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Sample records for brain-machine interface instructed

  1. A brain-machine interface instructed by direct intracortical microstimulation

    Directory of Open Access Journals (Sweden)

    Joseph E O'Doherty

    2009-09-01

    Full Text Available Brain-machine interfaces (BMIs establish direct communications between the brain and artificial actuators. As such, they hold considerable promise for restoring mobility and communication in patients suffering from severe body paralysis. To achieve this end, future BMIs must also provide a means for delivering sensory signals from the actuators back to the brain. Prosthetic sensation is needed so that neuroprostheses can be better perceived and controlled. Here we show that a direct intracortical input can be added to a BMI to instruct rhesus monkeys in choosing the direction of reaching movements generated by the BMI. Somatosensory instructions were provided to two monkeys operating the BMI using either: (a vibrotactile stimulation of the monkey’s hands or (b multi-channel intracortical microstimulation (ICMS delivered to the primary somatosensory cortex (S1 in one monkey and posterior parietal cortex (PP in the other. Stimulus delivery was contingent on the position of the computer cursor: the monkey placed it in the center of the screen to receive machine-brain recursive input. After two weeks of training, the same level of proficiency in utilizing somatosensory information was achieved with ICMS of S1 as with the stimulus delivered to the hand skin. ICMS of PP was not effective. These results indicate that direct, bi-directional communication between the brain and neuroprosthetic devices can be achieved through the combination of chronic multi-electrode recording and microstimulation of S1. We propose that in the future, bidirectional BMIs incorporating ICMS may become an effective paradigm for sensorizing neuroprosthetic devices.

  2. A Brain-Machine Interface Instructed by Direct Intracortical Microstimulation

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    O'Doherty, Joseph E.; Lebedev, Mikhail A.; Hanson, Timothy L.; Fitzsimmons, Nathan A.; Nicolelis, Miguel A. L.

    2009-01-01

    Brain–machine interfaces (BMIs) establish direct communication between the brain and artificial actuators. As such, they hold considerable promise for restoring mobility and communication in patients suffering from severe body paralysis. To achieve this end, future BMIs must also provide a means for delivering sensory signals from the actuators back to the brain. Prosthetic sensation is needed so that neuroprostheses can be better perceived and controlled. Here we show that a direct intracortical input can be added to a BMI to instruct rhesus monkeys in choosing the direction of reaching movements generated by the BMI. Somatosensory instructions were provided to two monkeys operating the BMI using either: (a) vibrotactile stimulation of the monkey's hands or (b) multi-channel intracortical microstimulation (ICMS) delivered to the primary somatosensory cortex (S1) in one monkey and posterior parietal cortex (PP) in the other. Stimulus delivery was contingent on the position of the computer cursor: the monkey placed it in the center of the screen to receive machine–brain recursive input. After 2 weeks of training, the same level of proficiency in utilizing somatosensory information was achieved with ICMS of S1 as with the stimulus delivered to the hand skin. ICMS of PP was not effective. These results indicate that direct, bi-directional communication between the brain and neuroprosthetic devices can be achieved through the combination of chronic multi-electrode recording and microstimulation of S1. We propose that in the future, bidirectional BMIs incorporating ICMS may become an effective paradigm for sensorizing neuroprosthetic devices. PMID:19750199

  3. Optimal Achievable Encoding for Brain Machine Interface

    Science.gov (United States)

    2017-12-22

    Brain - Machine Interface Eduardo Chichilnisky Leland Stanford Junior...Oct 2016 – 30 Sep 2017 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Optimal Achievable Encoding for Brain - Machine Interface 5b...Stanford Artificial Retina 15. SUBJECT TERMS Artificial retina, Retinal prosthesis, Brain - machine interface , Brain -computer interface ,

  4. Unsupervised adaptation of brain machine interface decoders

    Directory of Open Access Journals (Sweden)

    Tayfun eGürel

    2012-11-01

    Full Text Available The performance of neural decoders can degrade over time due to nonstationarities in the relationship between neuronal activity and behavior. In this case, brain-machine interfaces (BMI require adaptation of their decoders to maintain high performance across time. One way to achieve this is by use of periodical calibration phases, during which the BMI system (or an external human demonstrator instructs the user to perform certain movements or behaviors. This approach has two disadvantages: (i calibration phases interrupt the autonomous operation of the BMI and (ii between two calibration phases the BMI performance might not be stable but continuously decrease. A better alternative would be that the BMI decoder is able to continuously adapt in an unsupervised manner during autonomous BMI operation, i.e. without knowing the movement intentions of the user. In the present article, we present an efficient method for such unsupervised training of BMI systems for continuous movement control. The proposed method utilizes a cost function derived from neuronal recordings, which guides a learning algorithm to evaluate the decoding parameters. We verify the performance of our adaptive method by simulating a BMI user with an optimal feedback control model and its interaction with our adaptive BMI decoder. The simulation results show that the cost function and the algorithm yield fast and precise trajectories towards targets at random orientations on a 2-dimensional computer screen. For initially unknown and nonstationary tuning parameters, our unsupervised method is still able to generate precise trajectories and to keep its performance stable in the long term. The algorithm can optionally work also with neuronal error signals instead or in conjunction with the proposed unsupervised adaptation.

  5. Brain-machine interfaces beyond neuroprosthetics.

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    Moxon, Karen A; Foffani, Guglielmo

    2015-04-08

    The field of invasive brain-machine interfaces (BMIs) is typically associated with neuroprosthetic applications aiming to recover loss of motor function. However, BMIs also represent a powerful tool to address fundamental questions in neuroscience. The observed subjects of BMI experiments can also be considered as indirect observers of their own neurophysiological activity, and the relationship between observed neurons and (artificial) behavior can be genuinely causal rather than indirectly correlative. These two characteristics defy the classical object-observer duality, making BMIs particularly appealing for investigating how information is encoded and decoded by neural circuits in real time, how this coding changes with physiological learning and plasticity, and how it is altered in pathological conditions. Within neuroengineering, BMI is like a tree that opens its branches into many traditional engineering fields, but also extends deep roots into basic neuroscience beyond neuroprosthetics. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. Brain-Machine Interface Control Algorithms.

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    Shanechi, Maryam M

    2016-12-14

    Motor brain-machine interfaces (BMI) allow subjects to control external devices by modulating their neural activity. BMIs record the neural activity, use a mathematical algorithm to estimate the subject's intended movement, actuate an external device, and provide visual feedback of the generated movement to the subject. A critical component of a BMI system is the control algorithm, termed decoder. Significant progress has been made in the design of BMI decoders in recent years resulting in proficient control in non-human primates and humans. In this review article, we discuss the decoding algorithms developed in the BMI field, with particular focus on recent designs that are informed by closed-loop control ideas. A motor BMI can be modeled as a closed-loop control system, where the controller is the brain, the plant is the prosthetic, the feedback is the biofeedback, and the control command is the neural activity. Additionally, compared to other closed-loop systems, BMIs have various unique properties. Neural activity is noisy and stochastic, and often consists of a sequence of spike trains. Neural representations of movement could be non-stationary and change over time, for example as a result of learning. We review recent decoder designs that take these unique properties into account. We also discuss the opportunities that exist at the interface of control theory, statistical inference, and neuroscience to devise a control-theoretic framework for BMI design and help develop the next-generation BMI control algorithms.

  7. Brain-machine interface for eye movements.

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    Graf, Arnulf B A; Andersen, Richard A

    2014-12-09

    A number of studies in tetraplegic humans and healthy nonhuman primates (NHPs) have shown that neuronal activity from reach-related cortical areas can be used to predict reach intentions using brain-machine interfaces (BMIs) and therefore assist tetraplegic patients by controlling external devices (e.g., robotic limbs and computer cursors). However, to our knowledge, there have been no studies that have applied BMIs to eye movement areas to decode intended eye movements. In this study, we recorded the activity from populations of neurons from the lateral intraparietal area (LIP), a cortical node in the NHP saccade system. Eye movement plans were predicted in real time using Bayesian inference from small ensembles of LIP neurons without the animal making an eye movement. Learning, defined as an increase in the prediction accuracy, occurred at the level of neuronal ensembles, particularly for difficult predictions. Population learning had two components: an update of the parameters of the BMI based on its history and a change in the responses of individual neurons. These results provide strong evidence that the responses of neuronal ensembles can be shaped with respect to a cost function, here the prediction accuracy of the BMI. Furthermore, eye movement plans could be decoded without the animals emitting any actual eye movements and could be used to control the position of a cursor on a computer screen. These findings show that BMIs for eye movements are promising aids for assisting paralyzed patients.

  8. Brain Machine Interfaces for Robotic Control in Space Applications Project

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    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...

  9. Multiscale brain-machine interface decoders.

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    Han-Lin Hsieh; Shanechi, Maryam M

    2016-08-01

    Brain-machine interfaces (BMI) have vastly used a single scale of neural activity, e.g., spikes or electrocorticography (ECoG), as their control signal. New technology allows for simultaneous recording of multiple scales of neural activity, from spikes to local field potentials (LFP) and ECoG. These advances introduce the new challenge of modeling and decoding multiple scales of neural activity jointly. Such multi-scale decoding is challenging for two reasons. First, spikes are discrete-valued and ECoG/LFP are continuous-valued, resulting in fundamental differences in statistical characteristics. Second, the time-scales of these signals are different, with spikes having a millisecond time-scale and ECoG/LFP having much slower time-scales on the order of tens of milliseconds. Here we develop a new multiscale modeling and decoding framework that addresses these challenges. Our multiscale decoder extracts information from ECoG/LFP in addition to spikes, while operating at the fast time-scale of the spikes. The multiscale decoder specializes to a Kalman filter (KF) or to a point process filter (PPF) when no spikes or ECoG/LFP are available, respectively. Using closed-loop BMI simulations, we show that compared to PPF decoding of spikes alone or KF decoding of LFP/ECoG alone, the multiscale decoder significantly improves the accuracy and error performance of BMI control and runs at the fast millisecond time-scale of the spikes. This new multiscale modeling and decoding framework has the potential to improve BMI control using simultaneous multiscale neural activity.

  10. Brain-machine interface (BMI) in paralysis.

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    Chaudhary, U; Birbaumer, N; Curado, M R

    2015-02-01

    Brain-machine interfaces (BMIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this review, we focus on the current advances of non-invasive BMIs for communication in patients with amyotrophic lateral sclerosis (ALS) and for restoration of motor impairment after severe stroke. BMI represents a promising strategy to establish communication with paralyzed ALS patients as it does not need muscle engagement for its use. Distinct techniques have been explored to assess brain neurophysiology to control BMI for patients' communication, especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS). Previous studies demonstrated successful communication with ALS patients using EEG-BMI when patients still showed residual eye control, but patients with complete paralysis were unable to communicate with this system. We recently introduced functional NIRS (fNIRS)-BMI for communication in ALS patients in the complete locked-in syndrome (i.e., when ALS patients are unable to engage any muscle), opening new doors for communication in ALS patients after complete paralysis. In addition to assisted communication, BMI is also being extensively studied for motor recovery after stroke. BMI for stroke motor recovery includes intensive BMI training linking brain activity related to patient's intention to move the paretic limb with the contingent sensory feedback of the paretic limb movement guided by assistive devices. BMI studies in this area are mainly focused on EEG- or magnetoencephalography (MEG)-BMI systems due to their high temporal resolution, which facilitates online contingency between intention to move and sensory feedback of the intended movement. EEG-BMI training was recently demonstrated in a controlled study to significantly improve motor performance in stroke patients with severe paresis. Neural basis for BMI-induced restoration of motor function and perspectives for future

  11. 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.

  12. Future developments in brain-machine interface research.

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    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.

  13. A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

    OpenAIRE

    Su, Yi; Routhu, Sudhamayee; Moon, Kee S.; Lee, Sung Q.; Youm, WooSub; Ozturk, Yusuf

    2016-01-01

    All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in ...

  14. Dynamic Analysis of Naive Adaptive Brain-Machine Interfaces

    OpenAIRE

    Kowalski, Kevin C.; He, Bryan D.; Srinivasan, Lakshminarayan

    2013-01-01

    The closed-loop operation of brain-machine interfaces (BMI) provides a context to discover foundational principles behind human-computer interaction, with emerging clinical applications to stroke, neuromuscular diseases, and trauma. In the canonical BMI, a user controls a prosthetic limb through neural signals that are recorded by electrodes and processed by a decoder into limb movements. In laboratory demonstrations with able-bodied test subjects, parameters of the decoder are commonly tuned...

  15. Active tactile exploration using a brain-machine-brain interface.

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    O'Doherty, Joseph E; Lebedev, Mikhail A; Ifft, Peter J; Zhuang, Katie Z; Shokur, Solaiman; Bleuler, Hannes; Nicolelis, Miguel A L

    2011-10-05

    Brain-machine interfaces use neuronal activity recorded from the brain to establish direct communication with external actuators, such as prosthetic arms. It is hoped that brain-machine interfaces can be used to restore the normal sensorimotor functions of the limbs, but so far they have lacked tactile sensation. Here we report the operation of a brain-machine-brain interface (BMBI) that both controls the exploratory reaching movements of an actuator and allows signalling of artificial tactile feedback through intracortical microstimulation (ICMS) of the primary somatosensory cortex. Monkeys performed an active exploration task in which an actuator (a computer cursor or a virtual-reality arm) was moved using a BMBI that derived motor commands from neuronal ensemble activity recorded in the primary motor cortex. ICMS feedback occurred whenever the actuator touched virtual objects. Temporal patterns of ICMS encoded the artificial tactile properties of each object. Neuronal recordings and ICMS epochs were temporally multiplexed to avoid interference. Two monkeys operated this BMBI to search for and distinguish one of three visually identical objects, using the virtual-reality arm to identify the unique artificial texture associated with each. These results suggest that clinical motor neuroprostheses might benefit from the addition of ICMS feedback to generate artificial somatic perceptions associated with mechanical, robotic or even virtual prostheses.

  16. [Brain-machine interface (BMI) - application to neurological disorders].

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    Yoshimine, Toshiki; Yanagisawa, Takufumi; Hirata, Masayuki

    2013-01-01

    Brain-machine interface (BMI) is a new technology to receive input from the brain which is translated to operate a computer or other external device in real time. After significant progress during the recent 10 years, this technology is now very close to the clinical use to restore neural functions of patients with severe neurologic impairment. This technology is also a strong tool to investigate the mode of neuro-signal processing in the brain and to understand the mechanism of neural dysfunction which leads to the development of novel neurotechnology for the treatment of various sorts of neurological disorders.

  17. A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface.

    Science.gov (United States)

    Su, Yi; Routhu, Sudhamayee; Moon, Kee S; Lee, Sung Q; Youm, WooSub; Ozturk, Yusuf

    2016-09-24

    All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time.

  18. A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

    Directory of Open Access Journals (Sweden)

    Yi Su

    2016-09-01

    Full Text Available All neural information systems (NIS rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP oscillation and stimulate the target area at the same time.

  19. 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.

  20. Harnessing Prefrontal Cognitive Signals for Brain-Machine Interfaces.

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    Min, Byoung-Kyong; Chavarriaga, Ricardo; Millán, José Del R

    2017-07-01

    Brain-machine interfaces (BMIs) enable humans to interact with devices by modulating their brain signals. Despite impressive technological advancements, several obstacles remain. The most commonly used BMI control signals are derived from the brain areas involved in primary sensory- or motor-related processing. However, these signals only reflect a limited range of human intentions. Therefore, additional sources of brain activity for controlling BMIs need to be explored. In particular, higher-order cognitive brain signals, specifically those encoding goal-directed intentions, are natural candidates for enlarging the repertoire of BMI control signals and making them more efficient and intuitive. Thus, here, we identify the prefrontal brain area as a key target region for future BMIs, given its involvement in higher-order, goal-oriented cognitive processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Toward more versatile and intuitive cortical brain machine interfaces

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    Andersen, Richard A.; Kellis, Spencer; Klaes, Christian; Aflalo, Tyson

    2015-01-01

    Brain machine interfaces have great potential in neuroprosthetic applications to assist patients with brain injury and neurodegenerative diseases. One type of BMI is a cortical motor prosthetic which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using 1) recordings from cortical areas outside motor cortex; 2) local field potentials (LFPs) as a source of recorded signals; 3) somatosensory feedback for more dexterous control of robotics; and 4) new decoding methods that work in concert to form an ecology of decode algorithms. These new advances hold promise in greatly accelerating the applicability and ease of operation of motor prosthetics. PMID:25247368

  2. Modular particle filtering FPGA hardware architecture for brain machine interfaces.

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    Mountney, John; Obeid, Iyad; Silage, Dennis

    2011-01-01

    As the computational complexities of neural decoding algorithms for brain machine interfaces (BMI) increase, their implementation through sequential processors becomes prohibitive for real-time applications. This work presents the field programmable gate array (FPGA) as an alternative to sequential processors for BMIs. The reprogrammable hardware architecture of the FPGA provides a near optimal platform for performing parallel computations in real-time. The scalability and reconfigurability of the FPGA accommodates diverse sets of neural ensembles and a variety of decoding algorithms. Throughput is significantly increased by decomposing computations into independent parallel hardware modules on the FPGA. This increase in throughput is demonstrated through a parallel hardware implementation of the auxiliary particle filtering signal processing algorithm.

