improve the usability of existing low

  11. Human-Machine Interface for the Control of Multi-Function Systems Based on Electrocutaneous Menu: Application to Multi-Grasp Prosthetic Hands.

    Science.gov (United States)

    Gonzalez-Vargas, Jose; Dosen, Strahinja; Amsuess, Sebastian; Yu, Wenwei; Farina, Dario

    2015-01-01

    Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system's complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions. This approach can be difficult to implement when there are many functions (necessitating many command patterns) and/or the user has a considerable impairment (limited number of available signal sources). In this study, we propose a novel concept for a general-purpose HMI where the controller and the user communicate bidirectionally to select the desired function. The system first presents possible choices to the user via electro-tactile stimulation; the user then acknowledges the desired choice by generating a single command signal. Therefore, the proposed approach simplifies the user communication interface (one signal to generate), decoding (one signal to recognize), and allows selecting from a number of options. To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels. We performed experiments in healthy subjects and with one amputee to test the feasibility of the novel approach. The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces. The presented approach has a general applicability and the obtained results point out that it could be used to operate various assistive systems (e.g., prosthesis vs. wheelchair), or it could be integrated into other control schemes (e.g., myoelectric control, brain-machine interfaces) in order to improve the usability of existing low-bandwidth HMIs.

  12. A Brain-Computer Interface Project Applied in Computer Engineering

    Science.gov (United States)

    Katona, Jozsef; Kovari, Attila

    2016-01-01

    Keeping up with novel methods and keeping abreast of new applications are crucial issues in engineering education. In brain research, one of the most significant research areas in recent decades, many developments have application in both modern engineering technology and education. New measurement methods in the observation of brain activity open…

  13. Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke.

    Science.gov (United States)

    Mukaino, Masahiko; Ono, Takashi; Shindo, Keiichiro; Fujiwara, Toshiyuki; Ota, Tetsuo; Kimura, Akio; Liu, Meigen; Ushiba, Junichi

    2014-04-01

    Brain computer interface technology is of great interest to researchers as a potential therapeutic measure for people with severe neurological disorders. The aim of this study was to examine the efficacy of brain computer interface, by comparing conventional neuromuscular electrical stimulation and brain computer interface-driven neuromuscular electrical stimulation, using an A-B-A-B withdrawal single-subject design. A 38-year-old male with severe hemiplegia due to a putaminal haemorrhage participated in this study. The design involved 2 epochs. In epoch A, the patient attempted to open his fingers during the application of neuromuscular electrical stimulation, irrespective of his actual brain activity. In epoch B, neuromuscular electrical stimulation was applied only when a significant motor-related cortical potential was observed in the electroencephalogram. The subject initially showed diffuse functional magnetic resonance imaging activation and small electro-encephalogram responses while attempting finger movement. Epoch A was associated with few neurological or clinical signs of improvement. Epoch B, with a brain computer interface, was associated with marked lateralization of electroencephalogram (EEG) and blood oxygenation level dependent responses. Voluntary electromyogram (EMG) activity, with significant EEG-EMG coherence, was also prompted. Clinical improvement in upper-extremity function and muscle tone was observed. These results indicate that self-directed training with a brain computer interface may induce activity- dependent cortical plasticity and promote functional recovery. This preliminary clinical investigation encourages further research using a controlled design.

  14. Development of new plant monitoring and control system with advanced man-machine interfaces NUCAMM-80

    International Nuclear Information System (INIS)

    Sato, Hideyuki; Joge, Toshio; Miyake, Masao; Kishi, Shoichi

    1981-01-01

    BWR type nuclear power stations are the typical plants adopting central monitoring system in view of the size of the scale of system and the prevention of radiation exposure. Central control boards became large as much informations and many operating tools are concentrated on them. Recently, the unit capacity has increased, and the safety has been strengthened, therefore more improvement of the man-machine interface is required concerning the monitoring of plant operation. Hitachi Ltd. developed the central monitoring and control system for nuclear power stations ''NUCAMM-80'', concentrating related fundamental techniques such as the collection of plant informations, the expansion of automatic operation, the ergonomic re-evaluation of the arrangement of panels and subsystems, and the effective use of functional hardwares such as controlling computers and cathode ray tubes, for the purposes of improving the reliability of plant operation and the rate of operation, the reduction of the burden of operators and drastic labor saving. The fundamental policy of the development, the construction of the system, panel layout and the collection of informations, the development of the system for plant automation, the development of plant diagnosis and prevention systems, computer system and the merits of this system are described. (Kako, I.)

  15. A basic experimental study on mental workload for human cognitive work at man-machine interface

    International Nuclear Information System (INIS)

    Yoshikawa, Hidekazu; Shimoda, Hiroshi; Wakamori, Osamu; Nagai, Yoshinori

    1995-01-01

    The nature and measurement methods of mental workload (MWL) for human cognitive activity at man-machine interface (MMI) were firstly discussed from the viewpoint of human information process model. Then, a model VDT experiment which simplifies the actual human-computer-interaction situation at MMI, was conducted for several subjects, where two subjects participated in experiment series and tried to solve the same cognitive task in competition. Adopted experimental parameters were (i)different kinds of cognitive task, and (ii)cycle time of information display, to see the influence on MWL characteristics from psycho-physiological viewpoint. A special processing unit for eye camera was developed and used for measuring subjects' eye movement characteristics. Concerning data analysis, total number of display presentation until problem solving (ie., total information needed for problem solving) was assumed as anchoring objective measure for MWL, and the investigations were conducted from two aspects; (i)global interpretation on MWL characteristics seen in the subjects' behavior from viewpoint of human information process model, and (ii)applicability of MWL by means of biocybernetic method. As regards to applicability of biocybernetic method, the nature of MWL characteristics was first divided into two aspects : (i)efficiency of visual information acquisition, and (ii)difficulty of inner cognitive process to solve problem, both in time pressure situation. Then, the data analysis results for eye movement characteristics were correlated to (i), while for heart rate characteristics, (ii). (author)

  16. A study on the application of voice interaction in automotive human machine interface experience design

    Science.gov (United States)

    Huang, Zhaohui; Huang, Xiemin

    2018-04-01

    This paper, firstly, introduces the application trend of the integration of multi-channel interactions in automotive HMI ((Human Machine Interface) from complex information models faced by existing automotive HMI and describes various interaction modes. By comparing voice interaction and touch screen, gestures and other interaction modes, the potential and feasibility of voice interaction in automotive HMI experience design are concluded. Then, the related theories of voice interaction, identification technologies, human beings' cognitive models of voices and voice design methods are further explored. And the research priority of this paper is proposed, i.e. how to design voice interaction to create more humane task-oriented dialogue scenarios to enhance interactive experiences of automotive HMI. The specific scenarios in driving behaviors suitable for the use of voice interaction are studied and classified, and the usability principles and key elements for automotive HMI voice design are proposed according to the scenario features. Then, through the user participatory usability testing experiment, the dialogue processes of voice interaction in automotive HMI are defined. The logics and grammars in voice interaction are classified according to the experimental results, and the mental models in the interaction processes are analyzed. At last, the voice interaction design method to create the humane task-oriented dialogue scenarios in the driving environment is proposed.

  17. A comparative analysis of three non-invasive Human-Machine Interfaces for the disabled

    Directory of Open Access Journals (Sweden)

    Vikram eRavindra

    2014-10-01

    Full Text Available In the framework of rehabilitation robotics, a major role is played by theHuman-Machine Interface (HMI used to gather the patient's intent from biologicalsignals, and convert them into control signals for the robotic artifact. Surprisingly,decades of research haven't yet declared what the optimal HMI is in this context;in particular, the traditional approach based upon surface electromyography (sEMGstill yields unreliable results due to the inherent variability of the signal. Toovercome this problem, the scientific community has recently been advocating thediscovery, analysis and usage of novel HMIs to supersede or augment sEMG; a comparativeanalysis of such HMIs is therefore a very desirable investigation.In this paper we compare three such HMIs employed in the detection of finger forces,namely sEMG, ultrasound imaging and pressure sensing. The comparison is performed alongfour main lines: the accuracy in the prediction, the stability over time, the wearabilityand the cost. A psychophysical experiment involving ten intact subjects engaged ina simple finger-flexion task was set up. Our results show that, at least in thisexperiment, pressure sensing and sEMG yield comparably good prediction accuraciesas opposed to ultrasound imaging; and that pressure sensing enjoys a much better stabilitythan sEMG.Given that pressure sensors are as wearable as sEMG electrodes but way cheaper, we claimthat this HMI could represent a valid alternative /augmentation to sEMG to control amulti-fingered hand prosthesis.