  3. Brain-machine interfaces for real-time speech synthesis.

    Science.gov (United States)

    Guenther, Frank H; Brumberg, Jonathan S

    2011-01-01

    This paper reports on studies involving brain-machine interfaces (BMIs) that provide near-instantaneous audio feedback from a speech synthesizer to the BMI user. In one study, neural signals recorded by an intracranial electrode implanted in a speech-related region of the left precentral gyrus of a human volunteer suffering from locked-in syndrome were transmitted wirelessly across the scalp and used to drive a formant synthesizer, allowing the user to produce vowels. In a second, pilot study, a neurologically normal user was able to drive the formant synthesizer with imagined movements detected using electroencephalography. Our results support the feasibility of neural prostheses that have the potential to provide near-conversational synthetic speech for individuals with severely impaired speech output.

  4. Intracortical Brain-Machine Interfaces Advance Sensorimotor Neuroscience.

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    Schroeder, Karen E; Chestek, Cynthia A

    2016-01-01

    Brain-machine interfaces (BMIs) decode brain activity to control external devices. Over the past two decades, the BMI community has grown tremendously and reached some impressive milestones, including the first human clinical trials using chronically implanted intracortical electrodes. It has also contributed experimental paradigms and important findings to basic neuroscience. In this review, we discuss neuroscience achievements stemming from BMI research, specifically that based upon upper limb prosthetic control with intracortical microelectrodes. We will focus on three main areas: first, we discuss progress in neural coding of reaches in motor cortex, describing recent results linking high dimensional representations of cortical activity to muscle activation. Next, we describe recent findings on learning and plasticity in motor cortex on various time scales. Finally, we discuss how bidirectional BMIs have led to better understanding of somatosensation in and related to motor cortex.

  5. Physiological properties of brain-machine interface input signals.

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    Slutzky, Marc W; Flint, Robert D

    2017-08-01

    Brain-machine interfaces (BMIs), also called brain-computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably, the first of these is the selection of brain signals used to control BMIs. In this review, we summarize the physiological characteristics and performance-including movement-related information, longevity, and stability-of multiple types of input signals that have been used in invasive BMIs to date. These include intracortical spikes as well as field potentials obtained inside the cortex, at the surface of the cortex (electrocorticography), and at the surface of the dura mater (epidural signals). We also discuss the potential for future enhancements in input signal performance, both by improving hardware and by leveraging the knowledge of the physiological characteristics of these signals to improve decoding and stability. Copyright © 2017 the American Physiological Society.

  6. A chronic generalized bi-directional brain-machine interface.

    Science.gov (United States)

    Rouse, A G; Stanslaski, S R; Cong, P; Jensen, R M; Afshar, P; Ullestad, D; Gupta, R; Molnar, G F; Moran, D W; Denison, T J

    2011-06-01

    A bi-directional neural interface (NI) system was designed and prototyped by incorporating a novel neural recording and processing subsystem into a commercial neural stimulator architecture. The NI system prototype leverages the system infrastructure from an existing neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing predicate therapy capabilities, the device adds key elements to facilitate chronic research, such as four channels of electrocortigram/local field potential amplification and spectral analysis, a three-axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom-integrated micropower sensor and interface circuits facilitate extended operation in a power-limited device. The prototype underwent significant verification testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclinically validated using an in vivo non-human primate model for brain control of a computer cursor (i.e. brain-machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively measure signal decoding performance from brain activity that is similar in both amplitude and spectral content to other biomarkers used to detect disease states (e.g. Parkinson's disease). A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection. These techniques have the potential to be generalized beyond motor prosthesis, and are being explored for unmet needs in other neurological conditions such as movement disorders, stroke and epilepsy.

  7. A chronic generalized bi-directional brain-machine interface

    Science.gov (United States)

    Rouse, A. G.; Stanslaski, S. R.; Cong, P.; Jensen, R. M.; Afshar, P.; Ullestad, D.; Gupta, R.; Molnar, G. F.; Moran, D. W.; Denison, T. J.

    2011-06-01

    A bi-directional neural interface (NI) system was designed and prototyped by incorporating a novel neural recording and processing subsystem into a commercial neural stimulator architecture. The NI system prototype leverages the system infrastructure from an existing neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing predicate therapy capabilities, the device adds key elements to facilitate chronic research, such as four channels of electrocortigram/local field potential amplification and spectral analysis, a three-axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom-integrated micropower sensor and interface circuits facilitate extended operation in a power-limited device. The prototype underwent significant verification testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclinically validated using an in vivo non-human primate model for brain control of a computer cursor (i.e. brain-machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively measure signal decoding performance from brain activity that is similar in both amplitude and spectral content to other biomarkers used to detect disease states (e.g. Parkinson's disease). A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection. These techniques have the potential to be generalized beyond motor prosthesis, and are being explored for unmet needs in other neurological conditions such as movement disorders, stroke and epilepsy.

  8. Biomimetic Brain Machine Interfaces for the Control of Movement

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    Fagg, Andrew H.; Hatsopoulos, Nicholas G.; de Lafuente, Victor; Moxon, Karen A.; Nemati, Shamim; Rebesco, James M.; Romo, Ranulfo; Solla, Sara A.; Reimer, Jake; Tkach, Dennis; Pohlmeyer, Eric A.; Miller, Lee E.

    2008-01-01

    Quite recently, it has become possible to use signals recorded simultaneously from large numbers of cortical neurons for real-time control. Such brain machine interfaces (BMIs) have allowed animal subjects and human patients to control the position of a computer cursor or robotic limb under the guidance of visual feedback. Although impressive, such approaches essentially ignore the dynamics of the musculoskeletal system, and they lack potentially critical somatosensory feedback. In this mini-symposium, we will initiate a discussion of systems that more nearly mimic the control of natural limb movement. The work that we will describe is based on fundamental observations of sensorimotor physiology that have inspired novel BMI approaches. We will focus on what we consider to be three of the most important new directions for BMI development related to the control of movement. (1) We will present alternative methods for building decoders, including structured, nonlinear models, the explicit incorporation of limb state information, and novel approaches to the development of decoders for paralyzed subjects unable to generate an output signal. (2) We will describe the real-time prediction of dynamical signals, including joint torque, force, and EMG, and the real-time control of physical plants with dynamics like that of the real limb. (3) We will discuss critical factors that must be considered to incorporate somatosensory feedback to the BMI user, including its potential benefits, the differing representations of sensation and perception across cortical areas, and the changes in the cortical representation of tactile events after spinal injury. PMID:17978021

  9. Stop state classification in intracortical brain-machine-interface.

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    Tze Hui Koh; Libedinsky, Camilo; Cuntai Guan; Kai Keng Ang; So, Rosa Q

    2017-07-01

    Invasive brain-machine-interface (BMI) has the prospect to empower tetraplegic patients with independent mobility through the use of brain-controlled wheelchairs. For the practical and long-term use of such control systems, the system has to distinguish between stop and movement states and has to be robust to overcome non-stationarity in the brain signals. In this work, we investigates the non-stationarity of the stop state on neural data collected from a macaque trained to control a robotic platform to stop and move in left, right, forward directions We then propose a hybrid approach that employs both random forest and linear discriminant analysis (LDA). Using this approach, we performed offline decoding on 8 days of data collected over the course of three months during joystick control of the robotic platform. We compared the results of using the proposed approach with the use of LDA alone to perform direct classifications of stop, left, right and forward. The results showed an average performance increment of 22.7% using the proposed hybrid approach. The results yielded significant improvements during sessions where LDA showed a heavy bias towards the stop state. This suggests that the proposed hybrid approach addresses the non-stationarity in the stop state and subsequently facilitates a more accurate decoding of the movement states.

  10. Brain-machine interfaces in neurorehabilitation of stroke.

    Science.gov (United States)

    Soekadar, Surjo R; Birbaumer, Niels; Slutzky, Marc W; Cohen, Leonardo G

    2015-11-01

    Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30-50% of all stroke victims. The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain's remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis. The development of brain-machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis. First, BMIs can establish continuous high-dimensional brain-control of robotic devices or functional electric stimulation (FES) to assist in daily life activities (assistive BMI). Second, BMIs could facilitate neuroplasticity, thus enhancing motor learning and motor recovery (rehabilitative BMI). Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI systems' clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke. Copyright © 2014. Published by Elsevier Inc.

  11. Neuroplasticity subserving the operation of brain-machine interfaces.

    Science.gov (United States)

    Oweiss, Karim G; Badreldin, Islam S

    2015-11-01

    Neuroplasticity is key to the operation of brain machine interfaces (BMIs)-a direct communication pathway between the brain and a man-made computing device. Whereas exogenous BMIs that associate volitional control of brain activity with neurofeedback have been shown to induce long lasting plasticity, endogenous BMIs that use prolonged activity-dependent stimulation--and thus may curtail the time scale that governs natural sensorimotor integration loops--have been shown to induce short lasting plasticity. Here we summarize recent findings from studies using both categories of BMIs, and discuss the fundamental principles that may underlie their operation and the longevity of the plasticity they induce. We draw comparison to plasticity mechanisms known to mediate natural sensorimotor skill learning and discuss principles of homeostatic regulation that may constrain endogenous BMI effects in the adult mammalian brain. We propose that BMIs could be designed to facilitate structural and functional plasticity for the purpose of re-organization of target brain regions and directed augmentation of sensorimotor maps, and suggest possible avenues for future work to maximize their efficacy and viability in clinical applications. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. What limits the performance of current invasive brain machine interfaces?

    Science.gov (United States)

    Baranauskas, Gytis

    2014-01-01

    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 generation BMIs.

  13. [Brain-machine interface--current status and future prospects].

    Science.gov (United States)

    Ushiba, Junichi

    2010-02-01

    Recent advances in brain activity analysis and computational algorithms have enabled people with severe motor disorders to control external devices via brain activity. Brain-machine interface (BMI)/brain-computer interface has gained importance as the ultimate strategy for functional compensation because it improves impaired neuromuscular systems. Invasive BMI performed using needle arrays can best control robotic arms or computer cursors because it records neural activity in the primary motor cortex in detail. The extensive and validated physiological background of recorded signals enables researchers to develop highly accurate BMI systems with needle electrodes. Less invasive neural recording with an electrocorticogram (ECoG)-electrode array provides good temporal and spatial information for use in prosthetic control. ECoG electrodes have wide clinical applications in pain control and epilepsy; therefore, techniques for electrode implantation are well established compared to those for needle arrays. These electrodes may find wide clinical applications if their accuracy level reaches that suitable for practical use. Noninvasive BMI involving neural recording by electroencephalography (EEG) is the most widely used technique because of a convenient experimental setup, although it provides a limited range of decodable motor outputs. In EEG, arc-shaped mu rhythms of 8-12 Hz appear around the sensorimotor area in the resting state and diminish in amplitude during motor imagery. Thus, the mu rhythm amplitude may correlate with cortical excitability of the sensorimotor area, and EEG-BMI may be useful in the neurorehabilitation of patients with stroke-induced hemiplegia. Research on BMI as a therapeutic tool though emergent, may widen the scope of conventional BMI.

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

    Directory of Open Access Journals (Sweden)

    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

  15. A glucose fuel cell for implantable brain-machine interfaces.

    Directory of Open Access Journals (Sweden)

    Benjamin I Rapoport

    Full Text Available We have developed an implantable fuel cell that generates power through glucose oxidation, producing 3.4 μW cm(-2 steady-state power and up to 180 μW cm(-2 peak power. The fuel cell is manufactured using a novel approach, employing semiconductor fabrication techniques, and is therefore well suited for manufacture together with integrated circuits on a single silicon wafer. Thus, it can help enable implantable microelectronic systems with long-lifetime power sources that harvest energy from their surrounds. The fuel reactions are mediated by robust, solid state catalysts. Glucose is oxidized at the nanostructured surface of an activated platinum anode. Oxygen is reduced to water at the surface of a self-assembled network of single-walled carbon nanotubes, embedded in a Nafion film that forms the cathode and is exposed to the biological environment. The catalytic electrodes are separated by a Nafion membrane. The availability of fuel cell reactants, oxygen and glucose, only as a mixture in the physiologic environment, has traditionally posed a design challenge: Net current production requires oxidation and reduction to occur separately and selectively at the anode and cathode, respectively, to prevent electrochemical short circuits. Our fuel cell is configured in a half-open geometry that shields the anode while exposing the cathode, resulting in an oxygen gradient that strongly favors oxygen reduction at the cathode. Glucose reaches the shielded anode by diffusing through the nanotube mesh, which does not catalyze glucose oxidation, and the Nafion layers, which are permeable to small neutral and cationic species. We demonstrate computationally that the natural recirculation of cerebrospinal fluid around the human brain theoretically permits glucose energy harvesting at a rate on the order of at least 1 mW with no adverse physiologic effects. Low-power brain-machine interfaces can thus potentially benefit from having their implanted units

  16. 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.

  17. Dynamic analysis of naive adaptive brain-machine interfaces.

    Science.gov (United States)

    Kowalski, Kevin C; He, Bryan D; Srinivasan, Lakshminarayan

    2013-09-01

    The closed-loop operation of brain-machine interfaces (BMI) provides a context to discover foundational principles behind human-computer interaction, with emerging clinical applications to stroke, neuromuscular diseases, and trauma. In the canonical BMI, a user controls a prosthetic limb through neural signals that are recorded by electrodes and processed by a decoder into limb movements. In laboratory demonstrations with able-bodied test subjects, parameters of the decoder are commonly tuned using training data that include neural signals and corresponding overt arm movements. In the application of BMI to paralysis or amputation, arm movements are not feasible, and imagined movements create weaker, partially unrelated patterns of neural activity. BMI training must begin naive, without access to these prototypical methods for parameter initialization used in most laboratory BMI demonstrations. Naive adaptive BMI refer to a class of methods recently introduced to address this problem. We first identify the basic elements of existing approaches based on adaptive filtering and define a decoder, ReFIT-PPF to represent these existing approaches. We then present Joint RSE, a novel approach that logically extends prior approaches. Using recently developed human- and synthetic-subjects closed-loop BMI simulation platforms, we show that Joint RSE significantly outperforms ReFIT-PPF and nonadaptive (static) decoders. Control experiments demonstrate the critical role of jointly estimating neural parameters and user intent. In addition, we show that nonzero sensorimotor delay in the user significantly degrades ReFIT-PPF but not Joint RSE, owing to differences in the prior on intended velocity. Paradoxically, substantial differences in the nature of sensory feedback between these methods do not contribute to differences in performance between Joint RSE and ReFIT-PPF. Instead, BMI performance improvement is driven by machine learning, which outpaces rates of human learning in

  18. Brain-Machine Interface in chronic stroke rehabilitation: A controlled study

    NARCIS (Netherlands)

    Ramos-Murguialday, A.; Brötz, D.; Rea, M.; Laër, L.; Yilmaz, O.; Brasil, F.L.; Liberati, G.; Curado, M.R.; Garcia Cossio, E.; Vyziotis, A.; Cho, W.; Agostini, M.; Soares, E.; Soekadar, S.R.; Caria, A.; Cohen, L.G.; Birbaumer, N.

    2013-01-01

    Objective: Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double-blind

  19. The potential of multilateral analyses of neuronal activities in future brain-machine interface research.

    Science.gov (United States)

    Sakamoto, Kazuhiro

    2013-01-01

    Current brain-machine interfaces are based on the implicit assumption that information encoded by neuronal activities does not change despite some recent physiological studies indicating that information encoded by neuronal activities changes. Here, we highlight the necessity for advanced decoding of neuronal activities. Especially, we discuss the advantages of multilateral analyses of neuronal activities, including synchronization and variability.

  20. Stereoelectroencephalography for continuous two-dimensional cursor control in a brain-machine interface.

    Science.gov (United States)

    Vadera, Sumeet; Marathe, Amar R; Gonzalez-Martinez, Jorge; Taylor, Dawn M

    2013-06-01

    Stereoelectroencephalography (SEEG) is becoming more prevalent as a planning tool for surgical treatment of intractable epilepsy. Stereoelectroencephalography uses long, thin, cylindrical "depth" electrodes containing multiple recording contacts along each electrode's length. Each lead is inserted into the brain percutaneously. The advantage of SEEG is that the electrodes can easily target deeper brain structures that are inaccessible with subdural grid electrodes, and SEEG does not require a craniotomy. Brain-machine interface (BMI) research is also becoming more common in the Epilepsy Monitoring Unit. A brain-machine interface decodes a person's desired movement or action from the recorded brain activity and then uses the decoded brain activity to control an assistive device in real time. Although BMIs are primarily being developed for use by severely paralyzed individuals, epilepsy patients undergoing invasive brain monitoring provide an opportunity to test the effectiveness of different invasive recording electrodes for use in BMI systems. This study investigated the ability to use SEEG electrodes for control of 2D cursor velocity in a BMI. Two patients who were undergoing SEEG for intractable epilepsy participated in this study. Participants were instructed to wiggle or rest the hand contralateral to their SEEG electrodes to control the horizontal velocity of a cursor on a screen. Simultaneously they were instructed to wiggle or rest their feet to control the vertical component of cursor velocity. The BMI system was designed to detect power spectral changes associated with hand and foot activity and translate those spectral changes into horizontal and vertical cursor movements in real time. During testing, participants used their decoded SEEG signals to move the brain-controlled cursor to radial targets that appeared on the screen. Although power spectral information from 28 to 32 electrode contacts were used for cursor control during the experiment, post hoc

  1. Dynamic Brain-Machine Interface: a novel paradigm for bidirectional interaction between brains and dynamical systems.

    Science.gov (United States)

    Szymanski, Francois D; Semprini, Marianna; Mussa-Ivaldi, Ferdinando A; Fadiga, Luciano; Panzeri, Stefano; Vato, Alessandro

    2011-01-01

    Brain-Machine Interfaces (BMIs) are systems which mediate communication between brains and artificial devices. Their long term goal is to restore motor functions, and this ultimately demands the development of a new generation of bidirectional brain-machine interfaces establishing a two-way brain-world communication channel, by both decoding motor commands from neural activity and providing feedback to the brain by electrical stimulation. Taking inspiration from how the spinal cord of vertebrates mediates communication between the brain and the limbs, here we present a model of a bidirectional brain-machine interface that interacts with a dynamical system by generating a control policy in the form of a force field. In our model, bidirectional communication takes place via two elements: (a) a motor interface decoding activities recorded from a motor cortical area, and (b) a sensory interface encoding the state of the controlled device into electrical stimuli delivered to a somatosensory area. We propose a specific mathematical model of the sensory and motor interfaces guiding a point mass moving in a viscous medium, and we demonstrate its performance by testing it on realistically simulated neural responses.

  2. Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG).

    Science.gov (United States)

    Witkowski, Matthias; Cortese, Mario; Cempini, Marco; Mellinger, Jürgen; Vitiello, Nicola; Soekadar, Surjo R

    2014-12-16

    Brain-machine interfaces (BMIs) allow direct translation of electric, magnetic or metabolic brain signals into control commands of external devices such as robots, prostheses or exoskeletons. However, non-stationarity of brain signals and susceptibility to biological or environmental artifacts impede reliable control and safety of BMIs, particularly in daily life environments. Here we introduce and tested a novel hybrid brain-neural computer interaction (BNCI) system fusing electroencephalography (EEG) and electrooculography (EOG) to enhance reliability and safety of continuous hand exoskeleton-driven grasping motions. 12 healthy volunteers (8 male, mean age 28.1 ± 3.63y) used EEG (condition #1) and hybrid EEG/EOG (condition #2) signals to control a hand exoskeleton. Motor imagery-related brain activity was translated into exoskeleton-driven hand closing motions. Unintended motions could be interrupted by eye movement-related EOG signals. In order to evaluate BNCI control and safety, participants were instructed to follow a visual cue indicating either to move or not to move the hand exoskeleton in a random order. Movements exceeding 25% of a full grasping motion when the device was not supposed to be moved were defined as safety violation. While participants reached comparable control under both conditions, safety was frequently violated under condition #1 (EEG), but not under condition #2 (EEG/EOG). EEG/EOG biosignal fusion can substantially enhance safety of assistive BNCI systems improving their applicability in daily life environments.

  3. 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

  4. Wireless communication links for brain-machine interface applications

    Science.gov (United States)

    Larson, L.

    2016-05-01

    Recent technological developments have given neuroscientists direct access to neural signals in real time, with the accompanying ability to decode the resulting information and control various prosthetic devices and gain insight into deeper aspects of cognition. These developments - along with deep brain stimulation for Parkinson's disease and the possible use of electro-stimulation for other maladies - leads to the conclusion that the widespread use electronic brain interface technology is a long term possibility. This talk will summarize the various technical challenges and approaches that have been developed to wirelessly communicate with the brain, including technology constraints, dc power limits, compression and data rate issues.

  5. Forward Prediction in the Posterior Parietal Cortex and Dynamic Brain-Machine Interface.

    Science.gov (United States)

    Cui, He

    2016-01-01

    While remarkable progress has been made in brain-machine interfaces (BMIs) over the past two decades, it is still difficult to utilize neural signals to drive artificial actuators to produce predictive movements in response to dynamic stimuli. In contrast to naturalistic limb movements largely based on forward planning, brain-controlled neuroprosthetics mainly rely on feedback without prior trajectory formation. As an important sensorimotor interface integrating multisensory inputs and efference copy, the posterior parietal cortex (PPC) might play a proactive role in predictive motor control. Here it is proposed that predictive neural activity in PPC could be decoded to provide prosthetic control signals for guiding BMI systems in dynamic environments.

  6. Combining decoder design and neural adaptation in brain-machine interfaces.

    Science.gov (United States)

    Shenoy, Krishna V; Carmena, Jose M

    2014-11-19

    Brain-machine interfaces (BMIs) aim to help people with paralysis by decoding movement-related neural signals into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Despite compelling laboratory experiments and ongoing FDA pilot clinical trials, system performance, robustness, and generalization remain challenges. We provide a perspective on how two complementary lines of investigation, that have focused on decoder design and neural adaptation largely separately, could be brought together to advance BMIs. This BMI paradigm should also yield new scientific insights into the function and dysfunction of the nervous system. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. A bidirectional brain-machine interface connecting alert rodents to a dynamical system.

    Science.gov (United States)

    Boi, Fabio; Semprini, Marianna; Mussa Ivaldi, Ferdinando A; Panzeri, Stefano; Vato, Alessandro

    2015-01-01

    We present a novel experimental framework that implements a bidirectional brain-machine interface inspired by the operation of the spinal cord in vertebrates that generates a control policy in the form of a force field. The proposed experimental set-up allows connecting the brain of freely moving rats to an external device. We tested this apparatus in a preliminary experiment with an alert rat that used the interface for acquiring a food reward. The goal of this approach to bidirectional interfaces is to explore the role of voluntary neural commands in controlling a dynamical system represented by a small cart moving on vertical plane and connected to a water/pellet dispenser.

  8. 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.

  9. The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces.

    Science.gov (United States)

    O'Shea, Daniel J; Trautmann, Eric; Chandrasekaran, Chandramouli; Stavisky, Sergey; Kao, Jonathan C; Sahani, Maneesh; Ryu, Stephen; Deisseroth, Karl; Shenoy, Krishna V

    2017-01-01

    A central goal of neuroscience is to understand how populations of neurons coordinate and cooperate in order to give rise to perception, cognition, and action. Nonhuman primates (NHPs) are an attractive model with which to understand these mechanisms in humans, primarily due to the strong homology of their brains and the cognitively sophisticated behaviors they can be trained to perform. Using electrode recordings, the activity of one to a few hundred individual neurons may be measured electrically, which has enabled many scientific findings and the development of brain-machine interfaces. Despite these successes, electrophysiology samples sparsely from neural populations and provides little information about the genetic identity and spatial micro-organization of recorded neurons. These limitations have spurred the development of all-optical methods for neural circuit interrogation. Fluorescent calcium signals serve as a reporter of neuronal responses, and when combined with post-mortem optical clearing techniques such as CLARITY, provide dense recordings of neuronal populations, spatially organized and annotated with genetic and anatomical information. Here, we advocate that this methodology, which has been of tremendous utility in smaller animal models, can and should be developed for use with NHPs. We review here several of the key opportunities and challenges for calcium-based optical imaging in NHPs. We focus on motor neuroscience and brain-machine interface design as representative domains of opportunity within the larger field of NHP neuroscience. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  10. A 128-Channel Extreme Learning Machine-Based Neural Decoder for Brain Machine Interfaces.

    Science.gov (United States)

    Chen, Yi; Yao, Enyi; Basu, Arindam

    2016-06-01

    Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces are mostly implemented on a PC and consume significant amount of power. A machine learning coprocessor in 0.35- μm CMOS for the motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm and low-power analog processing, it achieves an energy efficiency of 3.45 pJ/MAC at a classification rate of 50 Hz. The learning in second stage and corresponding digitally stored coefficients are used to increase robustness of the core analog processor. The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3% for movement type. The same coprocessor is also used to decode time of movement from asynchronous neural spikes. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels. Further, a sparsity promoting training scheme enables reduction of number of programmable weights by ≈ 2X.

  11. Control of Redundant Kinematic Degrees of Freedom in a Closed-Loop Brain-Machine Interface.

    Science.gov (United States)

    Moorman, Helene G; Gowda, Suraj; Carmena, Jose M

    2017-06-01

    Brain-machine interface (BMI) systems use signals acquired from the brain to directly control the movement of an actuator, such as a computer cursor or a robotic arm, with the goal of restoring motor function lost due to injury or disease of the nervous system. In BMIs with kinematically redundant actuators, the combination of the task goals and the system under neural control can allow for many equally optimal task solutions. The extent to which kinematically redundant degrees of freedom (DOFs) in a BMI system may be under direct neural control is unknown. To address this question, a Kalman filter was used to decode single- and multi-unit cortical neural activity of two macaque monkeys into the joint velocities of a virtual four-link kinematic chain. Subjects completed movements of the chain's endpoint to instructed target locations within a two-dimensional plane. This system was kinematically redundant for an endpoint movement task, as four DOFs were used to manipulate the 2-D endpoint position. Both subjects successfully performed the task and improved with practice by producing faster endpoint velocity control signals. Kinematic redundancy allowed null movements whereby the individual links of the chain could move in a way that cancels out and does not result in endpoint movement. As the subjects became more proficient at controlling the chain, the amount of null movement also increased. Task performance suffered when the links of the kinematic chain were hidden and only the endpoint was visible. Furthermore, all four DOFs of the joint-velocity control space exhibited task-relevant modulation. The relative usage of each DOF depended on the configuration of the chain, and trials in which the less-prominent DOFs were utilized also had better task performance. Overall, these results indicate that the subjects incorporated the redundant components of the control space into their control strategy. Future BMI systems with kinematic redundancy, such as exoskeletal

  12. Current challenges to the clinical translation of brain machine interface technology.

    Science.gov (United States)

    Lu, Charles W; Patil, Parag G; Chestek, Cynthia A

    2012-01-01

    Development of neural prostheses over the past few decades has produced a number of clinically relevant brain-machine interfaces (BMIs), such as the cochlear prostheses and deep brain stimulators. Current research pursues the restoration of communication or motor function to individuals with neurological disorders. Efforts in the field, such as the BrainGate trials, have already demonstrated that such interfaces can enable humans to effectively control external devices with neural signals. However, a number of significant issues regarding BMI performance, device capabilities, and surgery must be resolved before clinical use of BMI technology can become widespread. This chapter reviews challenges to clinical translation and discusses potential solutions that have been reported in recent literature, with focuses on hardware reliability, state-of-the-art decoding algorithms, and surgical considerations during implantation. Copyright © 2012 Elsevier Inc. All rights reserved.

  13. Model development, testing and experimentation in a CyberWorkstation for Brain-Machine Interface research.

    Science.gov (United States)

    Rattanatamrong, Prapaporn; Matsunaga, Andrea; Raiturkar, Pooja; Mesa, Diego; Zhao, Ming; Mahmoudi, Babak; Digiovanna, Jack; Principe, Jose; Figueiredo, Renato; Sanchez, Justin; Fortes, Jose

    2010-01-01

    The CyberWorkstation (CW) is an advanced cyber-infrastructure for Brain-Machine Interface (BMI) research. It allows the development, configuration and execution of BMI computational models using high-performance computing resources. The CW's concept is implemented using a software structure in which an "experiment engine" is used to coordinate all software modules needed to capture, communicate and process brain signals and motor-control commands. A generic BMI-model template, which specifies a common interface to the CW's experiment engine, and a common communication protocol enable easy addition, removal or replacement of models without disrupting system operation. This paper reviews the essential components of the CW and shows how templates can facilitate the processes of BMI model development, testing and incorporation into the CW. It also discusses the ongoing work towards making this process infrastructure independent.

  14. Endogenous brain-machine interface based on the correlation of EEG maps.

    Science.gov (United States)

    Ubeda, Andrés; Iáñez, Eduardo; Azorín, José M; Perez-Vidal, Carlos

    2013-11-01

    In this paper, a non-invasive endogenous brain-machine interface (BMI) based on the correlation of EEG maps has been developed to work in real-time applications. The classifier is able to detect two mental tasks related to motor imagery with good success rates and stability. The BMI has been tested with four able-bodied volunteers. First, the users performed a training with visual feedback to adjust the classifier. Afterwards, the users carried out several trajectories in a visual interface controlling the cursor position with the BMI. In these tests, score and accuracy were measured. The results showed that the participants were able to follow the targets during the performed trajectory, proving that the EEG mapping correlation classifier is ready to work in more complex real-time applications aimed at helping people with a severe disability in their daily life. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  15. Forward prediction in the posterior parietal cortex and dynamic brain-machine interface

    Directory of Open Access Journals (Sweden)

    He Cui

    2016-10-01

    Full Text Available While remarkable progress has been made in brain-machine interfaces (BMIs over the past two decades, it is still difficult to utilize neural signals to drive artificial actuators to produce predictive movements in response to dynamic stimuli. In contrast to naturalistic limb movements largely based on forward planning, brain-controlled neuroprosthetics mainly rely on feedback without prior trajectory formation. As an important sensorimotor interface integrating multisensory inputs and efference copy, the posterior parietal cortex (PPC might play a proactive role in predictive motor control. Here it is proposed that predictive neural activity in PPC could be decoded to provide prosthetic control signals for guiding BMI systems in dynamic environments.

  16. A Closed Loop Brain-machine Interface for Epilepsy Control Using Dorsal Column Electrical Stimulation.

    Science.gov (United States)

    Pais-Vieira, Miguel; Yadav, Amol P; Moreira, Derek; Guggenmos, David; Santos, Amílcar; Lebedev, Mikhail; Nicolelis, Miguel A L

    2016-09-08

    Although electrical neurostimulation has been proposed as an alternative treatment for drug-resistant cases of epilepsy, current procedures such as deep brain stimulation, vagus, and trigeminal nerve stimulation are effective only in a fraction of the patients. Here we demonstrate a closed loop brain-machine interface that delivers electrical stimulation to the dorsal column (DCS) of the spinal cord to suppress epileptic seizures. Rats were implanted with cortical recording microelectrodes and spinal cord stimulating electrodes, and then injected with pentylenetetrazole to induce seizures. Seizures were detected in real time from cortical local field potentials, after which DCS was applied. This method decreased seizure episode frequency by 44% and seizure duration by 38%. We argue that the therapeutic effect of DCS is related to modulation of cortical theta waves, and propose that this closed-loop interface has the potential to become an effective and semi-invasive treatment for refractory epilepsy and other neurological disorders.

  17. Common Spatio-Time-Frequency Patterns for Motor Imagery-Based Brain Machine Interfaces

    Science.gov (United States)

    Higashi, Hiroshi; Tanaka, Toshihisa

    2013-01-01

    For efficient decoding of brain activities in analyzing brain function with an application to brain machine interfacing (BMI), we address a problem of how to determine spatial weights (spatial patterns), bandpass filters (frequency patterns), and time windows (time patterns) by utilizing electroencephalogram (EEG) recordings. To find these parameters, we develop a data-driven criterion that is a natural extension of the so-called common spatial patterns (CSP) that are known to be effective features in BMI. We show that the proposed criterion can be optimized by an alternating procedure to achieve fast convergence. Experiments demonstrate that the proposed method can effectively extract discriminative features for a motor imagery-based BMI. PMID:24302929

  18. 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.

  19. Applications of Brain-Machine Interface Systems in Stroke Recovery and Rehabilitation.

    Science.gov (United States)

    Venkatakrishnan, Anusha; Francisco, Gerard E; Contreras-Vidal, Jose L

    2014-06-01

    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.