  18. Human machine interface to manually drive rhombic like vehicles in remote handling operations

    Energy Technology Data Exchange (ETDEWEB)

    Lopes, Pedro; Vale, Alberto [Instituto de Plasmas e Fusao Nuclear, Instituto Superior Tecnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); Ventura, Rodrigo [Institute for Systems and Robotics, Instituto Superior Tecnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal)

    2015-07-01

    In the thermonuclear experimental reactor ITER, a vehicle named CTS is designed to transport a container with activated components inside the buildings. In nominal operations, the CTS is autonomously guided under supervision. However, in some unexpected situations, such as in rescue and recovery operations, the autonomous mode must be overridden and the CTS must be remotely guided by an operator. The CTS is a rhombic-like vehicle, with two drivable and steerable wheels along its longitudinal axis, providing omni-directional capabilities. The rhombic kinematics correspond to four control variables, which are difficult to manage in manual mode operation. This paper proposes a Human Machine Interface (HMI) to remotely guide the vehicle in manual mode. The proposed solution is implemented using a HMI with an encoder connected to a micro-controller and an analog 2-axis joystick. Experimental results were obtained comparing the proposed solution with other controller devices in different scenarios and using a software platform that simulates the kinematics and dynamics of the vehicle. (authors)

  19. Application of a driving simulator to the development of in-vehicle human–machine-interfaces

    Directory of Open Access Journals (Sweden)

    David H. Weir

    2010-07-01

    Full Text Available The use of a driving simulator in the development of human–machine-interfaces (HMI such as a navigation, information or entertainment system is discussed. Such use addresses the need to study and evaluate the characteristics of a candidate HMI early in the R&D and design stage to ensure that it is likely to meet various objectives and requirements, and to revise the HMI as may be necessary. Those HMI requirements include such things as usability, driver comfort, and an acceptable level of attentional demand in dual task conditions (driving while using an HMI. Typically, such an HMI involves an information display to the driver, and a means for driver input to the HMI. Corresponding simulator requirements are discussed, along with typical simulator features and components. The latter include a cab, control feel systems, visual image generator, real time scenario control (task definitions, a motion system (if provided, and data acquisition. Both fixed and moving base systems are described, together with associated benefits and tradeoffs. Considerations in the design of the evaluation experiment are discussed, including definition of primary and secondary tasks, and number of driver subjects (experimental participants. Possible response and performance measures for the primary and secondary tasks are noted, together with subjective measures such as task difficulty and ease of using the HMI. The advantages of using a driving simulator to support R&D are summarized. Some typical and example simulator uses are noted.

  20. Human factors evaluation of man-machine interface for periodic safety review of nuclear power plants

    International Nuclear Information System (INIS)

    Lee, Yong Hee; Lee, Jung Woon; Park, Jae Chang; Hwang, In Koo; Lee, Hyun Cheol; Jang, Tong Il; Ku, Jin Young; Kim, Soo Jin

    2004-12-01

    This report describes the research results of human factors assessment on the MMI(Man Machine Interface) equipment as part of Periodic Safety Review(PSR) of Nuclear Power Plants(NPPs). As MMI is a key factor among human factors to be reviewed in PSR, we reviewed the MMI components of nuclear power plants in aspect of human factors engineering. The availability, suitability, and effectiveness of the MMI devices were chosen to be reviewed. The MMI devices were investigated through the review of design documents related to the MMI, survey of control panels, evaluation of experts, and experimental assessment. Checklists were used to perform this assessment and record the review results. The items mentioned by the expert comments to review in detail in relation with task procedures were tested by experiments with operators' participation. For some questionable issues arisen during this MMI review, operator workload and possibility of errors in operator actions were analysed. The reviewed MMI devices contain MCR(Main Control Room), SPDS(Safety Parameter Display System), RSP(Remote Shutdown Panel), and the selected LCBs(Local Control Boards) importantly related to safety. As results of the assessments, any significant problem challenging the safety was not found on human factors in the MMI devices. However, several small items to be changed and improved in suitability of MMI devices were discovered. An action plan is recommended to accommodate the suggestions and review comments. It will enhance the plant safety on MMI area

  1. Study on HRA-based method for assessing digital man-machine interface

    International Nuclear Information System (INIS)

    Li Pengcheng; Dai Licao; Zhang Li; Zhao Ming; Hu Hong

    2014-01-01

    In order to identify the design flaws of digital man-machine interface (MMI) that may trigger human errors or weaken the performance of operators, a HRA-based method (namely HCR + CREAM + HEC) for assessing digital MMI was established. Firstly, the HCR method was used to identify the risk scenarios of high human error probability from the overall event as a whole perspective. Then, for the identified high-risk scenarios, the CREAM was adopted to determine the various error modes and its error probability, and the failure probability was ranked. Finally, the human factors engineering checklist of digital MMI was established according to the characteristics of digital MMI, it was used to check the digital MMI with high error probability in order to identify the design flaws of digital MMI, and the suggestions of optimization were provided. The results show that the provided assessment method can quickly and efficiently identify the design flaws of digital MMI which easily trigger human errors, and the safety of operation of the digital control system for nuclear power plants can be enhanced by optimization of design. (authors)

  2. Personalized keystroke dynamics for self-powered human--machine interfacing.

    Science.gov (United States)

    Chen, Jun; Zhu, Guang; Yang, Jin; Jing, Qingshen; Bai, Peng; Yang, Weiqing; Qi, Xuewei; Su, Yuanjie; Wang, Zhong Lin

    2015-01-27

    The computer keyboard is one of the most common, reliable, accessible, and effective tools used for human--machine interfacing and information exchange. Although keyboards have been used for hundreds of years for advancing human civilization, studying human behavior by keystroke dynamics using smart keyboards remains a great challenge. Here we report a self-powered, non-mechanical-punching keyboard enabled by contact electrification between human fingers and keys, which converts mechanical stimuli applied to the keyboard into local electronic signals without applying an external power. The intelligent keyboard (IKB) can not only sensitively trigger a wireless alarm system once gentle finger tapping occurs but also trace and record typed content by detecting both the dynamic time intervals between and during the inputting of letters and the force used for each typing action. Such features hold promise for its use as a smart security system that can realize detection, alert, recording, and identification. Moreover, the IKB is able to identify personal characteristics from different individuals, assisted by the behavioral biometric of keystroke dynamics. Furthermore, the IKB can effectively harness typing motions for electricity to charge commercial electronics at arbitrary typing speeds greater than 100 characters per min. Given the above features, the IKB can be potentially applied not only to self-powered electronics but also to artificial intelligence, cyber security, and computer or network access control.

  3. Integrated digital control and man-machine interface for complex remote handing systems

    International Nuclear Information System (INIS)

    Rowe, J.C.; Spille, R.F.; Zimmermann, S.D.

    1987-01-01

    The Advanced Integrated Maintenance System (AIMS) is part of a continuing effort within the Consolidated Fuel Reprocessing Program at Oak Ridge National Laboratory to develop and extend the capabilities of remote manipulation and maintenance technology. The AIMS is a totally integrated approach to remote handling in hazardous environments. State-of-the-art computer systems connected through a high-speed distributed control system that supports the flexibility and expandability needed for large integrated maintenance applications. A man-Machine Interface provides high-level human interaction through a powerful color graphics menu-controlled operator console. An auxiliary control system handles the real-time processing needs for a variety of support hardware. A pair of dedicated fiber-optic-linked master/slave computer systems control the Advanced Servomanipulator master/slave arms using powerful distributed digital processing methods. The FORTH language was used as a real-time operating and development environment for the entire system, and all of these components are integrated into a control room concept that represents the latest advancements in the development of remote maintenance facilities for hazardous environments

  4. Human machine interface to manually drive rhombic like vehicles in remote handling operations

    International Nuclear Information System (INIS)

    Lopes, Pedro; Vale, Alberto; Ventura, Rodrigo

    2015-01-01

    In the thermonuclear experimental reactor ITER, a vehicle named CTS is designed to transport a container with activated components inside the buildings. In nominal operations, the CTS is autonomously guided under supervision. However, in some unexpected situations, such as in rescue and recovery operations, the autonomous mode must be overridden and the CTS must be remotely guided by an operator. The CTS is a rhombic-like vehicle, with two drivable and steerable wheels along its longitudinal axis, providing omni-directional capabilities. The rhombic kinematics correspond to four control variables, which are difficult to manage in manual mode operation. This paper proposes a Human Machine Interface (HMI) to remotely guide the vehicle in manual mode. The proposed solution is implemented using a HMI with an encoder connected to a micro-controller and an analog 2-axis joystick. Experimental results were obtained comparing the proposed solution with other controller devices in different scenarios and using a software platform that simulates the kinematics and dynamics of the vehicle. (authors)

  5. Man-machine interface systems and operator training program for ABWR in Japan

    International Nuclear Information System (INIS)

    Kunito, Susumu

    2004-01-01

    The Tokyo Electric Power Company (TEPCO) has developed a new Main Control Room design for the Advanced Boiling Water Reactor (ABWR) to improve man-machine interface. New configuration of panels and enhanced automation are some of the features of the ABWR type Main Control Room design. Various technologies such as Cathode Ray Tubes (CRTs) and Flat Displays (FDs) with touch-sensitive operations are contributed to the development of the ABWR type control room design. This design will be first applied to Kashiwazaki-Kariwa Nuclear Power Station unit 6 (K-6). To train the operators sufficiently, TEPCO reviewed the operator training program. Compared with the conventional training, new training menu will be added and the training of ABWR operators will be started 6 months earlier. An ABWR simulator is under construction and training using this simulator is scheduled to be started in August 1994, which is 18 months before fuel loading of K-6. We are reviewing malfunction modes on the simulator. (author)

  6. Software and Human-Machine Interface Development for Environmental Controls Subsystem Support

    Science.gov (United States)

    Dobson, Matthew

    2018-01-01

    The Space Launch System (SLS) is the next premier launch vehicle for NASA. It is the next stage of manned space exploration from American soil, and will be the platform in which we push further beyond Earth orbit. In preparation of the SLS maiden voyage on Exploration Mission 1 (EM-1), the existing ground support architecture at Kennedy Space Center required significant overhaul and updating. A comprehensive upgrade of controls systems was necessary, including programmable logic controller software, as well as Launch Control Center (LCC) firing room and local launch pad displays for technician use. Environmental control acts as an integral component in these systems, being the foremost system for conditioning the pad and extremely sensitive launch vehicle until T-0. The Environmental Controls Subsystem (ECS) required testing and modification to meet the requirements of the designed system, as well as the human factors requirements of NASA software for Validation and Verification (V&V). This term saw significant strides in the progress and functionality of the human-machine interfaces used at the launch pad, and improved integration with the controller code.

  7. Integrated digital control and man-machine interface for complex remote handling systems

    International Nuclear Information System (INIS)

    Rowe, J.C.; Spille, R.F.; Zimmermann, S.D.

    1986-12-01

    The Advanced Integrated Maintenance System (AIMS) is part of a continuing effort within the Consolidated Fuel Reprocessing Program at Oak Ridge National Laboratory to develop and extend the capabilities of remote manipulation and maintenance technology. The AIMS is a totally integrated approach to remote handling in hazardous environments. State-of-the-art computer systems connected through a high-speed communication network provide a real-time distributed control system that supports the flexibility and expandability needed for large integrated maintenance applications. A Man-Machine Interface provides high-level human interaction through a powerful color graphics menu-controlled operator console. An auxiliary control system handles the real-time processing needs for a variety of support hardware. A pair of dedicated fiber-optic-linked master/slave computer system control the Advanced Servomanipulator master/slave arms using powerful distributed digital processing methods. The FORTH language was used as a real-time operating and development environment for the entire system, and all of these components are integrated into a control room concept that represents the latest advancements in the development of remote maintenance facilities for hazardous environments

  8. Man/machine interface algorithm for advanced delayed-neutron signal characterization system

    International Nuclear Information System (INIS)

    Gross, K.C.

    1985-01-01

    The present failed-element rupture detector (FERD) at Experimental Breeder Reactor II (EBR-II) consists of a single bank of delayed-neutron (DN) detectors at a fixed transit time from the core. Plans are currently under way to upgrade the FERD in 1986 and provide advanced DN signal characterization capability that is embodied in an equivalent-recoil-area (ERA) meter. The new configuration will make available to the operator a wealth of quantitative diagnostic information related to the condition and dynamic evolution of a fuel breach. The diagnostic parameters will include a continuous reading of the ERA value for the breach; the transit time, T/sub tr/, for DN emitters traveling from the core to the FERD; and the isotopic holdup time, T/sub h/, for the source. To enhance the processing, interpretation, and display of these parameters to the reactor operator, a man/machine interface (MMI) algorithm has been developed to run in the background on EBR-II's data acquisition system (DAS). The purpose of this paper is to describe the features and implementation of this newly developed MMI algorithm

  9. A human-machine interface evaluation method: A difficulty evaluation method in information searching (DEMIS)

    International Nuclear Information System (INIS)

    Ha, Jun Su; Seong, Poong Hyun

    2009-01-01

    A human-machine interface (HMI) evaluation method, which is named 'difficulty evaluation method in information searching (DEMIS)', is proposed and demonstrated with an experimental study. The DEMIS is based on a human performance model and two measures of attentional-resource effectiveness in monitoring and detection tasks in nuclear power plants (NPPs). Operator competence and HMI design are modeled to be most significant factors to human performance. One of the two effectiveness measures is fixation-to-importance ratio (FIR) which represents attentional resource (eye fixations) spent on an information source compared to importance of the information source. The other measure is selective attention effectiveness (SAE) which incorporates FIRs for all information sources. The underlying principle of the measures is that the information source should be selectively attended to according to its informational importance. In this study, poor performance in information searching tasks is modeled to be coupled with difficulties caused by poor mental models of operators or/and poor HMI design. Human performance in information searching tasks is evaluated by analyzing the FIR and the SAE. Operator mental models are evaluated by a questionnaire-based method. Then difficulties caused by a poor HMI design are evaluated by a focused interview based on the FIR evaluation and then root causes leading to poor performance are identified in a systematic way.

  10. Technology Roadmap Instrumentation, Control, and Human-Machine Interface to Support DOE Advanced Nuclear Energy Programs

    Energy Technology Data Exchange (ETDEWEB)

    Donald D Dudenhoeffer; Burce P Hallbert

    2007-03-01

    Instrumentation, Controls, and Human-Machine Interface (ICHMI) technologies are essential to ensuring delivery and effective operation of optimized advanced Generation IV (Gen IV) nuclear energy systems. In 1996, the Watts Bar I nuclear power plant in Tennessee was the last U.S. nuclear power plant to go on line. It was, in fact, built based on pre-1990 technology. Since this last U.S. nuclear power plant was designed, there have been major advances in the field of ICHMI systems. Computer technology employed in other industries has advanced dramatically, and computing systems are now replaced every few years as they become functionally obsolete. Functional obsolescence occurs when newer, more functional technology replaces or supersedes an existing technology, even though an existing technology may well be in working order.Although ICHMI architectures are comprised of much of the same technology, they have not been updated nearly as often in the nuclear power industry. For example, some newer Personal Digital Assistants (PDAs) or handheld computers may, in fact, have more functionality than the 1996 computer control system at the Watts Bar I plant. This illustrates the need to transition and upgrade current nuclear power plant ICHMI technologies.

  11. Brain-computer interface supported collaborative work: Implications for rehabilitation.

    Science.gov (United States)

    Nam, C S; Lee, J; Bahn, S

    2013-01-01

    Working together and collaborating in a group can provide greater benefits for people with severe motor disability. However, it is still not clear how collaboration should be supported by BCI systems. The present study explored BCI-supported collaborative work by investigating differences in performance and brain activity between when a pair of users performs a task jointly with each other and when they do alone only through means of their brain activity. We found differences in performance and brain activity between different work conditions. The results of this research should provide fundamental knowledge of BCI-supported cooperative work.

  12. Machine learning classifier using abnormal brain network topological metrics in major depressive disorder.

    Science.gov (United States)

    Guo, Hao; Cao, Xiaohua; Liu, Zhifen; Li, Haifang; Chen, Junjie; Zhang, Kerang

    2012-12-05

    Resting state functional brain networks have been widely studied in brain disease research. However, it is currently unclear whether abnormal resting state functional brain network metrics can be used with machine learning for the classification of brain diseases. Resting state functional brain networks were constructed for 28 healthy controls and 38 major depressive disorder patients by thresholding partial correlation matrices of 90 regions. Three nodal metrics were calculated using graph theory-based approaches. Nonparametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in six different algorithms. We used statistical significance as the threshold for selecting features and measured the accuracies of six classifiers with different number of features. A sensitivity analysis method was used to evaluate the importance of different features. The result indicated that some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia, medial temporal, and prefrontal regions. Support vector machine with radial basis kernel function algorithm and neural network algorithm exhibited the highest average accuracy (79.27 and 78.22%, respectively) with 28 features (Pdisorder is associated with abnormal functional brain network topological metrics and statistically significant nodal metrics can be successfully used for feature selection in classification algorithms.

  13. Brain-machine interfaces in space: Using spontaneous rather than intentionally generated brain signals

    NARCIS (Netherlands)

    Coffey, E.B.J.; Brouwer, A.M.; Wilschut, E.S.; Erp, J.B.F. van

    2010-01-01

    De auteurs bespreken de beperkingen en mogelijkheden van gesuggereerde BMI toepassingen in een ruimtevaart en breken een lans voor BMIs die zijn gebaseerd op spontane in plaats van op doelbewuste hersensignalen

  14. Neuroengineering tools/applications for bidirectional interfaces, brain computer interfaces, and neuroprosthetic implants - a review of recent progress

    Directory of Open Access Journals (Sweden)

    Ryan M Rothschild

    2010-10-01

    Full Text Available The main focus of this review is to provide a holistic amalgamated overview of the most recent human in vivo techniques for implementing brain-computer interfaces (BCIs, bidirectional interfaces and neuroprosthetics. Neuroengineering is providing new methods for tackling current difficulties; however neuroprosthetics have been studied for decades. Recent progresses are permitting the design of better systems with higher accuracies, repeatability and system robustness. Bidirectional interfaces integrate recording and the relaying of information from and to the brain for the development of BCIs. The concepts of non-invasive and invasive recording of brain activity are introduced. This includes classical and innovative techniques like electroencephalography (EEG and near-infrared spectroscopy (NIRS. Then the problem of gliosis and solutions for (semi- permanent implant biocompatibility such as innovative implant coatings, materials and shapes are discussed. Implant power and the transmission of their data through implanted pulse generators (IPGs and wireless telemetry are taken into account. How sensation can be relayed back to the brain to increase integration of the neuroengineered systems with the body by methods such as micro-stimulation and transcranial magnetic stimulation (TMS are then addressed. The neuroprosthetic section discusses some of the various types and how they operate. Visual prosthetics are discussed and the three types, dependant on implant location, are examined. Auditory prosthetics, being cochlear or cortical, are then addressed. Replacement hand and limb prosthetics are then considered. These are followed by sections concentrating on the control of wheelchairs, computers and robotics directly from brain activity as recorded by non-invasive and invasive techniques.