  20. Brain-machine interfacing control of whole-body humanoid motion

    Science.gov (United States)

    Bouyarmane, Karim; Vaillant, Joris; Sugimoto, Norikazu; Keith, François; Furukawa, Jun-ichiro; Morimoto, Jun

    2014-01-01

    We propose to tackle in this paper the problem of controlling whole-body humanoid robot behavior through non-invasive brain-machine interfacing (BMI), motivated by the perspective of mapping human motor control strategies to human-like mechanical avatar. Our solution is based on the adequate reduction of the controllable dimensionality of a high-DOF humanoid motion in line with the state-of-the-art possibilities of non-invasive BMI technologies, leaving the complement subspace part of the motion to be planned and executed by an autonomous humanoid whole-body motion planning and control framework. The results are shown in full physics-based simulation of a 36-degree-of-freedom humanoid motion controlled by a user through EEG-extracted brain signals generated with motor imagery task. PMID:25140134

  1. Neural Operant Conditioning as a Core Mechanism of Brain-Machine Interface Control

    Directory of Open Access Journals (Sweden)

    Yoshio Sakurai

    2016-08-01

    Full Text Available The process of changing the neuronal activity of the brain to acquire rewards in a broad sense is essential for utilizing brain-machine interfaces (BMIs, which is essentially operant conditioning of neuronal activity. Currently, this is also known as neural biofeedback, and it is often referred to as neurofeedback when human brain activity is targeted. In this review, we first illustrate biofeedback and operant conditioning, which are methodological background elements in neural operant conditioning. Then, we introduce research models of neural operant conditioning in animal experiments and demonstrate that it is possible to change the firing frequency and synchronous firing of local neuronal populations in a short time period. We also debate the possibility of the application of neural operant conditioning and its contribution to BMIs.

  2. Brain-Machine Interfacing Control of Whole-Body Humanoid Motion

    Directory of Open Access Journals (Sweden)

    Karim eBouyarmane

    2014-08-01

    Full Text Available We propose to tackle in this paper the problem of controlling whole-body humanoid robot behavior through non-invasive brain-machine interfacing (BMI, motivated by the perspective of mapping human motor control strategies to human-like mechanical avatar. Our solution is based on the adequate reduction of the controllable dimensionality of a high-DOF humanoid motion in line with the state-of-the-art possibilities of non-invasive BMI technologies, leaving the complement subspace part of the motion to be planned and executed by an autonomous humanoid whole-body motion planning and control framework. The results are shown in full physics-based simulation of a 36-degree-of-freedom humanoid motion controlled by a user through EEG-extracted brain signals generated with motor imagery task.

  3. Brain-machine interfacing control of whole-body humanoid motion.

    Science.gov (United States)

    Bouyarmane, Karim; Vaillant, Joris; Sugimoto, Norikazu; Keith, François; Furukawa, Jun-Ichiro; Morimoto, Jun

    2014-01-01

    We propose to tackle in this paper the problem of controlling whole-body humanoid robot behavior through non-invasive brain-machine interfacing (BMI), motivated by the perspective of mapping human motor control strategies to human-like mechanical avatar. Our solution is based on the adequate reduction of the controllable dimensionality of a high-DOF humanoid motion in line with the state-of-the-art possibilities of non-invasive BMI technologies, leaving the complement subspace part of the motion to be planned and executed by an autonomous humanoid whole-body motion planning and control framework. The results are shown in full physics-based simulation of a 36-degree-of-freedom humanoid motion controlled by a user through EEG-extracted brain signals generated with motor imagery task.

  4. 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.

  5. 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.

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

    Science.gov (United States)

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

    2018-04-01

    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. 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. 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. (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 of intracortical brain-machine interfaces in

  7. A non-linear mapping algorithm shaping the control policy of a bidirectional brain machine interface.

    Science.gov (United States)

    Boi, Fabio; Semprini, Marianna; Vato, Alessandro

    2016-08-01

    Motor brain-machine interfaces (BMIs) transform neural activities recorded directly from the brain into motor commands to control the movements of an external object by establishing an interface between the central nervous system (CNS) and the device. Bidirectional BMIs are closed-loop systems that add a sensory channel to provide the brain with an artificial feedback signal produced by the interaction between the device and the external world. Taking inspiration from the functioning of the spinal cord in mammalians, in our previous works we designed and developed a bidirectional BMI that uses the neural signals recorded form rats' motor cortex to control the movement of an external object. We implemented a decoding interface based on the approximation of a predefined force field with a central attractor point. Now we consider a non-linear transformation that allows to design a decoder approximating force fields with arbitrary attractors. We describe here the non-linear mapping algorithm and preliminary results of its use with behaving rats.

  8. Development of double density whole brain fNIRS with EEG system for brain machine interface.

    Science.gov (United States)

    Ishikawa, A; Udagawa, H; Masuda, Y; Kohno, S; Amita, T; Inoue, Y

    2011-01-01

    Brain-machine interfaces (BMI) are expected as new man-machine interfaces. Non-invasive BMI have the potential to improve the quality of life of many disabled individuals with safer operation. The non-invasive BMI using the functional functional near-infrared spectroscopy (fNIRS) with the electroencephalogram (EEG) has potential applicability beyond the restoration of lost movement and rehabilitation in paraplegics and would enable normal individuals to have direct brain control of external devices in their daily lives. To shift stage of the non-invasive BMI from laboratory to clinical, the key factor is to develop high-accuracy signal decoding technology and highly restrictive of the measurement area. In this article, we present the development of a high-accuracy brain activity measurement system by combining fNIRS and EEG. The new fNIRS had high performances with high spatial resolution using double density technique and a large number of measurement channels to cover a whole human brain.

  9. Control of a 2 DoF robot using a brain-machine interface.

    Science.gov (United States)

    Hortal, Enrique; Ubeda, Andrés; Iáñez, Eduardo; Azorín, José M

    2014-09-01

    In this paper, a non-invasive spontaneous Brain-Machine Interface (BMI) is used to control the movement of a planar robot. To that end, two mental tasks are used to manage the visual interface that controls the robot. The robot used is a PupArm, a force-controlled planar robot designed by the nBio research group at the Miguel Hernández University of Elche (Spain). Two control strategies are compared: hierarchical and directional control. The experimental test (performed by four users) consists of reaching four targets. The errors and time used during the performance of the tests are compared in both control strategies (hierarchical and directional control). The advantages and disadvantages of each method are shown after the analysis of the results. The hierarchical control allows an accurate approaching to the goals but it is slower than using the directional control which, on the contrary, is less precise. The results show both strategies are useful to control this planar robot. In the future, by adding an extra device like a gripper, this BMI could be used in assistive applications such as grasping daily objects in a realistic environment. In order to compare the behavior of the system taking into account the opinion of the users, a NASA Tasks Load Index (TLX) questionnaire is filled out after two sessions are completed. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  10. Creating new functional circuits for action via brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Amy eOrsborn

    2013-11-01

    Full Text Available Brain-machine interfaces (BMIs are an emerging technology with great promise for developing restorative therapies for those with disabilities. BMIs also create novel, well-defined functional circuits for action that are distinct from the natural sensorimotor apparatus. Closed-loop control of BMI systems can also actively engage learning and adaptation. These properties make BMIs uniquely suited to study learning of motor and non-physical, abstract skills. Recent work used motor BMIs to shed light on the neural representations of skill formation and motor adaptation. Emerging work in sensory BMIs, and other novel interface systems, also highlight the promise of using BMI systems to study fundamental questions in learning and sensorimotor control. This paper outlines the interpretation of BMIs as novel closed-loop systems and the benefits of these systems for studying learning. We review BMI learning studies, their relation to motor control, and propose future directions for this nascent field. Understanding learning in BMIs may both elucidate mechanisms of natural motor and abstract skill learning, and aid in developing the next generation of neuroprostheses.

  11. 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.

  12. A Review of fMRI as a Tool for Enhancing Eeg-Based Brain-Machine Interfaces

    Directory of Open Access Journals (Sweden)

    Luis J. Barrios

    2012-01-01

    Full Text Available Human-robot interaction has been going stronger and stronger, up to find a notorious level on brain-machines interfaces. This assistive technology offers a great hope for patients suffering severe neuromuscular disorders. Starting from the current limitations hindering its extensive application outside the research laboratories, this paper reviews findings and prospects on functional magnetic resonance imaging showing how fMRI can help to overcome those limitations, while playing a key role on improving the development of brain-machine interfaces based on electroencephalography. The different types of derived benefits for this interfaces, as well as the different kinds of impact on their components, are presented under a field classification that reveals the distinctive roles that fMRI can play on the present context. The review concludes that fMRI provides complementary knowledge of immediate application, and that a greater profit could be obtained from the own EEG signal by integrating both neuroimaging modalities.

  13. 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.

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

    Science.gov (United States)

    Vato, Alessandro; Szymanski, Francois D; Semprini, Marianna; Mussa-Ivaldi, Ferdinando A; Panzeri, Stefano

    2014-01-01

    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.

  15. Decoding hindlimb movement for a brain machine interface after a complete spinal transection.

    Directory of Open Access Journals (Sweden)

    Anitha Manohar

    Full Text Available Stereotypical locomotor movements can be made without input from the brain after a complete spinal transection. However, the restoration of functional gait requires descending modulation of spinal circuits to independently control the movement of each limb. To evaluate whether a brain-machine interface (BMI could be used to regain conscious control over the hindlimb, rats were trained to press a pedal and the encoding of hindlimb movement was assessed using a BMI paradigm. Off-line, information encoded by neurons in the hindlimb sensorimotor cortex was assessed. Next neural population functions, or weighted representations of the neuronal activity, were used to replace the hindlimb movement as a trigger for reward in real-time (on-line decoding in three conditions: while the animal could still press the pedal, after the pedal was removed and after a complete spinal transection. A novel representation of the motor program was learned when the animals used neural control to achieve water reward (e.g. more information was conveyed faster. After complete spinal transection, the ability of these neurons to convey information was reduced by more than 40%. However, this BMI representation was relearned over time despite a persistent reduction in the neuronal firing rate during the task. Therefore, neural control is a general feature of the motor cortex, not restricted to forelimb movements, and can be regained after spinal injury.

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

    Science.gov (United States)

    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. PMID:21687575

  17. Motor imaginary-based brain-machine interface design using programmable logic controllers for the disabled.

    Science.gov (United States)

    Jeyabalan, Vickneswaran; Samraj, Andrews; Loo, Chu Kiong

    2010-10-01

    Aiming at the implementation of brain-machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain’s motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.

  18. Use of a Bayesian maximum likelihood classifier to generate training data for brain-machine interfaces

    Science.gov (United States)

    Ludwig, Kip A.; Miriani, Rachel M.; Langhals, Nicholas B.; Marzullo, Timothy C.; Kipke, Daryl R.

    2011-01-01

    Brain-machine interface decoding algorithms need to be predicated on assumptions that are easily met outside of an experimental setting to enable a practical clinical device. Given present technology limitations, there is a need for decoding algorithms which a) are not dependent upon a large number of neurons for control, b) are adaptable to alternative sources of neuronal input such as local field potentials, and c) require only marginal training data for daily calibrations. Moreover, practical algorithms must recognize when the user is not intending to generate a control output and eliminate poor training data. In this study, we introduce and evaluate a Bayesian Maximum-Likelihood Estimation (bMLE) strategy to address the issues of isolating quality training data and self-paced control. Six animal subjects demonstrate that a multiple state classification task, loosely based on the standard center-out task, can be accomplished with fewer than five engaged neurons while requiring less than ten trials for algorithm training. In addition, untrained animals quickly obtained accurate device control utilizing local field potentials as well as neurons in cingulate cortex, two non-traditional neural inputs. PMID:21654038

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

    Science.gov (United States)

    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.

  20. 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.

  1. Adaptive decoding using local field potentials in a brain-machine interface.

    Science.gov (United States)

    Rosa So; Libedinsky, Camilo; Kai Keng Ang; Wee Chiek Clement Lim; Kyaw Kyar Toe; Cuntai Guan

    2016-08-01

    Brain-machine interface (BMI) systems have the potential to restore function to people who suffer from paralysis due to a spinal cord injury. However, in order to achieve long-term use, BMI systems have to overcome two challenges - signal degeneration over time, and non-stationarity of signals. Effects of loss in spike signals over time can be mitigated by using local field potential (LFP) signals for decoding, and a solution to address the signal non-stationarity is to use adaptive methods for periodic recalibration of the decoding model. We implemented a BMI system in a nonhuman primate model that allows brain-controlled movement of a robotic platform. Using this system, we showed that LFP signals alone can be used for decoding in a closed-loop brain-controlled BMI. Further, we performed offline analysis to assess the potential implementation of an adaptive decoding method that does not presume knowledge of the target location. Our results show that with periodic signal and channel selection adaptation, decoding accuracy using LFP alone can be improved by between 5-50%. These results demonstrate the feasibility of implementing unsupervised adaptive methods during asynchronous decoding of LFP signals for long-term usage in a BMI system.

  2. Quantized Attention-Gated Kernel Reinforcement Learning for Brain-Machine Interface Decoding.

    Science.gov (United States)

    Wang, Fang; Wang, Yiwen; Xu, Kai; Li, Hongbao; Liao, Yuxi; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang; Principe, Jose C

    2017-04-01

    Reinforcement learning (RL)-based decoders in brain-machine interfaces (BMIs) interpret dynamic neural activity without patients' real limb movements. In conventional RL, the goal state is selected by the user or defined by the physics of the problem, and the decoder finds an optimal policy essentially by assigning credit over time, which is normally very time-consuming. However, BMI tasks require finding a good policy in very few trials, which impose a limit on the complexity of the tasks that can be learned before the animal quits. Therefore, this paper explores the possibility of letting the agent infer potential goals through actions over space with multiple objects, using the instantaneous reward to assign credit spatially. A previous method, attention-gated RL employs a multilayer perceptron trained with backpropagation, but it is prone to local minima entrapment. We propose a quantized attention-gated kernel RL (QAGKRL) to avoid the local minima adaptation in spatial credit assignment and sparsify the network topology. The experimental results show that the QAGKRL achieves higher successful rates and more stable performance, indicating its powerful decoding ability for more sophisticated BMI tasks as required in clinical applications.

  3. Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control

    Science.gov (United States)

    Iturrate, Iñaki; Chavarriaga, Ricardo; Montesano, Luis; Minguez, Javier; Millán, José del R.

    2015-01-01

    Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. Here we demonstrate an alternative and complementary BMI paradigm that overcomes that limitation by decoding cognitive brain signals associated with monitoring processes relevant for achieving goals. In our approach the neuroprosthesis executes actions that the subject evaluates as erroneous or correct, and exploits the brain correlates of this assessment to learn suitable motor behaviours. Results show that, after a short user’s training period, this teaching BMI paradigm operated three different neuroprostheses and generalized across several targets. Our results further support that these error-related signals reflect a task-independent monitoring mechanism in the brain, making this teaching paradigm scalable. We anticipate this BMI approach to become a key component of any neuroprosthesis that mimics natural motor control as it enables continuous adaptation in the absence of explicit information about goals. Furthermore, our paradigm can seamlessly incorporate other cognitive signals and conventional neuroprosthetic approaches, invasive or non-invasive, to enlarge the range and complexity of tasks that can be accomplished. PMID:26354145

  4. Multiscale decoding for reliable brain-machine interface performance over time.

    Science.gov (United States)

    Han-Lin Hsieh; Wong, Yan T; Pesaran, Bijan; Shanechi, Maryam M

    2017-07-01

    Recordings from invasive implants can degrade over time, resulting in a loss of spiking activity for some electrodes. For brain-machine interfaces (BMI), such a signal degradation lowers control performance. Achieving reliable performance over time is critical for BMI clinical viability. One approach to improve BMI longevity is to simultaneously use spikes and other recording modalities such as local field potentials (LFP), which are more robust to signal degradation over time. We have developed a multiscale decoder that can simultaneously model the different statistical profiles of multi-scale spike/LFP activity (discrete spikes vs. continuous LFP). This decoder can also run at multiple time-scales (millisecond for spikes vs. tens of milliseconds for LFP). Here, we validate the multiscale decoder for estimating the movement of 7 major upper-arm joint angles in a non-human primate (NHP) during a 3D reach-to-grasp task. The multiscale decoder uses motor cortical spike/LFP recordings as its input. We show that the multiscale decoder can improve decoding accuracy by adding information from LFP to spikes, while running at the fast millisecond time-scale of the spiking activity. Moreover, this improvement is achieved using relatively few LFP channels, demonstrating the robustness of the approach. These results suggest that using multiscale decoders has the potential to improve the reliability and longevity of BMIs.

  5. [An outlook on the present and future of brain-machine interface research].