  15. Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface

    Science.gov (United States)

    LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

    2013-01-01

    Objective At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional physical space using noninvasive scalp EEG in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that operation of a real world device has on subjects’ control with comparison to a two-dimensional virtual cursor task. Approach Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a three-dimensional physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m/s. Significance Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user’s ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in the three-dimensional physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG based BCI systems to accomplish complex control in three-dimensional physical space. The present study may serve as a framework for the investigation of multidimensional non-invasive brain-computer interface control in a physical environment using telepresence robotics. PMID:23735712

  16. Control of an Ambulatory Exoskeleton with a Brain–Machine Interface for Spinal Cord Injury Gait Rehabilitation

    Science.gov (United States)

    López-Larraz, Eduardo; Trincado-Alonso, Fernando; Rajasekaran, Vijaykumar; Pérez-Nombela, Soraya; del-Ama, Antonio J.; Aranda, Joan; Minguez, Javier; Gil-Agudo, Angel; Montesano, Luis

    2016-01-01

    The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain–machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton—without any weight or balance support—for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and

  17. Ownership and Agency of an Independent Supernumerary Hand Induced by an Imitation Brain-Computer Interface.

    Science.gov (United States)

    Bashford, Luke; Mehring, Carsten

    2016-01-01

    To study body ownership and control, illusions that elicit these feelings in non-body objects are widely used. Classically introduced with the Rubber Hand Illusion, these illusions have been replicated more recently in virtual reality and by using brain-computer interfaces. Traditionally these illusions investigate the replacement of a body part by an artificial counterpart, however as brain-computer interface research develops it offers us the possibility to explore the case where non-body objects are controlled in addition to movements of our own limbs. Therefore we propose a new illusion designed to test the feeling of ownership and control of an independent supernumerary hand. Subjects are under the impression they control a virtual reality hand via a brain-computer interface, but in reality there is no causal connection between brain activity and virtual hand movement but correct movements are observed with 80% probability. These imitation brain-computer interface trials are interspersed with movements in both the subjects' real hands, which are in view throughout the experiment. We show that subjects develop strong feelings of ownership and control over the third hand, despite only receiving visual feedback with no causal link to the actual brain signals. Our illusion is crucially different from previously reported studies as we demonstrate independent ownership and control of the third hand without loss of ownership in the real hands.

  18. As We May Think and Be: Brain-computer interfaces to expand the substrate of mind

    Directory of Open Access Journals (Sweden)

    Mijail Demian Serruya

    2015-04-01

    Full Text Available Over a half-century ago, the scientist Vannevar Bush explored the conundrum of how to tap the exponentially rising sea of human knowledge for the betterment of humanity. In his description of a hypothetical electronic library he dubbed the memex, he anticipated internet search and online encyclopedias (Bush, 1945. By blurring the boundary between brain and computer, brain-computer interfaces (BCI could lead to more efficient use of electronic resources (Schalk, 2008. We could expand the substrate of the mind itself rather than merely interfacing it to external computers. Components of brain-computer interfaces could be re-arranged to create brain-brain interfaces, or tightly interconnected links between a person’s brain and ectopic neural modules. Such modules – whether sitting in a bubbling Petri dish, rendered in reciprocally linked integrated circuits, or implanted in our belly – would mark the first step on to a path of breaking out of the limitations imposed by our phylogenetic past Novel BCI architectures could generate novel abilities to navigate and access information that might speed translational science efforts and push the boundaries of human knowledge in an unprecedented manner.

  19. Preface (to: Towards Practical Brain-Computer Interfaces)

    NARCIS (Netherlands)

    Allison, Brendan Z.; Dunne, Stephen; Leeb, Robert; Millán, Jose del R.; Allison, Brendan Z.; Dunne, Stephen; Leeb, Robert; Millán, Jose del R.; Nijholt, Antinus

    2012-01-01

    Brain–computer interface (BCI) research is advancing rapidly. The last few years have seen a dramatic rise in journal publications, academic workshops and conferences, books, new products aimed at both healthy and disabled users, research funding from different sources, and media attention. This

  20. The Brain Computer Interface Future: Time for a Strategy

    Science.gov (United States)

    2013-02-14

    neural processing software developer Mind Technologies, Geger Technologies in Austria and the Sony Corporation in Japan. The WTEC report in 2007...managing photos, video, web surfing, and music , and although in their infancy, researchers have used a web browser interface to control Google...society as a whole. The prospect of BCI entertainment , neuroprostheses, online neuroresearching and marketing, and cognitive performance enhancement

  1. The investigation of brain-computer interface for motor imagery and execution using functional near-infrared spectroscopy

    Science.gov (United States)

    Zhang, Zhen; Jiao, Xuejun; Xu, Fengang; Jiang, Jin; Yang, Hanjun; Cao, Yong; Fu, Jiahao

    2017-01-01

    Functional near-infrared spectroscopy (fNIRS), which can measure cortex hemoglobin activity, has been widely adopted in brain-computer interface (BCI). To explore the feasibility of recognizing motor imagery (MI) and motor execution (ME) in the same motion. We measured changes of oxygenated hemoglobin (HBO) and deoxygenated hemoglobin (HBR) on PFC and Motor Cortex (MC) when 15 subjects performing hand extension and finger tapping tasks. The mean, slope, quadratic coefficient and approximate entropy features were extracted from HBO as the input of support vector machine (SVM). For the four-class fNIRS-BCI classifiers, we realized 87.65% and 87.58% classification accuracy corresponding to hand extension and finger tapping tasks. In conclusion, it is effective for fNIRS-BCI to recognize MI and ME in the same motion.

  2. Energetic optimization of a piezo-based touch-operated button for man–machine interfaces

    International Nuclear Information System (INIS)

    Sun, Hao; De Vries, Theo J A; De Vries, Rene; Van Dalen, Harry

    2012-01-01

    This paper discusses the optimization of a touch-operated button for man–machine interfaces based on piezoelectric energy harvesting techniques. In the mechanical button, a common piezoelectric diaphragm, is assembled to harvest the ambient energy from the source, i.e. the operator’s touch. Under touch force load, the integrated diaphragm will have a bending deformation. Then, its mechanical strain is converted into the required electrical energy by means of the piezoelectric effect presented to the diaphragm. Structural design (i) makes the piezoceramic work under static compressive stress instead of static or dynamic tensile stress, (ii) achieves a satisfactory stress level and (iii) provides the diaphragm and the button with a fatigue lifetime in excess of millions of touch operations. To improve the button’s function, the effect of some key properties consisting of dimension, boundary condition and load condition on electrical behavior of the piezoelectric diaphragm are evaluated by electromechanical coupling analysis in ANSYS. The finite element analysis (FEA) results indicate that the modification of these properties could enhance the diaphragm significantly. Based on the key properties’ different contributions to the improvement of the diaphragm’s electrical energy output, they are incorporated into the piezoelectric diaphragm’s redesign or the structural design of the piezo-based button. The comparison of the original structure and the optimal result shows that electrical energy stored in the diaphragm and the voltage output are increased by 1576% and 120%, respectively, and the volume of the piezoceramic is reduced to 33.6%. These results will be adopted to update the design of the self-powered button, thus enabling a large decrease of energy consumption and lifetime cost of the MMI. (paper)

  3. Development of an advanced human-machine interface for next generation nuclear power plants

    International Nuclear Information System (INIS)

    Chang, Soon Heung; Choi, Seong Soo; Park, Jin Kyun; Heo, Gyunyoung; Kim, Han Gon

    1999-01-01

    An advanced human-machine interface (HMI) has been developed to enhance the safety and availability of a nuclear power plant (NPP) by improving operational reliability. The key elements of the proposed HMI are the large display panels which present synopsis of plant status and the compact, computer-based work stations for monitoring, control and protection functions. The work station consists of four consoles such as a dynamic alarm console (DAC), a system information console (SIC), a computerized operating-procedure console (COC), and a safety system information console (SSIC). The DAC provides clean alarm pictures, in which information overlapping is excluded and alarm impacts are discriminated, for quick situation awareness. The SIC supports a normal operation by offering all necessary system information and control functions over non-safety systems. In addition, it is closely linked to the other consoles in order to automatically display related system information according to situations of the DAC and the COC. The COC aids operators with proper operating procedures during normal plant startup and shutdown or after a plant trip, and it also reduces their physical/mental burden through soft automation. The SSIC continuously displays safety system status and enables operators to control safety systems. The proposed HMI has been evaluated using the checklists that are extracted from various human factors guidelines. From the evaluation results, it can be concluded that the HMI is so designed as to address the human factors issues reasonably. After sufficient validation, the concept and the design features of the proposed HMI will be reflected in the design of the main control room of the Korean Next Generation Reactor (KNGR)

  4. A novel device for head gesture measurement system in combination with eye-controlled human machine interface

    Science.gov (United States)

    Lin, Chern-Sheng; Ho, Chien-Wa; Chang, Kai-Chieh; Hung, San-Shan; Shei, Hung-Jung; Yeh, Mau-Shiun

    2006-06-01

    This study describes the design and combination of an eye-controlled and a head-controlled human-machine interface system. This system is a highly effective human-machine interface, detecting head movement by changing positions and numbers of light sources on the head. When the users utilize the head-mounted display to browse a computer screen, the system will catch the images of the user's eyes with CCD cameras, which can also measure the angle and position of the light sources. In the eye-tracking system, the program in the computer will locate each center point of the pupils in the images, and record the information on moving traces and pupil diameters. In the head gesture measurement system, the user wears a double-source eyeglass frame, so the system catches images of the user's head by using a CCD camera in front of the user. The computer program will locate the center point of the head, transferring it to the screen coordinates, and then the user can control the cursor by head motions. We combine the eye-controlled and head-controlled human-machine interface system for the virtual reality applications.