    Science.gov (United States)

    Majima, Kei; Kamitani, Yukiyasu

    2011-03-01

    The goal of brain-machine interface (BMI) research is to interpret brain signals in order to control an external device. Substantial progress toward this goal has been achieved over the last decade. Currently, BMI algorithms can translate neural signals into motor commands that reproduce arm-reaching and hand-grasping movements in artificial actuators, thereby promising the restoration of limb mobility in paralyzed people. In one study, a tetraplegic human subject used a clinical neuromotor prosthesis to restore his communication and mobility. Furthermore, a recently developed neural decoding technology provides an effective means to read out mental states from human brain activity. Decoding of mental states could be used for direct human-human communication outside the brain's normal pathways. However, for BMI practical, long-term stability of signal interpretation is required. Unfortunately, the classical invasive BMI methods suffer from poor long-term stability because of deterioration in signal quality. Two new approaches to long-term BMI applications are showing promising results in maintaining signal quality. One is the use of newly developed electrodes that are less harmful to neural tissues, and the other is the use of electrocorticograms (ECoGs), which measure population activity of neurons with electrodes placed on the surface of the brain. Both these new technologies facilitate clearer signals from the brain and greater stability of brain signals over time. In this review, we summarize the previous BMI approaches and shed light upon the new advances that may enable long-term BMI use.

  6. A novel bioelectronic tongue in vivo for highly sensitive bitterness detection with brain-machine interface.

    Science.gov (United States)

    Qin, Zhen; Zhang, Bin; Hu, Liang; Zhuang, Liujing; Hu, Ning; Wang, Ping

    2016-04-15

    Animals' gustatory system has been widely acknowledged as one of the most sensitive chemosensing systems, especially for its ability to detect bitterness. Since bitterness usually symbolizes inedibility, the potential to use rodent's gustatory system is investigated to detect bitter compounds. In this work, the extracellular potentials of a group of neurons are recorded by chronically coupling microelectrode array to rat's gustatory cortex with brain-machine interface (BMI) technology. Local field potentials (LFPs), which represent the electrophysiological activity of neural networks, are chosen as target signals due to stable response patterns across trials and are further divided into different oscillations. As a result, different taste qualities yield quality-specific LFPs in time domain which suggests the selectivity of this in vivo bioelectronic tongue. Meanwhile, more quantitative study in frequency domain indicates that the post-stimulation power of beta and low gamma oscillations shows dependence with concentrations of denatonium benzoate, a prototypical bitter compound, and the limit of detection is deduced to be 0.076 μM, which is two orders lower than previous in vitro bioelectronic tongues and conventional electronic tongues. According to the results, this in vivo bioelectronic tongue in combination with BMI presents a promising method in highly sensitive bitterness detection and is supposed to provide new platform in measuring bitterness degree. Copyright © 2015 Elsevier B.V. All rights reserved.

  7. Errare machinale est: the use of error-related potentials in brain-machine interfaces

    Science.gov (United States)

    Chavarriaga, Ricardo; Sobolewski, Aleksander; Millán, José del R.

    2014-01-01

    The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these signals to improve human-computer interaction (HCI) or brain-machine interfacing (BMI). Here, we present a review of over a decade of developments toward this goal. This body of work provides consistent evidence that ErrPs can be successfully detected on a single-trial basis, and that they can be effectively used in both HCI and BMI applications. We first describe the ErrP phenomenon and follow up with an analysis of different strategies to increase the robustness of a system by incorporating single-trial ErrP recognition, either by correcting the machine's actions or by providing means for its error-based adaptation. These approaches can be applied both when the user employs traditional HCI input devices or in combination with another BMI channel. Finally, we discuss the current challenges that have to be overcome in order to fully integrate ErrPs into practical applications. This includes, in particular, the characterization of such signals during real(istic) applications, as well as the possibility of extracting richer information from them, going beyond the time-locked decoding that dominates current approaches. PMID:25100937

  8. 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.

  9. Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex.

    Science.gov (United States)

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

    2015-01-01

    Brain machine interfaces (BMIs) aim to assist people with paralysis by increasing their independence and ability to communicate, e.g., by using a cursor-based virtual keyboard. Current BMI clinical trials are hampered by modest performance that causes selection of wrong characters (errors) and thus reduces achieved typing rate. If it were possible to detect these errors without explicit knowledge of the task goal, this could be used to automatically "undo" wrong selections or even prevent upcoming wrong selections. We decoded imminent or recent errors during closed-loop BMI control from intracortical spiking neural activity. In our experiment, a non-human primate controlled a neurally-driven BMI cursor to acquire targets on a grid, which simulates a virtual keyboard. In offline analyses of this closed-loop BMI control data, we identified motor cortical neural signals indicative of task error occurrence. We were able to detect task outcomes (97% accuracy) and even predict upcoming task outcomes (86% accuracy) using neural activity alone. This novel strategy may help increase the performance and clinical viability of BMIs.

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

    Science.gov (United States)

    Rouse, Adam G.

    2016-06-01

    Objective. 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. Approach. 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. Main results. 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. Significance. 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.

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

    Science.gov (United States)

    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.

  12. 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.

  13. Decoding hindlimb movement for a brain machine interface after a complete spinal transection.

    Science.gov (United States)

    Manohar, Anitha; Flint, Robert D; Knudsen, Eric; Moxon, Karen A

    2012-01-01

    Stereotypical locomotor movements can be made without input from the brain after a complete spinal transection. However, the restoration of functional gait requires descending modulation of spinal circuits to independently control the movement of each limb. To evaluate whether a brain-machine interface (BMI) could be used to regain conscious control over the hindlimb, rats were trained to press a pedal and the encoding of hindlimb movement was assessed using a BMI paradigm. Off-line, information encoded by neurons in the hindlimb sensorimotor cortex was assessed. Next neural population functions, or weighted representations of the neuronal activity, were used to replace the hindlimb movement as a trigger for reward in real-time (on-line decoding) in three conditions: while the animal could still press the pedal, after the pedal was removed and after a complete spinal transection. A novel representation of the motor program was learned when the animals used neural control to achieve water reward (e.g. more information was conveyed faster). After complete spinal transection, the ability of these neurons to convey information was reduced by more than 40%. However, this BMI representation was relearned over time despite a persistent reduction in the neuronal firing rate during the task. Therefore, neural control is a general feature of the motor cortex, not restricted to forelimb movements, and can be regained after spinal injury.

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

    Science.gov (United States)

    Alam, Monzurul; Chen, Xi; Zhang, Zicong; Li, Yan; He, Jufang

    2014-01-01

    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.

  15. Quantifying the role of motor imagery in brain-machine interfaces

    Science.gov (United States)

    Marchesotti, Silvia; Bassolino, Michela; Serino, Andrea; Bleuler, Hannes; Blanke, Olaf

    2016-04-01

    Despite technical advances in brain machine interfaces (BMI), for as-yet unknown reasons the ability to control a BMI remains limited to a subset of users. We investigate whether individual differences in BMI control based on motor imagery (MI) are related to differences in MI ability. We assessed whether differences in kinesthetic and visual MI, in the behavioral accuracy of MI, and in electroencephalographic variables, were able to differentiate between high- versus low-aptitude BMI users. High-aptitude BMI users showed higher MI accuracy as captured by subjective and behavioral measurements, pointing to a prominent role of kinesthetic rather than visual imagery. Additionally, for the first time, we applied mental chronometry, a measure quantifying the degree to which imagined and executed movements share a similar temporal profile. We also identified enhanced lateralized μ-band oscillations over sensorimotor cortices during MI in high- versus low-aptitude BMI users. These findings reveal that subjective, behavioral, and EEG measurements of MI are intimately linked to BMI control. We propose that poor BMI control cannot be ascribed only to intrinsic limitations of EEG recordings and that specific questionnaires and mental chronometry can be used as predictors of BMI performance (without the need to record EEG activity).

  16. 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.

  17. Errare machinale est: The use of error-related potentials in brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Ricardo eChavarriaga

    2014-07-01

    Full Text Available The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs. Consequently, it has been proposed to use these signals to improve human-computer interaction (HCI or brain-machine interfacing (BMI. Here, we present a review of over a decade of developments towards this goal. This body of work provides consistent evidence that ErrPs can be successfully detected on a single-trial basis, and that they can be effectively used in both HCI and BMI applications.We first describe the ErrP phenomenon and follow up with an analysis of different strategies to increase the robustness of a system by incorporating single-trial ErrP recognition, either by correcting the machine's actions or by providing means for its error-based adaptation. These approaches can be applied both when the user employs traditional HCI input devices or in combination with another BMI channel.Finally, we discuss the current challenges that have to be overcome in order to fully integrate ErrPs into practical applications. This includes, in particular, the characterization of such signals during real(istic applications, as well as the possibility of extracting richer information from them, going beyond the time-locked decoding that dominates current approaches.

  18. Recasting brain-machine interface design from a physical control system perspective.

    Science.gov (United States)

    Zhang, Yin; Chase, Steven M

    2015-10-01

    With the goal of improving the quality of life for people suffering from various motor control disorders, brain-machine interfaces provide direct neural control of prosthetic devices by translating neural signals into control signals. These systems act by reading motor intent signals directly from the brain and using them to control, for example, the movement of a cursor on a computer screen. Over the past two decades, much attention has been devoted to the decoding problem: how should recorded neural activity be translated into the movement of the cursor? Most approaches have focused on this problem from an estimation standpoint, i.e., decoders are designed to return the best estimate of motor intent possible, under various sets of assumptions about how the recorded neural signals represent motor intent. Here we recast the decoder design problem from a physical control system perspective, and investigate how various classes of decoders lead to different types of physical systems for the subject to control. This framework leads to new interpretations of why certain types of decoders have been shown to perform better than others. These results have implications for understanding how motor neurons are recruited to perform various tasks, and may lend insight into the brain's ability to conceptualize artificial systems.

  19. Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces.

    Science.gov (United States)

    Dangi, Siddharth; Orsborn, Amy L; Moorman, Helene G; Carmena, Jose M

    2013-07-01

    Closed-loop decoder adaptation (CLDA) is an emerging paradigm for achieving rapid performance improvements in online brain-machine interface (BMI) operation. Designing an effective CLDA algorithm requires making multiple important decisions, including choosing the timescale of adaptation, selecting which decoder parameters to adapt, crafting the corresponding update rules, and designing CLDA parameters. These design choices, combined with the specific settings of CLDA parameters, will directly affect the algorithm's ability to make decoder parameters converge to values that optimize performance. In this article, we present a general framework for the design and analysis of CLDA algorithms and support our results with experimental data of two monkeys performing a BMI task. First, we analyze and compare existing CLDA algorithms to highlight the importance of four critical design elements: the adaptation timescale, selective parameter adaptation, smooth decoder updates, and intuitive CLDA parameters. Second, we introduce mathematical convergence analysis using measures such as mean-squared error and KL divergence as a useful paradigm for evaluating the convergence properties of a prototype CLDA algorithm before experimental testing. By applying these measures to an existing CLDA algorithm, we demonstrate that our convergence analysis is an effective analytical tool that can ultimately inform and improve the design of CLDA algorithms.

  20. Improving brain-machine interface performance by decoding intended future movements

    Science.gov (United States)

    Willett, Francis R.; Suminski, Aaron J.; Fagg, Andrew H.; Hatsopoulos, Nicholas G.

    2013-04-01

    Objective. A brain-machine interface (BMI) records neural signals in real time from a subject's brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subject's intended movements a short time lead in the future. Approach. We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the user's intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen. Main Results. Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the user's future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads. Significance. This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user's future intent can compensate for the negative effect of control delay on BMI performance.

  1. Errare machinale est: the use of error-related potentials in brain-machine interfaces.

    Science.gov (United States)

    Chavarriaga, Ricardo; Sobolewski, Aleksander; Millán, José Del R

    2014-01-01

    The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these signals to improve human-computer interaction (HCI) or brain-machine interfacing (BMI). Here, we present a review of over a decade of developments toward this goal. This body of work provides consistent evidence that ErrPs can be successfully detected on a single-trial basis, and that they can be effectively used in both HCI and BMI applications. We first describe the ErrP phenomenon and follow up with an analysis of different strategies to increase the robustness of a system by incorporating single-trial ErrP recognition, either by correcting the machine's actions or by providing means for its error-based adaptation. These approaches can be applied both when the user employs traditional HCI input devices or in combination with another BMI channel. Finally, we discuss the current challenges that have to be overcome in order to fully integrate ErrPs into practical applications. This includes, in particular, the characterization of such signals during real(istic) applications, as well as the possibility of extracting richer information from them, going beyond the time-locked decoding that dominates current approaches.

  2. Cortical modulations increase in early sessions with brain-machine interface.

    Directory of Open Access Journals (Sweden)

    Miriam Zacksenhouse

    2007-07-01

    Full Text Available During planning and execution of reaching movements, the activity of cortical motor neurons is modulated by a diversity of motor, sensory, and cognitive signals. Brain-machine interfaces (BMIs extract part of these modulations to directly control artificial actuators. However, cortical modulations that emerge in the novel context of operating the BMI are poorly understood.Here we analyzed the changes in neuronal modulations that occurred in different cortical motor areas as monkeys learned to use a BMI to control reaching movements. Using spike-train analysis methods we demonstrate that the modulations of the firing-rates of cortical neurons increased abruptly after the monkeys started operating the BMI. Regression analysis revealed that these enhanced modulations were not correlated with the kinematics of the movement. The initial enhancement in firing rate modulations declined gradually with subsequent training in parallel with the improvement in behavioral performance.We conclude that the enhanced modulations are related to computational tasks that are significant especially in novel motor contexts. Although the function and neuronal mechanism of the enhanced cortical modulations are open for further inquiries, we discuss their potential role in processing execution errors and representing corrective or explorative activity. These representations are expected to contribute to the formation of internal models of the external actuator and their decoding may facilitate BMI improvement.

  3. Optimal calibration of the learning rate in closed-loop adaptive brain-machine interfaces.

    Science.gov (United States)

    Hsieh, Han-Lin; Shanechi, Maryam M

    2015-08-01

    Closed-loop decoder adaptation (CLDA) can improve brain-machine interface (BMI) performance. CLDA methods use batches of data to refit the decoder parameters in closed-loop operation. Recently, dynamic state-space algorithms have also been designed to fit the parameters of a point process decoder (PPF). A main design parameter that needs to be selected in any CLDA algorithm is the learning rate, i.e., how fast should the decoder parameters be updated on the basis of new neural observations. So far, the learning rate of CLDA algorithms has been selected empirically using ad-hoc methods. Here we develop a principled framework to calibrate the learning rate in adaptive state-space algorithms. The learning rate introduces a trade-off between the convergence rate and the steady-state error covariance of the estimated decoder parameters. Hence our algorithm first finds an analytical upper-bound on the steady-state error covariance as a function of the learning rate. It then finds the inverse mapping to select the optimal learning rate based on the maximum allowable steady-state error. Using numerical BMI experiments, we show that the calibration algorithm selects the optimal learning rate that meets the requirement on steady-state error level while achieving the fastest convergence rate possible corresponding to this steady-state level.

  4. Long-term, stable behavior of local field potentials during brain machine interface use.

    Science.gov (United States)

    Scheid, Michael R; Flint, Robert D; Wright, Zachary A; Slutzky, Marc W

    2013-01-01

    Local field potentials (LFPs) have the potential to provide robust, long-lasting control signals for brain-machine interfaces (BMIs). Moreover, they have been hypothesized to be a stable signal source. Here we assess the long-term stability of LFPs and multi-unit spikes (MSPs) in two monkeys using both LFP-based and MSP-based, biomimetic BMIs to control a computer cursor. The monkeys demonstrated highly accurate performance using both the LFP- and MSP-based BMIs. This performance remained high for 11 and 6 months, respectively, without adapting or retraining. We evaluated the stability of the LFP features and MSPs themselves by building, in each session, linear decoders of the BMI-controlled cursor velocity using single features or single MSPs. We then used these single-feature decoders to decode BMI-controlled cursor velocity in the last session. Many of the LFP features and MSPs showed stably-high correlations with the cursor velocity over the entire study period. This implies that the monkeys were able to maintain a stable mapping between either motor cortical field potentials or multi-spike potentials and BMI-controlled outputs.

  5. Training of a leaning agent for navigation--inspired by brain-machine interface.

    Science.gov (United States)

    Kitamura, Tadashi; Nishino, Daisuke

    2006-04-01

    The design clue for the remote control of a mobile robot is inspired by the Talwar's brain-machine interface technology for remotely training and controlling rats. Our biologically inspired autonomous robot control consciousness-based architecture (CBA) is used for the remote control of a robot as a substitute for a rat. CBA is a developmental hierarchy model of the relationship between consciousness and behavior, including a training algorithm. This training algorithm computes a shortcut path to a goal using a cognitive map created based on behavior obstructions during a single successful trial. However, failures in reaching the goal due to errors of the vision and dead reckoning sensors require human intervention to improve autonomous navigation. A human operator remotely intervenes in autonomous behaviors in two ways: low-level intervention in reflexive actions and high-level ones in the cognitive map. Experiments are conducted to test CBA functions for intervention with a joystick for a Khepera robot navigating from the center of a square obstacle with an open side toward a goal. Their statistical results show that both human interventions, especially high-level ones, are effective in drastically improving the success rate of autonomous detours.

  6. 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.

  7. Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control.

    Science.gov (United States)

    Iturrate, Iñaki; Chavarriaga, Ricardo; Montesano, Luis; Minguez, Javier; Millán, José del R

    2015-09-10

    Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. Here we demonstrate an alternative and complementary BMI paradigm that overcomes that limitation by decoding cognitive brain signals associated with monitoring processes relevant for achieving goals. In our approach the neuroprosthesis executes actions that the subject evaluates as erroneous or correct, and exploits the brain correlates of this assessment to learn suitable motor behaviours. Results show that, after a short user's training period, this teaching BMI paradigm operated three different neuroprostheses and generalized across several targets. Our results further support that these error-related signals reflect a task-independent monitoring mechanism in the brain, making this teaching paradigm scalable. We anticipate this BMI approach to become a key component of any neuroprosthesis that mimics natural motor control as it enables continuous adaptation in the absence of explicit information about goals. Furthermore, our paradigm can seamlessly incorporate other cognitive signals and conventional neuroprosthetic approaches, invasive or non-invasive, to enlarge the range and complexity of tasks that can be accomplished.

  8. Advancing brain-machine interfaces: Moving beyond linear state space models

    Directory of Open Access Journals (Sweden)

    Adam G Rouse

    2015-07-01

    Full Text Available Advances in recent years have dramatically improved output control by Brain-Machine Interfaces (BMIs. Such devices nevertheless remain robotic and limited in their movements compared to normal human motor performance. Most current BMIs rely on transforming recorded neural activity to a linear state space composed of a set number of fixed degrees of freedom. Here we consider a variety of ways in which BMI design might be advanced further by applying non-linear dynamics observed in normal motor behavior. We consider i the dynamic range and precision of natural movements, ii differences between cortical activity and actual body movement, iii kinematic and muscular synergies, and iv the implications of large neuronal populations. We advance the hypothesis that a given population of recorded neurons may transmit more useful information than can be captured by a single, linear model across all movement phases and contexts. We argue that incorporating these various non-linear characteristics will be an important next step in advancing BMIs to more closely match natural motor performance.

  9. Uniform and Non-uniform Perturbations in Brain-Machine Interface Task Elicit Similar Neural Strategies.

    Science.gov (United States)

    Armenta Salas, Michelle; Helms Tillery, Stephen I

    2016-01-01

    The neural mechanisms that take place during learning and adaptation can be directly probed with brain-machine interfaces (BMIs). We developed a BMI controlled paradigm that enabled us to enforce learning by introducing perturbations which changed the relationship between neural activity and the BMI's output. We introduced a uniform perturbation to the system, through a visuomotor rotation (VMR), and a non-uniform perturbation, through a decorrelation task. The controller in the VMR was essentially unchanged, but produced an output rotated at 30° from the neurally specified output. The controller in the decorrelation trials decoupled the activity of neurons that were highly correlated in the BMI task by selectively forcing the preferred directions of these cell pairs to be orthogonal. We report that movement errors were larger in the decorrelation task, and subjects needed more trials to restore performance back to baseline. During learning, we measured decreasing trends in preferred direction changes and cross-correlation coefficients regardless of task type. Conversely, final adaptations in neural tunings were dependent on the type controller used (VMR or decorrelation). These results hint to the similar process the neural population might engage while adapting to new tasks, and how, through a global process, the neural system can arrive to individual solutions.

  10. 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.

  11. 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

  12. 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.

  13. 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.

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

    OpenAIRE

    Eduardo López-Larraz; Eduardo López-Larraz; Fernando Trincado-Alonso; Vijaykumar Rajasekaran; Soraya Perez-Nombela; Antonio José Del-Ama; Joan Aranda; Javier Minguez; Javier Minguez; Javier Minguez; Ángel Gil-Agudo; Luis Montesano; Luis Montesano

    2016-01-01

    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 ...

  15. Social Robots, Brain Machine Interfaces and Neuro/Cognitive Enhancers: Three Emerging Science and Technology Products through the Lens of Technology Acceptance Theories, Models and Frameworks

    National Research Council Canada - National Science Library

    Gregor Wolbring; Lucy Diep; Sophya Yumakulov; Natalie Ball; Dean Yergens

    2013-01-01

      Social robotics, brain machine interfaces and neuro and cognitive enhancement products are three emerging science and technology products with wide-reaching impact for disabled and non-disabled people...

  16. 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.

  17. 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

  18. Brain-Machine-Interface in Chronic Stroke Rehabilitation: A Controlled Study

    Science.gov (United States)

    Ramos-Murguialday, Ander; Broetz, Doris; Rea, Massimiliano; Läer, Leonhard; Yilmaz, Özge; Brasil, Fabricio L; Liberati, Giulia; Curado, Marco R; Garcia-Cossio, Eliana; Vyziotis, Alexandros; Cho, Woosang; Agostini, Manuel; Soares, Ernesto; Soekadar, Surjo; Caria, Andrea; Cohen, Leonardo G; Birbaumer, Niels

    2013-01-01

    Objective Chronic stroke patients with severe hand weakness, respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine-interface training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double blind sham-controlled design proof of concept study. Methods 32 chronic stroke patients with severe hand weakness, were randomly assigned to two matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms (SMR) with contingent online movements of hand and arm orthoses (experimental group , n=16). In the control group (sham group, n=16) movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo-expectancy effects and functional magnetic resonance imaging (MRI) blood oxygenation level dependent activity were assessed before and after intervention. Results A significant group × time interaction in upper limb Fugl-Meyer motor (cFMA) scores was found. cFMA scores improved more in the experimental than in the control group, presenting a significant improvement of cFMA scores (3.41±0.563 points difference, p=0.018) reflecting a clinically meaningful change from no activity to some in paretic muscles. cFMA improvements in the experimental group correlated with changes in functional MRI laterality index and with paretic hand electromyography activity. Placebo-expectancy scores were comparable for both groups. Interpretation The addition of BMI training to behaviorally oriented physiotherapy can be used to induce functional improvements in motor function in chronic stroke patients without residual finger movements and may open a new door in stroke neurorehabilitation. PMID:23494615

  19. 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.

  20. A generalizable adaptive brain-machine interface design for control of anesthesia.

    Science.gov (United States)

    Yuxiao Yang; Shanechi, Maryam M

    2015-08-01

    Brain-machine interfaces (BMIs) for closed-loop control of anesthesia have the potential to automatically monitor and control brain states under anesthesia. Since a variety of anesthetic states are needed in different clinical scenarios, designing a generalizable BMI architecture that can control a wide range of anesthetic states is essential. In addition, drug dynamics are non-stationary over time and could change with the depth of anesthesia. Hence for precise control, a BMI needs to track these non-stationarities online. Here we design a BMI architecture that generalizes to control of various anesthetic states and their associated neural signatures, and is adaptive to time-varying drug dynamics. We provide a systematic approach to build general parametric models that quantify the anesthetic state and describe the drug dynamics. Based on these models, we develop an adaptive closed-loop controller within the framework of stochastic optimal feedback control. This controller tracks the non-stationarities in drug dynamics, achieves tight control in a time-varying environment, and removes the need for an offline system identification session. For robustness, the BMI also ensures small drug infusion rate variations at steady state. We test the BMI architecture for control of two common anesthetic states, i.e., burst suppression in medically-induced coma and unconsciousness in general anesthesia. Using numerical experiments, we find that the BMI generalizes to control of both these anesthetic states; in a time-varying environment, even without initial knowledge of model parameters, the BMI accurately controls these two different anesthetic states, reducing bias and error more than 70 times and 9 times, respectively, compared with a non-adaptive system.

  1. Brain-machine interface in chronic stroke rehabilitation: a controlled study.

    Science.gov (United States)

    Ramos-Murguialday, Ander; Broetz, Doris; Rea, Massimiliano; Läer, Leonhard; Yilmaz, Ozge; Brasil, Fabricio L; Liberati, Giulia; Curado, Marco R; Garcia-Cossio, Eliana; Vyziotis, Alexandros; Cho, Woosang; Agostini, Manuel; Soares, Ernesto; Soekadar, Surjo; Caria, Andrea; Cohen, Leonardo G; Birbaumer, Niels

    2013-07-01

    Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double-blind sham-controlled design proof of concept study. Thirty-two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo-expectancy effects, and functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent activity were assessed before and after intervention. A significant group × time interaction in upper limb (combined hand and modified arm) Fugl-Meyer assessment (cFMA) motor scores was found. cFMA scores improved more in the experimental than in the control group, presenting a significant improvement of cFMA scores (3.41 ± 0.563-point difference, p = 0.018) reflecting a clinically meaningful change from no activity to some in paretic muscles. cFMA improvements in the experimental group correlated with changes in fMRI laterality index and with paretic hand electromyography activity. Placebo-expectancy scores were comparable for both groups. The addition of BMI training to behaviorally oriented physiotherapy can be used to induce functional improvements in motor function in chronic stroke patients without residual finger movements and may open a new door in stroke neurorehabilitation. Copyright © 2013 American Neurological

  2. 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.

  3. 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

    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. 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. 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. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe that these results represent an important step

  4. 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 W; Osborne, Leslie C; Fagg, Andrew H; Oweiss, Karim G; Hatsopoulos, Nicholas G

    2017-12-13

    The development of coordinated reach to grasp has been well-studied in infants and children (Kuhtz-Buschbeck, Stolze, Jöhnk, Boczek-Funcke, & Illert, 1998; von Hofsten, 1984a). However, the role of motor cortex during this development is unclear because it is difficult to study in humans. We took the approach 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 (Qi, Stepniewska, & Kaas, 2000; Schieber & Deuel, 1997; Wu & Kaas, 1999), but prior BMI work has largely relied on finding neurons that already encode specific movement-related information. Here, 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 at the behavioral and cortical levels.

  5. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    Directory of Open Access Journals (Sweden)

    Eric A Pohlmeyer

    Full Text Available Brain-machine interface (BMI systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings. These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

  6. Motor cortical control of movement speed with implications for brain-machine interface control.

    Science.gov (United States)

    Golub, Matthew D; Yu, Byron M; Schwartz, Andrew B; Chase, Steven M

    2014-07-15

    Motor cortex plays a substantial role in driving movement, yet the details underlying this control remain unresolved. We analyzed the extent to which movement-related information could be extracted from single-trial motor cortical activity recorded while monkeys performed center-out reaching. Using information theoretic techniques, we found that single units carry relatively little speed-related information compared with direction-related information. This result is not mitigated at the population level: simultaneously recorded population activity predicted speed with significantly lower accuracy relative to direction predictions. Furthermore, a unit-dropping analysis revealed that speed accuracy would likely remain lower than direction accuracy, even given larger populations. These results suggest that the instantaneous details of single-trial movement speed are difficult to extract using commonly assumed coding schemes. This apparent paucity of speed information takes particular importance in the context of brain-machine interfaces (BMIs), which rely on extracting kinematic information from motor cortex. Previous studies have highlighted subjects' difficulties in holding a BMI cursor stable at targets. These studies, along with our finding of relatively little speed information in motor cortex, inspired a speed-dampening Kalman filter (SDKF) that automatically slows the cursor upon detecting changes in decoded movement direction. Effectively, SDKF enhances speed control by using prevalent directional signals, rather than requiring speed to be directly decoded from neural activity. SDKF improved success rates by a factor of 1.7 relative to a standard Kalman filter in a closed-loop BMI task requiring stable stops at targets. BMI systems enabling stable stops will be more effective and user-friendly when translated into clinical applications. Copyright © 2014 the American Physiological Society.

  7. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    Science.gov (United States)

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

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

  8. Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance

    Science.gov (United States)

    Christie, Breanne P.; Tat, Derek M.; Irwin, Zachary T.; Gilja, Vikash; Nuyujukian, Paul; Foster, Justin D.; Ryu, Stephen I.; Shenoy, Krishna V.; Thompson, David E.; Chestek, Cynthia A.

    2015-02-01

    Objective. For intracortical brain-machine interfaces (BMIs), action potential voltage waveforms are often sorted to separate out individual neurons. If these neurons contain independent tuning information, this process could increase BMI performance. However, the sorting of action potentials (‘spikes’) requires high sampling rates and is computationally expensive. To explicitly define the difference between spike sorting and alternative methods, we quantified BMI decoder performance when using threshold-crossing events versus sorted action potentials. Approach. We used data sets from 58 experimental sessions from two rhesus macaques implanted with Utah arrays. Data were recorded while the animals performed a center-out reaching task with seven different angles. For spike sorting, neural signals were sorted into individual units by using a mixture of Gaussians to cluster the first four principal components of the waveforms. For thresholding events, spikes that simply crossed a set threshold were retained. We decoded the data offline using both a Naïve Bayes classifier for reaching direction and a linear regression to evaluate hand position. Main results. We found the highest performance for thresholding when placing a threshold between -3 and -4.5 × Vrms. Spike sorted data outperformed thresholded data for one animal but not the other. The mean Naïve Bayes classification accuracy for sorted data was 88.5% and changed by 5% on average when data were thresholded. The mean correlation coefficient for sorted data was 0.92, and changed by 0.015 on average when thresholded. Significance. For prosthetics applications, these results imply that when thresholding is used instead of spike sorting, only a small amount of performance may be lost. The utilization of threshold-crossing events may significantly extend the lifetime of a device because these events are often still detectable once single neurons are no longer isolated.

  9. 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.

  10. A Wireless Brain-Machine Interface for Real-Time Speech Synthesis

    Science.gov (United States)

    Guenther, Frank H.; Brumberg, Jonathan S.; Wright, E. Joseph; Nieto-Castanon, Alfonso; Tourville, Jason A.; Panko, Mikhail; Law, Robert; Siebert, Steven A.; Bartels, Jess L.; Andreasen, Dinal S.; Ehirim, Princewill; Mao, Hui; Kennedy, Philip R.

    2009-01-01

    Background 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. Methodology/Principal Findings 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. Conclusions/Significance 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. PMID:20011034

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

    Science.gov (United States)

    Shanechi, Maryam M; Williams, Ziv M; Wornell, Gregory W; Hu, Rollin C; Powers, Marissa; Brown, Emery N

    2013-01-01

    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.

  12. 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.

  13. Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering.

    Directory of Open Access Journals (Sweden)

    Maryam M Shanechi

    2016-04-01

    Full Text Available Much progress has been made in brain-machine interfaces (BMI using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA. However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC model of the brain's behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user's motor intention during CLDA-a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was

  14. A Brain-Machine Interface for Control of Medically-Induced Coma

    Science.gov (United States)

    Liberman, Max; Solt, Ken; Brown, Emery N.

    2013-01-01

    Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95

  15. Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering.

    Science.gov (United States)

    Shanechi, Maryam M; Orsborn, Amy L; Carmena, Jose M

    2016-04-01

    Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain's behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user's motor intention during CLDA-a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter

  16. Minimizing data transfer with sustained performance in wireless brain-machine interfaces.

    Science.gov (United States)

    Thorbergsson, Palmi Thor; Garwicz, Martin; Schouenborg, Jens; Johansson, Anders J

    2012-06-01

    Brain-machine interfaces (BMIs) may be used to investigate neural mechanisms or to treat the symptoms of neurological disease and are hence powerful tools in research and clinical practice. Wireless BMIs add flexibility to both types of applications by reducing movement restrictions and risks associated with transcutaneous leads. However, since wireless implementations are typically limited in terms of transmission capacity and energy resources, the major challenge faced by their designers is to combine high performance with adaptations to limited resources. Here, we have identified three key steps in dealing with this challenge: (1) the purpose of the BMI should be clearly specified with regard to the type of information to be processed; (2) the amount of raw input data needed to fulfill the purpose should be determined, in order to avoid over- or under-dimensioning of the design; and (3) processing tasks should be allocated among the system parts such that all of them are utilized optimally with respect to computational power, wireless link capacity and raw input data requirements. We have focused on step (2) under the assumption that the purpose of the BMI (step 1) is to assess single- or multi-unit neuronal activity in the central nervous system with single-channel extracellular recordings. The reliability of this assessment depends on performance in detection and sorting of spikes. We have therefore performed absolute threshold spike detection and spike sorting with the principal component analysis and fuzzy c-means on a set of synthetic extracellular recordings, while varying the sampling rate and resolution, noise level and number of target units, and used the known ground truth to quantitatively estimate the performance. From the calculated performance curves, we have identified the sampling rate and resolution breakpoints, beyond which performance is not expected to increase by more than 1-5%. We have then estimated the performance of alternative

  17. Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

    Science.gov (United States)

    Carmena, Jose M.