  5. Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair.

    Science.gov (United States)

    Ron-Angevin, Ricardo; Velasco-Álvarez, Francisco; Fernández-Rodríguez, Álvaro; Díaz-Estrella, Antonio; Blanca-Mena, María José; Vizcaíno-Martín, Francisco Javier

    2017-05-30

    Certain diseases affect brain areas that control the movements of the patients' body, thereby limiting their autonomy and communication capacity. Research in the field of Brain-Computer Interfaces aims to provide patients with an alternative communication channel not based on muscular activity, but on the processing of brain signals. Through these systems, subjects can control external devices such as spellers to communicate, robotic prostheses to restore limb movements, or domotic systems. The present work focus on the non-muscular control of a robotic wheelchair. A proposal to control a wheelchair through a Brain-Computer Interface based on the discrimination of only two mental tasks is presented in this study. The wheelchair displacement is performed with discrete movements. The control signals used are sensorimotor rhythms modulated through a right-hand motor imagery task or mental idle state. The peculiarity of the control system is that it is based on a serial auditory interface that provides the user with four navigation commands. The use of two mental tasks to select commands may facilitate control and reduce error rates compared to other endogenous control systems for wheelchairs. Seventeen subjects initially participated in the study; nine of them completed the three sessions of the proposed protocol. After the first calibration session, seven subjects were discarded due to a low control of their electroencephalographic signals; nine out of ten subjects controlled a virtual wheelchair during the second session; these same nine subjects achieved a medium accuracy level above 0.83 on the real wheelchair control session. The results suggest that more extensive training with the proposed control system can be an effective and safe option that will allow the displacement of a wheelchair in a controlled environment for potential users suffering from some types of motor neuron diseases.

  6. Online LDA BASED brain-computer interface system to aid disabled people

    OpenAIRE

    Apdullah Yayık; Yakup Kutlu

    2017-01-01

    This paper aims to develop brain-computer interface system based on electroencephalography that can aid disabled people in daily life. The system relies on one of the most effective event-related potential wave, P300, which can be elicited by oddball paradigm. Developed application has a basic interaction tool that enables disabled people to convey their needs to other people selecting related objects. These objects pseudo-randomly flash in a visual interface on computer screen. The user must...

  7. A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm.

    Science.gov (United States)

    Lu, Siyuan; Qiu, Xin; Shi, Jianping; Li, Na; Lu, Zhi-Hai; Chen, Peng; Yang, Meng-Meng; Liu, Fang-Yuan; Jia, Wen-Juan; Zhang, Yudong

    2017-01-01

    It is beneficial to classify brain images as healthy or pathological automatically, because 3D brain images can generate so much information which is time consuming and tedious for manual analysis. Among various 3D brain imaging techniques, magnetic resonance (MR) imaging is the most suitable for brain, and it is now widely applied in hospitals, because it is helpful in the four ways of diagnosis, prognosis, pre-surgical, and postsurgical procedures. There are automatic detection methods; however they suffer from low accuracy. Therefore, we proposed a novel approach which employed 2D discrete wavelet transform (DWT), and calculated the entropies of the subbands as features. Then, a bat algorithm optimized extreme learning machine (BA-ELM) was trained to identify pathological brains from healthy controls. A 10x10-fold cross validation was performed to evaluate the out-of-sample performance. The method achieved a sensitivity of 99.04%, a specificity of 93.89%, and an overall accuracy of 98.33% over 132 MR brain images. The experimental results suggest that the proposed approach is accurate and robust in pathological brain detection. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  8. Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees

    Science.gov (United States)

    Verhoeven, Thibault; Schmid, Konstantin; Müller, Klaus-Robert; Tangermann, Michael; Kindermans, Pieter-Jan

    2017-01-01

    Objective Using traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in practice, none of them can provide theoretical guarantees. Our objective is to modify an event-related potential (ERP) paradigm to work in unison with the machine learning decoder, and thus to achieve a reliable unsupervised calibrationless decoding with a guarantee to recover the true class means. Method We introduce learning from label proportions (LLP) to the BCI community as a new unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The LLP estimates the mean target and non-target responses based on known proportions of these two classes in different groups of the data. We present a visual ERP speller to meet the requirements of LLP. For evaluation, we ran simulations on artificially created data sets and conducted an online BCI study with 13 subjects performing a copy-spelling task. Results Theoretical considerations show that LLP is guaranteed to minimize the loss function similar to a corresponding supervised classifier. LLP performed well in simulations and in the online application, where 84.5% of characters were spelled correctly on average without prior calibration. Significance The continuously adapting LLP classifier is the first unsupervised decoder for ERP BCIs guaranteed to find the optimal decoder. This makes it an ideal solution to avoid tedious calibration sessions. Additionally, LLP works on complementary principles compared to existing unsupervised methods, opening the door for their further enhancement when combined with LLP. PMID:28407016

  9. A dry EEG-system for scientific research and brain-computer interfaces

    Directory of Open Access Journals (Sweden)

    Thorsten Oliver Zander

    2011-05-01

    Full Text Available Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG still forms the method of choice in a wide variety of clinical and research applications. In the context of Brain-Computer Interfacing (BCI, EEG recently has become a tool to enhance Human-Machine Interaction (HMI. EEG could be employed in a wider range of environments, especially for the use of BCI systems in a clinical context or at the homes of patients. However, the application of EEG in these contexts is impeded by the cumbersome preparation of the electrodes with conductive gel that is necessary to lower the impedance between electrodes and scalp. Dry electrodes could provide a solution to this barrier and allow for EEG applications outside the laboratory. In addition, dry electrodes may reduce the time needed for neurological exams in clinical practice. This study evaluates a prototype of a three-channel dry electrode EEG system, comparing it to state-of-the-art conventional EEG electrodes. Two experimental paradigms were used: first, Event-Related Potentials (ERP were investigated with a variant of the oddball paradigm. Second, features of the frequency domain were compared by a paradigm inducing occipital alpha. Furthermore, both paradigms were used to evaluate BCI classification accuracies of both EEG systems. Amplitude and temporal structure of ERPs as well as features in the frequency domain did not differ significantly between the EEG systems. BCI classification accuracies were equally high in both systems when the frequency domain was considered. With respect to the oddball classification accuracy, there were slight differences between the wet and dry electrode systems. We conclude that the tested dry electrodes were capable to detect EEG signals with good quality and that these signals can be used for research or BCI applications. Easy to handle electrodes may help to foster the use of EEG among a wider range of potential users.

  10. Summary of the snowmass working group on machine-detector interface

    International Nuclear Information System (INIS)

    Bharadwaj, V.; Colestock, P.; Cooper, J.; Goderre, G.; Holt, J.

    1993-01-01

    From the detector point of view, what experimenters need is an outline of the EXPECTED machine parameters tempered with some indication of the POSSIBLE machine parameters. Given guidance from accelerator physicists on the machine, experimenters may get the germ of an idea of how to exploit a particular machine property. Similarly, given some indication of what is important to the experimenters, accelerator physicists may have ideas of how to modify the machine appropriately. The authors discuss a list of machine parameters from the perspective of experimentalists. These include: luminosity; number of bunches/bunch spacing; beam energy; luminosity lifetime; β * /longitudinal emittance; transverse emittance. They also include a summary of tevatron performance - past, present, and projected

  11. Bottlenecks to Clinical Translation of Direct Brain-Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Mijail Demian Serruya

    2014-12-01

    Full Text Available Despite several decades of research into novel brain-implantable devices to treat a range of diseases, only two- cochlear implants for sensorineural hearing loss and deep brain stimulation for movement disorders- have yielded any appreciable clinical benefit. Obstacles to translation include technical factors (e.g., signal loss due to gliosis or micromotion, lack of awareness of current clinical options for patients that the new therapy must outperform, traversing between federal and corporate funding needed to support clinical trials, and insufficient management expertise. This commentary reviews these obstacles preventing the translation of promising new neurotechnologies into clinical application and suggests some principles that interdisciplinary teams in academia and industry could adopt to enhance their chances of success.

  12. Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury

    OpenAIRE

    Rupp, Rüdiger

    2014-01-01

    Brain computer interfaces (BCIs) are devices that measure brain activities and translate them into control signals used for a variety of applications. Among them are systems for communication, environmental control, neuroprostheses, exoskeletons, or restorative therapies. Over the last years the technology of BCIs has reached a level of matureness allowing them to be used not only in research experiments supervised by scientists, but also in clinical routine with patients with neurological im...

  13. A Hybrid Hierarchical Approach for Brain Tissue Segmentation by Combining Brain Atlas and Least Square Support Vector Machine

    Science.gov (United States)

    Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

    2013-01-01

    In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth. PMID:24696800

  14. A brain-computer interface for potential non-verbal facial communication based on EEG signals related to specific emotions.

    Science.gov (United States)

    Kashihara, Koji

    2014-01-01

    Unlike assistive technology for verbal communication, the brain-machine or brain-computer interface (BMI/BCI) has not been established as a non-verbal communication tool for amyotrophic lateral sclerosis (ALS) patients. Face-to-face communication enables access to rich emotional information, but individuals suffering from neurological disorders, such as ALS and autism, may not express their emotions or communicate their negative feelings. Although emotions may be inferred by looking at facial expressions, emotional prediction for neutral faces necessitates advanced judgment. The process that underlies brain neuronal responses to neutral faces and causes emotional changes remains unknown. To address this problem, therefore, this study attempted to decode conditioned emotional reactions to neutral face stimuli. This direction was motivated by the assumption that if electroencephalogram (EEG) signals can be used to detect patients' emotional responses to specific inexpressive faces, the results could be incorporated into the design and development of BMI/BCI-based non-verbal communication tools. To these ends, this study investigated how a neutral face associated with a negative emotion modulates rapid central responses in face processing and then identified cortical activities. The conditioned neutral face-triggered event-related potentials that originated from the posterior temporal lobe statistically significantly changed during late face processing (600-700 ms) after stimulus, rather than in early face processing activities, such as P1 and N170 responses. Source localization revealed that the conditioned neutral faces increased activity in the right fusiform gyrus (FG). This study also developed an efficient method for detecting implicit negative emotional responses to specific faces by using EEG signals. A classification method based on a support vector machine enables the easy classification of neutral faces that trigger specific individual emotions. In

  15. A covert attention P300-based brain-computer interface: Geospell.

    Science.gov (United States)

    Aloise, Fabio; Aricò, Pietro; Schettini, Francesca; Riccio, Angela; Salinari, Serenella; Mattia, Donatella; Babiloni, Fabio; Cincotti, Febo

    2012-01-01

    The Farwell and Donchin P300 speller interface is one of the most widely used brain-computer interface (BCI) paradigms for writing text. Recent studies have shown that the recognition accuracy of the P300 speller decreases significantly when eye movement is impaired. This report introduces the GeoSpell interface (Geometric Speller), which implements a stimulation framework for a P300-based BCI that has been optimised for operation in covert visual attention. We compared the Geospell with the P300 speller interface under overt attention conditions with regard to effectiveness, efficiency and user satisfaction. Ten healthy subjects participated in the study. The performance of the GeoSpell interface in covert attention was comparable with that of the P300 speller in overt attention. As expected, the effectiveness of the spelling decreased with the new interface in covert attention. The NASA task load index (TLX) for workload assessment did not differ significantly between the two modalities. This study introduces and evaluates a gaze-independent, P300-based brain-computer interface, the efficacy and user satisfaction of which were comparable with those off the classical P300 speller. Despite a decrease in effectiveness due to the use of covert attention, the performance of the GeoSpell far exceeded the threshold of accuracy with regard to effective spelling.

  16. Integrated Multi-Scale Data Analytics and Machine Learning for the Distribution Grid and Building-to-Grid Interface

    Energy Technology Data Exchange (ETDEWEB)

    Stewart, Emma M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Hendrix, Val [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Deka, Deepjyoti [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-03-16

    This white paper introduces the application of advanced data analytics to the modernized grid. In particular, we consider the field of machine learning and where it is both useful, and not useful, for the particular field of the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper we consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid and the building-to-grid interface. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. Machine learning is a subfield of computer science that studies and constructs algorithms that can learn from data and make predictions and improve forecasts. Incorporation of machine learning in grid monitoring and analysis tools may have the potential to solve data and operational challenges that result from increasing penetration of distributed and behind-the-meter energy resources. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors – such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals – such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis

  17. User-centered design in brain-computer interfaces-a case study.

    Science.gov (United States)

    Schreuder, Martijn; Riccio, Angela; Risetti, Monica; Dähne, Sven; Ramsay, Andrew; Williamson, John; Mattia, Donatella; Tangermann, Michael

    2013-10-01

    The array of available brain-computer interface (BCI) paradigms has continued to grow, and so has the corresponding set of machine learning methods which are at the core of BCI systems. The latter have evolved to provide more robust data analysis solutions, and as a consequence the proportion of healthy BCI users who can use a BCI successfully is growing. With this development the chances have increased that the needs and abilities of specific patients, the end-users, can be covered by an existing BCI approach. However, most end-users who have experienced the use of a BCI system at all have encountered a single paradigm only. This paradigm is typically the one that is being tested in the study that the end-user happens to be enrolled in, along with other end-users. Though this corresponds to the preferred study arrangement for basic research, it does not ensure that the end-user experiences a working BCI. In this study, a different approach was taken; that of a user-centered design. It is the prevailing process in traditional assistive technology. Given an individual user with a particular clinical profile, several available BCI approaches are tested and - if necessary - adapted to him/her until a suitable BCI system is found. Described is the case of a 48-year-old woman who suffered from an ischemic brain stem stroke, leading to a severe motor- and communication deficit. She was enrolled in studies with two different BCI systems before a suitable system was found. The first was an auditory event-related potential (ERP) paradigm and the second a visual ERP paradigm, both of which are established in literature. The auditory paradigm did not work successfully, despite favorable preconditions. The visual paradigm worked flawlessly, as found over several sessions. This discrepancy in performance can possibly be explained by the user's clinical deficit in several key neuropsychological indicators, such as attention and working memory. While the auditory paradigm relies

  18. Human machine interface to manually drive rhombic like vehicles such as transport casks in ITER

    International Nuclear Information System (INIS)

    Lopes, Pedro; Vale, Alberto; Ventura, Rodrigo

    2015-01-01

    The Cask and Plug Remote Handling System (CPRHS) and the respective Cask Transfer System (CTS) are designed to transport activated components between the reactor and the hot cell buildings of ITER during maintenance operations. In nominal operation, the CPRHS/CTS shall operate autonomously under human supervision. However, in some unexpected situations, the automatic mode must be overridden and the vehicle must be remotely guided by a human operator due to the harsh conditions of the environment. The CPRHS/CTS is a rhombic-like vehicle with two independent steerable and drivable wheels along its longitudinal axis, giving it omni-directional capabilities. During manual guidance, the human operator has to deal with four degrees of freedom, namely the orientations and speeds of two wheels. This work proposes a Human Machine Interface (HMI) to manage the degrees of freedom and to remotely guide the CPRHS/CTS in ITER taking the most advantages of rhombic like capabilities. Previous work was done to drive each wheel independently, i.e., control the orientation and speed of each wheel independently. The results have shown that the proposed solution is inefficient. The attention of the human operator becomes focused in a single wheel. In addition, the proposed solution cannot assure that the commands accomplish the physical constrains of the vehicle, resulting in slippage or even in clashes. This work proposes a solution that consists in the control of the vehicle looking at the position of its center of mass and its heading in the world frame. The solution is implemented using a rotational disk to control the vehicle heading and a common analogue joystick to control the vector speed of the center of the mass of the vehicle. The number of degrees of freedom reduces to three, i.e., two angles (vehicle heading and the orientation of the vector speed) and a scalar (the magnitude of the speed vector). This is possible using a kinematic model based on the vehicle Instantaneous

  19. Human machine interface to manually drive rhombic like vehicles such as transport casks in ITER

    Energy Technology Data Exchange (ETDEWEB)

    Lopes, Pedro; Vale, Alberto [Instituto de Plasmas e Fusao Nuclear, Instituto Superior Tecnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); Ventura, Rodrigo [Institute for Systems and Robotics, Instituto Superior Tecnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal)

    2015-07-01

    The Cask and Plug Remote Handling System (CPRHS) and the respective Cask Transfer System (CTS) are designed to transport activated components between the reactor and the hot cell buildings of ITER during maintenance operations. In nominal operation, the CPRHS/CTS shall operate autonomously under human supervision. However, in some unexpected situations, the automatic mode must be overridden and the vehicle must be remotely guided by a human operator due to the harsh conditions of the environment. The CPRHS/CTS is a rhombic-like vehicle with two independent steerable and drivable wheels along its longitudinal axis, giving it omni-directional capabilities. During manual guidance, the human operator has to deal with four degrees of freedom, namely the orientations and speeds of two wheels. This work proposes a Human Machine Interface (HMI) to manage the degrees of freedom and to remotely guide the CPRHS/CTS in ITER taking the most advantages of rhombic like capabilities. Previous work was done to drive each wheel independently, i.e., control the orientation and speed of each wheel independently. The results have shown that the proposed solution is inefficient. The attention of the human operator becomes focused in a single wheel. In addition, the proposed solution cannot assure that the commands accomplish the physical constrains of the vehicle, resulting in slippage or even in clashes. This work proposes a solution that consists in the control of the vehicle looking at the position of its center of mass and its heading in the world frame. The solution is implemented using a rotational disk to control the vehicle heading and a common analogue joystick to control the vector speed of the center of the mass of the vehicle. The number of degrees of freedom reduces to three, i.e., two angles (vehicle heading and the orientation of the vector speed) and a scalar (the magnitude of the speed vector). This is possible using a kinematic model based on the vehicle Instantaneous

  20. Ethical Issues in Brain-Computer Interface Research, Development, and Dissemination

    NARCIS (Netherlands)

    Vlek, Rutger; Steines, David; Szibbo, Dyana; Kübler, Andrea; Schneider, Mary-Jane; Haselager, Pim; Nijboer, Femke

    The steadily growing field of brain-computer interfacing (BCI) may develop useful technologies, with a potential impact not only on individuals, but also on society as a whole. At the same time, the development of BCI presents significant ethical and legal challenges. In a workshop during the 4th

  1. Myndplay: Measuring Attention Regulation with Single Dry Electrode Brain Computer Interface

    NARCIS (Netherlands)

    van der Wal, C.N.; Irrmischer, M.; Guo, Y.; Friston, K.; Faisal, A.; Hill, S.; Peng, H.