    2016-01-01

    Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to

  18. Designing the Instructional Interface.

    Science.gov (United States)

    Lohr, L. L.

    2000-01-01

    Designing the instructional interface is a challenging endeavor requiring knowledge and skills in instructional and visual design, psychology, human-factors, ergonomic research, computer science, and editorial design. This paper describes the instructional interface, the challenges of its development, and an instructional systems approach to its…

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

    Directory of Open Access Journals (Sweden)

    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.

  20. Brain-machine interface facilitated neurorehabilitation via spinal stimulation after spinal cord injury: Recent progress and future perspectives.

    Science.gov (United States)

    Alam, Monzurul; Rodrigues, Willyam; Pham, Bau Ngoc; Thakor, Nitish V

    2016-09-01

    Restoration of motor function is one of the highest priorities in individuals afflicted with spinal cord injury (SCI). The application of brain-machine interfaces (BMIs) to neuroprostheses provides an innovative approach to treat patients with sensorimotor impairments. A BMI decodes motor intent from cortical signals to control external devices such as a computer cursor or a robotic arm. Recent BMI systems can now use these motor intent signals to directly activate paretic muscles or to modulate the spinal cord in a way that reengage dormant neuromuscular systems below the level of injury. In this perspective, we review the progress made in the development of brain-machine-spinal-cord interfaces (BMSCIs) and highlight their potential for neurorehabilitation after SCI. The advancement and application of these neuroprostheses goes beyond improved motor control. The use of BMSCI may combine repetitive physical training along with intent-driven neuromodulation to promote neurorehabilitation by facilitating activity-dependent plasticity. Strong evidence suggests that proper timing of volitional neuromodulation facilitates long-term potentiation in the neuronal circuits that can promote permanent functional recovery in SCI subjects. However, the effectiveness of these implantable neuroprostheses must take into account the fact that there will be continuous changes in the interface between the signals of intent and the actual trigger to initiate the motor action. Copyright © 2016 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    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

  2. Brain-machine interface via real-time fMRI: preliminary study on thought-controlled robotic arm.

    Science.gov (United States)

    Lee, Jong-Hwan; Ryu, Jeongwon; Jolesz, Ferenc A; Cho, Zang-Hee; Yoo, Seung-Schik

    2009-01-23

    Real-time functional MRI (rtfMRI) has been used as a basis for brain-computer interface (BCI) due to its ability to characterize region-specific brain activity in real-time. As an extension of BCI, we present an rtfMRI-based brain-machine interface (BMI) whereby 2-dimensional movement of a robotic arm was controlled by the regulation (and concurrent detection) of regional cortical activations in the primary motor areas. To do so, the subjects were engaged in the right- and/or left-hand motor imagery tasks. The blood oxygenation level dependent (BOLD) signal originating from the corresponding hand motor areas was then translated into horizontal or vertical robotic arm movement. The movement was broadcasted visually back to the subject as a feedback. We demonstrated that real-time control of the robotic arm only through the subjects' thought processes was possible using the rtfMRI-based BMI trials.

  3. 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 (pbrain-machine interface system is feasible for use in real-world clinical settings.

  4. Motor cortical prediction of EMG: evidence that a kinetic brain-machine interface may be robust across altered movement dynamics.

    Science.gov (United States)

    Cherian, A; Krucoff, M O; Miller, L E

    2011-08-01

    During typical movements, signals related to both the kinematics and kinetics of movement are mutually correlated, and each is correlated to some extent with the discharge of neurons in the primary motor cortex (M1). However, it is well known, if not always appreciated, that causality cannot be inferred from correlations. Although these mutual correlations persist, their nature changes with changing postural or dynamical conditions. Under changing conditions, only signals directly controlled by M1 can be expected to maintain a stable relationship with its discharge. If one were to rely on noncausal correlations for a brain-machine interface, its generalization across conditions would likely suffer. We examined this effect, using multielectrode recordings in M1 as input to linear decoders of both end point kinematics (position and velocity) and proximal limb myoelectric signals (EMG) during reaching. We tested these decoders across tasks that altered either the posture of the limb or the end point forces encountered during movement. Within any given task, the accuracy of the kinematic predictions tended to be somewhat better than the EMG predictions. However, when we used the decoders developed under one task condition to predict the signals recorded under different postural or dynamical conditions, only the EMG decoders consistently generalized well. Our results support the view that M1 discharge is more closely related to kinetic variables like EMG than it is to limb kinematics. These results suggest that brain-machine interface applications using M1 to control kinetic variables may prove to be more successful than the more standard kinematic approach.

  5. 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.

  6. 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'.

  7. Estimation of neuromuscular primitives from EEG slow cortical potentials in incomplete spinal cord injury individuals for a new class of brain-machine interfaces

    NARCIS (Netherlands)

    Úbeda, Andrés; Azorín, Jose M.; Farina, Dario; Sartori, Massimo

    2018-01-01

    One of the current challenges in human motor rehabilitation is the robust application of Brain-Machine Interfaces to assistive technologies such as powered lower limb exoskeletons. Reliable decoding of motor intentions and accurate timing of the robotic device actuation is fundamental to optimally

  8. Residual upper arm motor function primes innervation of paretic forearm muscles in chronic stroke after Brain-Machine Interface (BMI) training

    NARCIS (Netherlands)

    Curado, M.R.; Garcia Cossio, E.; Brötz, D.; Agostini, M.; Cho, W.; Brasil, F.L.; Yilmaz, O.; Liberati, G.; Lepski, G.; Birbaumer, N.; Ramos-Murguialday, A.

    2015-01-01

    Background Abnormal upper arm-forearm muscle synergies after stroke are poorly understood. We investigated whether upper arm function primes paralyzed forearm muscles in chronic stroke patients after Brain-Machine Interface (BMI)-based rehabilitation. Shaping upper arm-forearm muscle synergies may

  9. 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.

  10. Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control.

    Science.gov (United States)

    Flint, Robert D; Scheid, Michael R; Wright, Zachary A; Solla, Sara A; Slutzky, Marc W

    2016-03-23

    The human motor system is capable of remarkably precise control of movements--consider the skill of professional baseball pitchers or surgeons. This precise control relies upon stable representations of movements in the brain. Here, we investigated the stability of cortical activity at multiple spatial and temporal scales by recording local field potentials (LFPs) and action potentials (multiunit spikes, MSPs) while two monkeys controlled a cursor either with their hand or directly from the brain using a brain-machine interface. LFPs and some MSPs were remarkably stable over time periods ranging from 3 d to over 3 years; overall, LFPs were significantly more stable than spikes. We then assessed whether the stability of all neural activity, or just a subset of activity, was necessary to achieve stable behavior. We showed that projections of neural activity into the subspace relevant to the task (the "task-relevant space") were significantly more stable than were projections into the task-irrelevant (or "task-null") space. This provides cortical evidence in support of the minimum intervention principle, which proposes that optimal feedback control (OFC) allows the brain to tightly control only activity in the task-relevant space while allowing activity in the task-irrelevant space to vary substantially from trial to trial. We found that the brain appears capable of maintaining stable movement representations for extremely long periods of time, particularly so for neural activity in the task-relevant space, which agrees with OFC predictions. It is unknown whether cortical signals are stable for more than a few weeks. Here, we demonstrate that motor cortical signals can exhibit high stability over several years. This result is particularly important to brain-machine interfaces because it could enable stable performance with infrequent recalibration. Although we can maintain movement accuracy over time, movement components that are unrelated to the goals of a task (such

  11. 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.

  12. Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

    Science.gov (United States)

    Panzeri, Stefano; Safaai, Houman; De Feo, Vito; Vato, Alessandro

    2016-01-01

    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 brain. 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.

  13. 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.

  14. A Hardware-Efficient Scalable Spike Sorting Neural Signal Processor Module for Implantable High-Channel-Count Brain Machine Interfaces.

    Science.gov (United States)

    Yang, Yuning; Boling, Sam; Mason, Andrew J

    2017-08-01

    Next-generation brain machine interfaces demand a high-channel-count neural recording system to wirelessly monitor activities of thousands of neurons. A hardware efficient neural signal processor (NSP) is greatly desirable to ease the data bandwidth bottleneck for a fully implantable wireless neural recording system. This paper demonstrates a complete multichannel spike sorting NSP module that incorporates all of the necessary spike detector, feature extractor, and spike classifier blocks. To meet high-channel-count and implantability demands, each block was designed to be highly hardware efficient and scalable while sharing resources efficiently among multiple channels. To process multiple channels in parallel, scalability analysis was performed, and the utilization of each block was optimized according to its input data statistics and the power, area and/or speed of each block. Based on this analysis, a prototype 32-channel spike sorting NSP scalable module was designed and tested on an FPGA using synthesized datasets over a wide range of signal to noise ratios. The design was mapped to 130 nm CMOS to achieve 0.75 μW power and 0.023 mm2 area consumptions per channel based on post synthesis simulation results, which permits scalability of digital processing to 690 channels on a 4×4 mm2 electrode array.

  15. Brain-Machine Interface to Control a Prosthetic Arm with Monkey ECoGs during Periodic Movements

    Directory of Open Access Journals (Sweden)

    Soichiro eMorishita

    2014-12-01

    Full Text Available Brain Machine Interfaces (BMIs are promising technologies to rehabilitate the function of upper limbs in severely paralyzed patients. We succeeded in developing a BMI prosthetic arm for a monkey implanted with electrocorticogram (ECoG electrodes and trained in a reaching task. It had stability in preventing the misclassification of ECoG patterns. However, the latency was about 200 ms as a trade-off for the stability. To improve the response of this BMI prosthetic arm, the generation of a trigger event by decoding muscle activity was adopted. It was performed to predict integrated electromyograms (iEMGs from the ECoGs. Experiments were conducted to verify the availability of this method, and the results confirmed that the proposed method was superior to the conventional one. In addition, a performance test of the proposed method with actually achieved iEMGs instead of predicted iEMGs was performed, and we found that the motor intention is finely expressed through estimated muscle activity from brain activity rather than actual muscle activity.

  16. Changes in cortical network connectivity with long-term brain-machine interface exposure after chronic amputation.

    Science.gov (United States)

    Balasubramanian, Karthikeyan; Vaidya, Mukta; Southerland, Joshua; Badreldin, Islam; Eleryan, Ahmed; Takahashi, Kazutaka; Qian, Kai; Slutzky, Marc W; Fagg, Andrew H; Oweiss, Karim; Hatsopoulos, Nicholas G

    2017-11-27

    Studies on neural plasticity associated with brain-machine interface (BMI) exposure have primarily documented changes in single neuron activity, and largely in intact subjects. Here, we demonstrate significant changes in ensemble-level functional connectivity among primary motor cortical (MI) neurons of chronically amputated monkeys exposed to control a multiple-degree-of-freedom robot arm. A multi-electrode array was implanted in M1 contralateral or ipsilateral to the amputation in three animals. Two clusters of stably recorded neurons were arbitrarily assigned to control reach and grasp movements, respectively. With exposure, network density increased in a nearly monotonic fashion in the contralateral monkeys, whereas the ipsilateral monkey pruned the existing network before re-forming a denser connectivity. Excitatory connections among neurons within a cluster were denser, whereas inhibitory connections were denser among neurons across the two clusters. These results indicate that cortical network connectivity can be modified with BMI learning, even among neurons that have been chronically de-efferented and de-afferented due to amputation.

  17. Development of an implantable wireless ECoG 128ch recording device for clinical brain machine interface.

    Science.gov (United States)

    Matsushita, Kojiro; Hirata, Masayuki; Suzuki, Takafumi; Ando, Hiroshi; Ota, Yuki; Sato, Fumihiro; Morris, Shyne; Yoshida, Takeshi; Matsuki, Hidetoshi; Yoshimine, Toshiki

    2013-01-01

    Brain Machine Interface (BMI) is a system that assumes user's intention by analyzing user's brain activities and control devices with the assumed intention. It is considered as one of prospective tools to enhance paralyzed patients' quality of life. In our group, we especially focus on ECoG (electro-corti-gram)-BMI, which requires surgery to place electrodes on the cortex. We try to implant all the devices within the patient's head and abdomen and to transmit the data and power wirelessly. Our device consists of 5 parts: (1) High-density multi-electrodes with a 3D shaped sheet fitting to the individual brain surface to effectively record the ECoG signals; (2) A small circuit board with two integrated circuit chips functioning 128 [ch] analogue amplifiers and A/D converters for ECoG signals; (3) A Wifi data communication & control circuit with the target PC; (4) A non-contact power supply transmitting electrical power minimum 400[mW] to the device 20[mm] away. We developed those devices, integrated them, and, investigated the performance.

  18. 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.

  19. Brain-machine interface to control a prosthetic arm with monkey ECoGs during periodic movements.

    Science.gov (United States)

    Morishita, Soichiro; Sato, Keita; Watanabe, Hidenori; Nishimura, Yukio; Isa, Tadashi; Kato, Ryu; Nakamura, Tatsuhiro; Yokoi, Hiroshi

    2014-01-01

    Brain-machine interfaces (BMIs) are promising technologies for rehabilitation of upper limb functions in patients with severe paralysis. We previously developed a BMI prosthetic arm for a monkey implanted with electrocorticography (ECoG) electrodes, and trained it in a reaching task. The stability of the BMI prevented incorrect movements due to misclassification of ECoG patterns. As a trade-off for the stability, however, the latency (the time gap between the monkey's actual motion and the prosthetic arm movement) was about 200 ms. Therefore, in this study, we aimed to improve the response time of the BMI prosthetic arm. We focused on the generation of a trigger event by decoding muscle activity in order to predict integrated electromyograms (iEMGs) from the ECoGs. We verified the achievability of our method by conducting a performance test of the proposed method with actual achieved iEMGs instead of predicted iEMGs. Our results confirmed that the proposed method with predicted iEMGs eliminated the time delay. In addition, we found that motor intention is better reflected by muscle activity estimated from brain activity rather than actual muscle activity. Therefore, we propose that using predicted iEMGs to guide prosthetic arm movement results in minimal delay and excellent performance.

  20. Electroencephalography-based real-time cortical monitoring system that uses hierarchical Bayesian estimations for the brain-machine interface.

    Science.gov (United States)

    Choi, Kyuwan

    2014-06-01

    In this study, a real-time cortical activity monitoring system was constructed, which could estimate cortical activities every 125 milliseconds over 2,240 vertexes from 64 channel electroencephalography signals through the Hierarchical Bayesian estimation that uses functional magnetic resonance imaging data as its prior information. Recently, functional magnetic resonance imaging has mostly been used in the neurofeedback field because it allows for high spatial resolution. However, in functional magnetic resonance imaging, the time for the neurofeedback information to reach the patient is delayed several seconds because of its poor temporal resolution. Therefore, a number of problems need to be solved to effectively implement feedback training paradigms in patients. To address this issue, this study used a new cortical activity monitoring system that improved both spatial and temporal resolution by using both functional magnetic resonance imaging data and electroencephalography signals in conjunction with one another. This system is advantageous as it can improve applications in the fields of real-time diagnosis, neurofeedback, and the brain-machine interface.

  1. A novel bioelectronic nose based on brain-machine interface using implanted electrode recording in vivo in olfactory bulb.

    Science.gov (United States)

    Dong, Qi; Du, Liping; Zhuang, Liujing; Li, Rong; Liu, Qingjun; Wang, Ping

    2013-11-15

    The mammalian olfactory system has merits of higher sensitivity, selectivity and faster response than current electronic nose system based on chemical sensor array. It is advanced and feasible to detect and discriminate odors by mammalian olfactory system. The purpose of this study is to develop a novel bioelectronic nose based on the brain-machine interface (BMI) technology for odor detection by in vivo electrophysiological measurements of olfactory bulb. In this work, extracellular potentials of mitral/tufted (M/T) cells in olfactory bulb (OB) were recorded by implanted 16-channel microwire electrode arrays. The odor-evoked response signals were analyzed. We found that neural activities of different neurons showed visible different firing patterns both in temporal features and rate features when stimulated by different small molecular odorants. The detection low limit is below 1 ppm for some specific odors. Odors were classified by an algorithm based on population vector similarity and support vector machine (SVM). The results suggested that the novel bioelectonic nose was sensitive to odorant stimuli. The best classifying accuracy was up to 95%. With the development of the BMI and olfactory decoding methods, we believe that this system will represent emerging and promising platforms for wide applications in medical diagnosis and security fields. Copyright © 2013. Published by Elsevier B.V.

  2. 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.