    2015-01-01

    Future applications for the detection of attention can be helped by the development and validation of single electrode brain computer interfaces that are small and user-friendly. The two objectives of this study were: to (1) understand the correlates of attention regulation as detected with the

  2. Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

    DEFF Research Database (Denmark)

    Bender, Thomas; Kjaer, Troels W.; Thomsen, Carsten E.

    2013-01-01

    This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters...

  3. Performance of Brain-computer Interfacing based on tactile selective sensation and motor imagery

    DEFF Research Database (Denmark)

    Yao, Lin; Sheng, Xinjun; Mrachacz-Kersting, Natalie

    2018-01-01

    We proposed a multi-class tactile brain-computer interface that utilizes stimulus-induced oscillatory dynamics. It was hypothesized that somatosensory attention can modulate tactile induced oscillation changes, which can decode different sensation attention tasks. Subjects performed four tactile...

  4. Bigger data for big data: from Twitter to brain-computer interfaces.

    Science.gov (United States)

    Roesch, Etienne B; Stahl, Frederic; Gaber, Mohamed Medhat

    2014-02-01

    We are sympathetic with Bentley et al.'s attempt to encompass the wisdom of crowds in a generative model, but posit that a successful attempt at using big data will include more sensitive measurements, more varied sources of information, and will also build from the indirect information available through technology, from ancillary technical features to data from brain-computer interfaces.

  5. The Asilomar Survey: Stakeholders' Opinions on Ethical Issues Related to Brain-Computer Interfacing

    NARCIS (Netherlands)

    Nijboer, Femke; Clausen, Jens; Allison, Brendan Z.; Haselager, Pim

    2013-01-01

    Brain-Computer Interface (BCI) research and (future) applications raise important ethical issues that need to be addressed to promote societal acceptance and adequate policies. Here we report on a survey we conducted among 145 BCI researchers at the 4th International BCI conference, which took place

  6. The Asilomar Survey: Stakeholders’ Opinions on Ethical Issues Related to Brain-Computer Interfacing

    NARCIS (Netherlands)

    Nijboer, F.; Clausen, J.; Allison, B.Z.; Haselager, W.F.G.

    2013-01-01

    Brain-Computer Interface (BCI) research and (future) applications raise important ethical issues that need to be addressed to promote societal acceptance and adequate policies. Here we report on a survey we conducted among 145 BCI researchers at the 4th International BCI conference, which took place

  7. Wyrm: A Brain-Computer Interface Toolbox in Python.

    Science.gov (United States)

    Venthur, Bastian; Dähne, Sven; Höhne, Johannes; Heller, Hendrik; Blankertz, Benjamin

    2015-10-01

    In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machine learning packages like scikit-learn or packages for data analysis like Pandas are building on top of it. In this paper we present Wyrm ( https://github.com/bbci/wyrm ), an open source BCI toolbox in Python. Wyrm is applicable to a broad range of neuroscientific problems. It can be used as a toolbox for analysis and visualization of neurophysiological data and in real-time settings, like an online BCI application. In order to prevent software defects, Wyrm makes extensive use of unit testing. We will explain the key aspects of Wyrm's software architecture and design decisions for its data structure, and demonstrate and validate the use of our toolbox by presenting our approach to the classification tasks of two different data sets from the BCI Competition III. Furthermore, we will give a brief analysis of the data sets using our toolbox, and demonstrate how we implemented an online experiment using Wyrm. With Wyrm we add the final piece to our ongoing effort to provide a complete, free and open source BCI system in Python.

  8. Turbo-Satori: a neurofeedback and brain-computer interface toolbox for real-time functional near-infrared spectroscopy.

    Science.gov (United States)

    Lührs, Michael; Goebel, Rainer

    2017-10-01

    Turbo-Satori is a neurofeedback and brain-computer interface (BCI) toolbox for real-time functional near-infrared spectroscopy (fNIRS). It incorporates multiple pipelines from real-time preprocessing and analysis to neurofeedback and BCI applications. The toolbox is designed with a focus in usability, enabling a fast setup and execution of real-time experiments. Turbo-Satori uses an incremental recursive least-squares procedure for real-time general linear model calculation and support vector machine classifiers for advanced BCI applications. It communicates directly with common NIRx fNIRS hardware and was tested extensively ensuring that the calculations can be performed in real time without a significant change in calculation times for all sampling intervals during ongoing experiments of up to 6 h of recording. Enabling immediate access to advanced processing features also allows the use of this toolbox for students and nonexperts in the field of fNIRS data acquisition and processing. Flexible network interfaces allow third party stimulus applications to access the processed data and calculated statistics in real time so that this information can be easily incorporated in neurofeedback or BCI presentations.

  9. Brain-computer interface based on generation of visual images.

    Directory of Open Access Journals (Sweden)

    Pavel Bobrov

    Full Text Available This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP classifier.

  10. Human machine interface based on labview for vacuum system operation of cyclotron proton DECY-13 MeV

    International Nuclear Information System (INIS)

    Fajar Sidik Permana; Saminto; Kurnia Wibowo; Vika Arwida Fanita Sari

    2016-01-01

    Center of Accelerator Science and Technology (CAST), BATAN is designing DECY-13 MeV Proton Cyclotron. So far, this operation system has been conducted conventionally. In this research, an Human Machine Interface system has been successfully built for simplifying operation and monitoring pressure inside vacuum chamber of cyclotron DECY-13 MeV. HMI system is built with LabVIEW software and integrated with Programmable Logic Controller FX-2424 series and NI cRIO (NI-9025 and NI-9870) module. HMI system consist of turning on/of pumps (rotary and diffusion), opening/ closing valve automatically, and retrieving of data from sensor in real time. (author)

  11. Function analysis of nuclear power plants for developing of man-machine interface system for Korean next generation reactor

    Energy Technology Data Exchange (ETDEWEB)

    Goo, In Soo; Kim, Jang Yyul; Kim, Jung Soo; Kim, Chang Hoi; Na, Nan Joo; Park, Keun Ok; Park, Won Man; Park, Jae Chang; Suh, Sang Moon; Oh, In Suk; Lee, Dong Young; Lee, Yong Hee; Cha, Kyung Ho; Chun, Se Woo; Hur, Sup [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of); Jung, Kyung Hoon [Korea Electric Power Co., Seoul (Korea, Republic of); Park, Yeon Sik; Lee, Bum Joo [Korea Power Engineering Company, Inc., Seoul (Korea, Republic of)

    1995-02-01

    In this study, we developed the methodology and implementation plant of function analysis and performed function analysis, which is one of the major activities for the development of Man-Machine Interface System of the KNGR. Identifying the functions of existing plants followed by structuring the functions, we established functions structured at the conceptual and middle levels. This structure was further checked if it would satisfy regulatory requirements and developed to include the aspects of plant performance and other plant features to emphasize its practicality for the application to the design. (Author) 13 refs., 8 figs., 4 tabs.

  12. Function analysis of nuclear power plants for developing of man-machine interface system for Korean next generation reactor

    International Nuclear Information System (INIS)

    Goo, In Soo; Kim, Jang Yyul; Kim, Jung Soo; Kim, Chang Hoi; Na, Nan Joo; Park, Keun Ok; Park, Won Man; Park, Jae Chang; Suh, Sang Moon; Oh, In Suk; Lee, Dong Young; Lee, Yong Hee; Cha, Kyung Ho; Chun, Se Woo; Hur, Sup; Jung, Kyung Hoon; Park, Yeon Sik; Lee, Bum Joo

    1995-02-01

    In this study, we developed the methodology and implementation plant of function analysis and performed function analysis, which is one of the major activities for the development of Man-Machine Interface System of the KNGR. Identifying the functions of existing plants followed by structuring the functions, we established functions structured at the conceptual and middle levels. This structure was further checked if it would satisfy regulatory requirements and developed to include the aspects of plant performance and other plant features to emphasize its practicality for the application to the design. (Author) 13 refs., 8 figs., 4 tabs

  13. Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

    Directory of Open Access Journals (Sweden)

    R. Rajesh Sharma

    2015-01-01

    algorithm (RGSA. Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002. The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

  14. A study on dynamic evaluation methods for human-machine interfaces in advanced control rooms

    International Nuclear Information System (INIS)

    Park, Jin Kyun

    1998-02-01

    Extensive efforts have been performed to reveal factors that largely affect to the safety of nuclear power plants (NPPs). Among them, human factors were known as a dominant cause of a severe accident, such as Three Mile Island and Chernobyl accidents. Thus a lot of efforts to resolve human factors related problems have been spent, and one of these efforts is an advanced control room (ACR) design to enhance human performance and the safety of NPPs. There are two important trends in the design of ACRs. The first one is increasing automation level, and the second one is the development of computer based compact workstations for control room operations including intelligent operator aid systems. However, several problems have been reported when another factors are not properly incorporated into the design of ACRs. Among them, one of the most important factors that significantly affect to operator performance is the design of human machine interfaces (HMIs). Thus, HMI evaluation should be emphasized to ensure appropriateness of HMI designs and the safety of NPPs. In general, two kinds of evaluations have been frequently used to assess appropriateness of the proposed HMI design. The one is the static evaluation and the other is the dynamic evaluation. Here, the static evaluation is the one based on guidelines that are extracted from various researches on HMI designs. And the dynamic evaluation generally attempts to evaluate and predict human performance through a model that can describe cognitive behaviors of human or interactions between HMIs and human. However, the static evaluation seems to be inappropriate because it can't properly capture context of task environment that strongly affects to human performance. In addition, in case of dynamic evaluations, development of a model that can sufficiently describe interactions or cognitive behaviors of human operators is very arduous and laborious. To overcome these problems, dynamic evaluation methods that can

  15. Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR

    Directory of Open Access Journals (Sweden)

    Eva Maria Hammer

    2014-08-01

    Full Text Available Modulation of sensorimotor rhythms (SMR was suggested as a control signal for brain-computer interfaces (BCI. Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80-100% performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCI performance from psychological variables that were assessed prior to the BCI sessions and BCI control was supported with machine-learning techniques. We described two significant psychological predictors, namely the visuo-motor coordination ability and the ability to concentrate on the task. The purpose of the current study was to replicate these results thereby validating these predictors within a neurofeedback based SMR-BCI that involved no machine learning. Thirty-three healthy BCI novices participated in a calibration session and three further neurofeedback training sessions. Two variables were related with mean SMR-BCI performance: (1 A measure for the accuracy of fine motor skills, i.e. a trade for a person’s visuo-motor control ability and (2 subject’s attentional impulsivity. In a linear regression they accounted for almost 20% in variance of SMR-BCI performance, but predictor (1 failed significance. Nevertheless, on the basis of our prior regression model for sensorimotor control ability we could predict current SMR-BCI performance with an average prediction error of M = 12.07%. In more than 50% of the participants, the prediction error was smaller than 10%. Hence, psychological variables played a moderate role in predicting SMR-BCI performance in a neurofeedback approach that involved no machine learning. Future studies are needed to further consolidate (or reject the present predictors.

  16. A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation.

    Science.gov (United States)

    Kumar, Deepesh; Das, Abhijit; Lahiri, Uttama; Dutta, Anirban

    2016-04-12

    A stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow to brain thereby preventing delivery of oxygen and nutrients. About half of the stroke survivors are left with some degree of disability. Innovative methodologies for restorative neurorehabilitation are urgently required to reduce long-term disability. The ability of the nervous system to reorganize its structure, function and connections as a response to intrinsic or extrinsic stimuli is called neuroplasticity. Neuroplasticity is involved in post-stroke functional disturbances, but also in rehabilitation. Beneficial neuroplastic changes may be facilitated with non-invasive electrotherapy, such as neuromuscular electrical stimulation (NMES) and sensory electrical stimulation (SES). NMES involves coordinated electrical stimulation of motor nerves and muscles to activate them with continuous short pulses of electrical current while SES involves stimulation of sensory nerves with electrical current resulting in sensations that vary from barely perceivable to highly unpleasant. Here, active cortical participation in rehabilitation procedures may be facilitated by driving the non-invasive electrotherapy with biosignals (electromyogram (EMG), electroencephalogram (EEG), electrooculogram (EOG)) that represent simultaneous active perception and volitional effort. To achieve this in a resource-poor setting, e.g., in low- and middle-income countries, we present a low-cost human-machine-interface (HMI) by leveraging recent advances in off-the-shelf video game sensor technology. In this paper, we discuss the open-source software interface that integrates low-cost off-the-shelf sensors for visual-auditory biofeedback with non-invasive electrotherapy to assist postural control during balance rehabilitation. We demonstrate the proof-of-concept on healthy volunteers.

  17. Evolution of the Brain Computing Interface (BCI and Proposed Electroencephalography (EEG Signals Based Authentication Model

    Directory of Open Access Journals (Sweden)

    Ramzan Qaseem

    2018-01-01

    Full Text Available With current advancements in the field of Brain Computer interface it is required to study how it will affect the other technologies currently in use. In this paper, the authors motivate the need of Brain Computing Interface in the era of IoT (Internet of Things, and analyze how BCI in the presence of IoT could have serious privacy breach if not protected by new kind of more secure protocols. Security breach and hacking has been around for a long time but now we are sensitive towards data as our lives depend on it. When everything is interconnected through IoT and considering that we control all interconnected things by means of our brain using BCI (Brain Computer Interface, the meaning of security breach becomes much more sensitive than in the past. This paper describes the old security methods being used for authentication and how they can be compromised. Considering the sensitivity of data in the era of IoT, a new form of authentication is required, which should incorporate BCI rather than usual authentication techniques.

  18. Support vector machine classification and characterization of age-related reorganization of functional brain networks.

    Science.gov (United States)

    Meier, Timothy B; Desphande, Alok S; Vergun, Svyatoslav; Nair, Veena A; Song, Jie; Biswal, Bharat B; Meyerand, Mary E; Birn, Rasmus M; Prabhakaran, Vivek

    2012-03-01

    Most of what is known about the reorganization of functional brain networks that accompanies normal aging is based on neuroimaging studies in which participants perform specific tasks. In these studies, reorganization is defined by the differences in task activation between young and old adults. However, task activation differences could be the result of differences in task performance, strategy, or motivation, and not necessarily reflect reorganization. Resting-state fMRI provides a method of investigating functional brain networks without such confounds. Here, a support vector machine (SVM) classifier was used in an attempt to differentiate older adults from younger adults based on their resting-state functional connectivity. In addition, the information used by the SVM was investigated to see what functional connections best differentiated younger adult brains from older adult brains. Three separate resting-state scans from 26 younger adults (18-35 yrs) and 26 older adults (55-85) were obtained from the International Consortium for Brain Mapping (ICBM) dataset made publically available in the 1000 Functional Connectomes project www.nitrc.org/projects/fcon_1000. 100 seed-regions from four functional networks with 5mm(3) radius were defined based on a recent study using machine learning classifiers on adolescent brains. Time-series for every seed-region were averaged and three matrices of z-transformed correlation coefficients were created for each subject corresponding to each individual's three resting-state scans. SVM was then applied using leave-one-out cross-validation. The SVM classifier was 84% accurate in classifying older and younger adult brains. The majority of the connections used by the classifier to distinguish subjects by age came from seed-regions belonging to the sensorimotor and cingulo-opercular networks. These results suggest that age-related decreases in positive correlations within the cingulo-opercular and default networks, and decreases in

  19. Human Machine Interface and Usability Issues: Exploring a Preliminary Mission Management System Evaluation Methodology

    National Research Council Canada - National Science Library

    Kardos, Monique

    2001-01-01

    ...) concept demonstrator test during June 2000. The questionnaire was designed to examine usability and interface issues relating to the design of the systems or tools, and to provide suggestions for future iterations of the system...

  20. [A new human machine interface in neurosurgery: The Leap Motion(®). Technical note regarding a new touchless interface].

    Science.gov (United States)

    Di Tommaso, L; Aubry, S; Godard, J; Katranji, H; Pauchot, J

    2016-06-01

    Currently, cross-sectional imaging viewing is used in routine practice whereas the surgical procedure requires physical contact with an interface (mouse or touch-sensitive screen). This type of contact results in a risk of lack of aseptic control and causes loss of time. The recent appearance of devices such as the Leap Motion(®) (Leap Motion society, San Francisco, USA) a sensor which enables to interact with the computer without any physical contact is of major interest in the field of surgery. However, its configuration and ergonomics produce key challenges in order to adapt to the practitioner's requirements, the imaging software as well as the surgical environment. This article aims to suggest an easy configuration of the Leap Motion(®) in neurosurgery on a PC for an optimized utilization with Carestream(®) Vue PACS v11.3.4 (Carestream Health, Inc., Rochester, USA) using a plug-in (to download at: https://drive.google.com/?usp=chrome_app#folders/0B_F4eBeBQc3ybElEeEhqME5DQkU) and a video tutorial (https://www.youtube.com/watch?v=yVPTgxg-SIk). Copyright © 2016 Elsevier Masson SAS. All rights reserved.