  3. A classification method of different motor imagery tasks based on fractal features for brain-machine interface.

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    2009-03-01

    The objective of this study is to classify spontaneous electroencephalogram (EEG) signal on the basis of fractal concepts. Four motor imagery tasks (left hand movement, right hand movement, feet movement, and tongue movement) were investigated for each EEG recording session. Ten subjects volunteered to participate in this study. As we known, fractal geometry is a mathematical tool for dealing with complex systems like EEG signal. Therefore, we used the fractal dimension (FD) as feature for the application of brain-machine interface (BMI). Effective algorithm, namely, detrended fluctuation analysis (DFA) has been selected to estimate embedded FD values between relaxing and imaging states of the recorded EEG signal. To show the pattern of FDs, we propose a windowing-based method or also called time-dependent fractal dimension (TDFD) and the Kullback-Leibler (K-L) divergence. The K-L divergence and different expected values are employed as the input parameters of classifier. Finally, featured data are classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Experimental results show that the proposed method is more effective than the conventional methods.

  4. 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.

  5. Social Robots, Brain Machine Interfaces and Neuro/Cognitive Enhancers: Three Emerging Science and Technology Products through the Lens of Technology Acceptance Theories, Models and Frameworks

    Directory of Open Access Journals (Sweden)

    Gregor Wolbring

    2013-06-01

    Full Text Available Social robotics, brain machine interfaces and neuro and cognitive enhancement products are three emerging science and technology products with wide-reaching impact for disabled and non-disabled people. Acceptance of ideas and products depend on multiple parameters and many models have been developed to predict product acceptance. We investigated which frequently employed technology acceptance models (consumer theory, innovation diffusion model, theory of reasoned action, theory of planned behaviour, social cognitive theory, self-determination theory, technology of acceptance model, Unified Theory of Acceptance and Use of Technology UTAUT and UTAUT2 are employed in the social robotics, brain machine interfaces and neuro and cognitive enhancement product literature and which of the core measures used in the technology acceptance models are implicit or explicit engaged with in the literature.

  6. 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.

  7. 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.

  8. A non-adhesive solid-gel electrode for a non-invasive brain-machine interface.

    Science.gov (United States)

    Toyama, Shigeru; Takano, Kouji; Kansaku, Kenji

    2012-01-01

    A non-invasive brain-machine interface (BMI) or brain-computer interface 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 electroencephalography (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 non-adhesive 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 h) 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.

  9. Performance measurement for brain-computer or brain-machine interfaces: a tutorial

    Science.gov (United States)

    Thompson, David E.; Quitadamo, Lucia R.; Mainardi, Luca; Rehman Laghari, Khalil ur; Gao, Shangkai; Kindermans, Pieter-Jan; Simeral, John D.; Fazel-Rezai, Reza; Matteucci, Matteo; Falk, Tiago H.; Bianchi, Luigi; Chestek, Cynthia A.; Huggins, Jane E.

    2014-06-01

    Objective. Brain-computer interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research. Approach. A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This paper contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop. Main results. Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their use to encourage uniform application between laboratories. Significance. Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field.

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

    Science.gov (United States)

    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.

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

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    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.

  12. Kinematic and neurophysiological consequences of an assisted-force-feedback brain-machine interface training: a case study

    Directory of Open Access Journals (Sweden)

    Stefano eSilvoni

    2013-11-01

    Full Text Available In a proof-of-principle prototypical demonstration we describe a new type of brain-machine interface (BMI paradigm for upper limb motor training. The proposed technique allows a fast contingent and proportionally modulated stimulation of afferent proprioceptive and motor output neural pathways using operant learning.Continuous and immediate assisted-feedback of force proportional to rolandic rhythm oscillations during actual movements was employed and illustrated with a single case experiment. One hemiplegic patient was trained for two weeks coupling somatosensory brain oscillations with force field control during a robot mediated centre-out motor task whose execution approaches movements of everyday life. The robot facilitated actual movements adding a modulated force directed to the target, thus providing a non-delayed proprioceptive feedback. Neuro-electric, kinematic and motor-behavioural measures were recorded in pre- and post-assessments without force assistance. Patient’s healthy arm was used as control since neither a placebo control was possible nor other control conditions. We observed a generalized and significant kinematic improvement in the affected arm and a spatial accuracy improvement in both arms, together with an increase and focalization of the somatosensory rhythm changes used to provide assisted-force-feedback. The interpretation of the neurophysiological and kinematic evidences reported here is strictly related to the repetition of the motor-task and the presence of the assisted-force-feedback. Results are described as systematic observations only, without firm conclusions about the effectiveness of the methodology. In this prototypical view, the design of appropriate control conditions is discussed. This study presents a novel operant-learning-based BMI-application for motor training coupling brain oscillations and force feedback during an actual movement.

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

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    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.

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

    Science.gov (United States)

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

    2014-01-01

    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 onthree 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.

  15. Subject-specific modulation of local field potential spectral power during brain-machine interface control in primates

    Science.gov (United States)

    So, Kelvin; Dangi, Siddharth; Orsborn, Amy L.; Gastpar, Michael C.; Carmena, Jose M.

    2014-04-01

    Objective. Intracortical brain-machine interfaces (BMIs) have predominantly utilized spike activity as the control signal. However, an increasing number of studies have shown the utility of local field potentials (LFPs) for decoding motor related signals. Currently, it is unclear how well different LFP frequencies can serve as features for continuous, closed-loop BMI control. Approach. We demonstrate 2D continuous LFP-based BMI control using closed-loop decoder adaptation, which adapts decoder parameters to subject-specific LFP feature modulations during BMI control. We trained two macaque monkeys to control a 2D cursor in a center-out task by modulating LFP power in the 0-150 Hz range. Main results. While both monkeys attained control, they used different strategies involving different frequency bands. One monkey primarily utilized the low-frequency spectrum (0-80 Hz), which was highly correlated between channels, and obtained proficient performance even with a single channel. In contrast, the other monkey relied more on higher frequencies (80-150 Hz), which were less correlated between channels, and had greater difficulty with control as the number of channels decreased. We then restricted the monkeys to use only various sub-ranges (0-40, 40-80, and 80-150 Hz) of the 0-150 Hz band. Interestingly, although both monkeys performed better with some sub-ranges than others, they were able to achieve BMI control with all sub-ranges after decoder adaptation, demonstrating broad flexibility in the frequencies that could potentially be used for LFP-based BMI control. Significance. Overall, our results demonstrate proficient, continuous BMI control using LFPs and provide insight into the subject-specific spectral patterns of LFP activity modulated during control.

  16. 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.

  17. Kinematic and neurophysiological consequences of an assisted-force-feedback brain-machine interface training: a case study.

    Science.gov (United States)

    Silvoni, Stefano; Cavinato, Marianna; Volpato, Chiara; Cisotto, Giulia; Genna, Clara; Agostini, Michela; Turolla, Andrea; Ramos-Murguialday, Ander; Piccione, Francesco

    2013-01-01

    In a proof-of-principle prototypical demonstration we describe a new type of brain-machine interface (BMI) paradigm for upper limb motor-training. The proposed technique allows a fast contingent and proportionally modulated stimulation of afferent proprioceptive and motor output neural pathways using operant learning. Continuous and immediate assisted-feedback of force proportional to rolandic rhythm oscillations during actual movements was employed and illustrated with a single case experiment. One hemiplegic patient was trained for 2 weeks coupling somatosensory brain oscillations with force-field control during a robot-mediated center-out motor-task whose execution approaches movements of everyday life. The robot facilitated actual movements adding a modulated force directed to the target, thus providing a non-delayed proprioceptive feedback. Neuro-electric, kinematic, and motor-behavioral measures were recorded in pre- and post-assessments without force assistance. Patient's healthy arm was used as control since neither a placebo control was possible nor other control conditions. We observed a generalized and significant kinematic improvement in the affected arm and a spatial accuracy improvement in both arms, together with an increase and focalization of the somatosensory rhythm changes used to provide assisted-force-feedback. The interpretation of the neurophysiological and kinematic evidences reported here is strictly related to the repetition of the motor-task and the presence of the assisted-force-feedback. Results are described as systematic observations only, without firm conclusions about the effectiveness of the methodology. In this prototypical view, the design of appropriate control conditions is discussed. This study presents a novel operant-learning-based BMI-application for motor-training coupling brain oscillations and force feedback during an actual movement.

  18. 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.

  19. High-performance brain-machine interface enabled by an adaptive optimal feedback-controlled point process decoder.

    Science.gov (United States)

    Shanechi, Maryam M; Orsborn, Amy; Moorman, Helene; Gowda, Suraj; Carmena, Jose M

    2014-01-01

    Brain-machine interface (BMI) performance has been improved using Kalman filters (KF) combined with closed-loop decoder adaptation (CLDA). CLDA fits the decoder parameters during closed-loop BMI operation based on the neural activity and inferred user velocity intention. These advances have resulted in the recent ReFIT-KF and SmoothBatch-KF decoders. Here we demonstrate high-performance and robust BMI control using a novel closed-loop BMI architecture termed adaptive optimal feedback-controlled (OFC) point process filter (PPF). Adaptive OFC-PPF allows subjects to issue neural commands and receive feedback with every spike event and hence at a faster rate than the KF. Moreover, it adapts the decoder parameters with every spike event in contrast to current CLDA techniques that do so on the time-scale of minutes. Finally, unlike current methods that rotate the decoded velocity vector, adaptive OFC-PPF constructs an infinite-horizon OFC model of the brain to infer velocity intention during adaptation. Preliminary data collected in a monkey suggests that adaptive OFC-PPF improves BMI control. OFC-PPF outperformed SmoothBatch-KF in a self-paced center-out movement task with 8 targets. This improvement was due to both the PPF's increased rate of control and feedback compared with the KF, and to the OFC model suggesting that the OFC better approximates the user's strategy. Also, the spike-by-spike adaptation resulted in faster performance convergence compared to current techniques. Thus adaptive OFC-PPF enabled proficient BMI control in this monkey.

  20. 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.

  1. A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes

    Science.gov (United States)

    Stavisky, Sergey D.; Kao, Jonathan C.; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.

    2015-06-01

    Objective. Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. Approach. Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. Main results. LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. Significance. These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.

  2. Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions.

    Science.gov (United States)

    Hortal, Enrique; Planelles, Daniel; Resquin, Francisco; Climent, José M; Azorín, José M; Pons, José L

    2015-10-17

    As a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes. In this work, a system based on a hybrid upper limb exoskeleton is used for neurological rehabilitation. Reaching movements are supported by the passive exoskeleton ArmeoSpring and FES. The movement execution is triggered by an EEG-based BMI. The BMI uses two different methods to interact with the exoskeleton from the user's brain activity. The first method relies on motor imagery tasks classification, whilst the second one is based on movement intention detection. Three healthy users and five patients with neurological conditions participated in the experiments to verify the usability of the system. Using the BMI based on motor imagery, healthy volunteers obtained an average accuracy of 82.9 ± 14.5 %, and patients obtained an accuracy of 65.3 ± 9.0 %, with a low False Positives rate (FP) (19.2 ± 10.4 % and 15.0 ± 8.4 %, respectively). On the other hand, by using the BMI based on detecting the arm movement intention, the average accuracy was 76.7 ± 13.2 % for healthy users and 71.6 ± 15.8 % for patients, with 28.7 ± 19.9 % and 21.2 ± 13.3 % of FP rate (healthy users and patients, respectively). The accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies. The advantage of this system is that the user is an active part of the rehabilitation procedure. The next step will be to verify what are the clinical benefits for the patients using this new rehabilitation procedure.

  3. 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

  4. Characterization of Artifacts produced by gel displacement on non-invasive Brain-Machine Interfaces during ambulation

    Directory of Open Access Journals (Sweden)

    Alvaro eCosta

    2016-02-01

    Full Text Available So far, Brain-Machine Interfaces (BMIs have been mainly used to study brain potentials during movement-free conditions. Recently, due to the emerging concern of improving rehabilitation therapies, these systems are also being used during gait experiments. Under this new condition, the evaluation of motion artifacts has become a critical point to assure the validity of the results obtained. Due to the high signal to noise ratio provided, the use of wet electrodes is a widely accepted technic to acquire electroencephalographic (EEG signals. To perform these recordings it is necessary to apply a conductive gel between the scalp and the electrodes. This work is focused on the study of gel displacements produced during ambulation and how they affect the amplitude of EEG signals. Data recorded during three ambulation conditions (gait training and one movement-free condition (BMI motor imagery task are compared to perform this study.Two phenomenons, manifested as unusual increases of the signals' amplitude, have been identified and characterized during this work. Results suggest that they are caused by abrupt changes on the conductivity between the electrode and the scalp due to gel displacement produced during ambulation and head movements. These artifacts significantly increase the Power Spectral Density (PSD of EEG recordings at all frequencies from 5 to 90 Hz, corresponding to the main bandwidth of electrocortical potentials. They should be taken into consideration before performing EEG recordings in order to asses the correct gel allocation and to avoid the use of electrodes on certain scalp areas depending on the experimental conditions.

  5. Characterization of Artifacts Produced by Gel Displacement on Non-invasive Brain-Machine Interfaces during Ambulation

    Science.gov (United States)

    Costa, Álvaro; Salazar-Varas, Rocio; Úbeda, Andrés; Azorín, José M.

    2016-01-01

    So far, Brain-Machine Interfaces (BMIs) have been mainly used to study brain potentials during movement-free conditions. Recently, due to the emerging concern of improving rehabilitation therapies, these systems are also being used during gait experiments. Under this new condition, the evaluation of motion artifacts has become a critical point to assure the validity of the results obtained. Due to the high signal to noise ratio provided, the use of wet electrodes is a widely accepted technic to acquire electroencephalographic (EEG signals). To perform these recordings it is necessary to apply a conductive gel between the scalp and the electrodes. This work is focused on the study of gel displacements produced during ambulation and how they affect the amplitude of EEG signals. Data recorded during three ambulation conditions (gait training) and one movement-free condition (BMI motor imagery task) are compared to perform this study. Two phenomenons, manifested as unusual increases of the signals' amplitude, have been identified and characterized during this work. Results suggest that they are caused by abrupt changes on the conductivity between the electrode and the scalp due to gel displacement produced during ambulation and head movements. These artifacts significantly increase the Power Spectral Density (PSD) of EEG recordings at all frequencies from 5 to 90 Hz, corresponding to the main bandwidth of electrocortical potentials. They should be taken into consideration before performing EEG recordings in order to asses the correct gel allocation and to avoid the use of electrodes on certain scalp areas depending on the experimental conditions. PMID:26941601

  6. Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping.

    Science.gov (United States)

    Downey, John E; Weiss, Jeffrey M; Muelling, Katharina; Venkatraman, Arun; Valois, Jean-Sebastien; Hebert, Martial; Bagnell, J Andrew; Schwartz, Andrew B; Collinger, Jennifer L

    2016-03-18

    Recent studies have shown that brain-machine interfaces (BMIs) offer great potential for restoring upper limb function. However, grasping objects is a complicated task and the signals extracted from the brain may not always be capable of driving these movements reliably. Vision-guided robotic assistance is one possible way to improve BMI performance. We describe a method of shared control where the user controls a prosthetic arm using a BMI and receives assistance with positioning the hand when it approaches an object. Two human subjects with tetraplegia used a robotic arm to complete object transport tasks with and without shared control. The shared control system was designed to provide a balance between BMI-derived intention and computer assistance. An autonomous robotic grasping system identified and tracked objects and defined stable grasp positions for these objects. The system identified when the user intended to interact with an object based on the BMI-controlled movements of the robotic arm. Using shared control, BMI controlled movements and autonomous grasping commands were blended to ensure secure grasps. Both subjects were more successful on object transfer tasks when using shared control compared to BMI control alone. Movements made using shared control were more accurate, more efficient, and less difficult. One participant attempted a task with multiple objects and successfully lifted one of two closely spaced objects in 92 % of trials, demonstrating the potential for users to accurately execute their intention while using shared control. Integration of BMI control with vision-guided robotic assistance led to improved performance on object transfer tasks. Providing assistance while maintaining generalizability will make BMI systems more attractive to potential users. NCT01364480 and NCT01894802 .

  7. Towards a naturalistic brain-machine interface: hybrid torque and position control allows generalization to novel dynamics.

    Directory of Open Access Journals (Sweden)

    Pratik Y Chhatbar

    Full Text Available Realization of reaching and grasping movements by a paralytic person or an amputee would greatly facilitate her/his activities of daily living. Towards this goal, control of a computer cursor or robotic arm using neural signals has been demonstrated in rodents, non-human primates and humans. This technology is commonly referred to as a Brain-Machine Interface (BMI and is achieved by predictions of kinematic parameters, e.g. position or velocity. However, execution of natural movements, such as swinging baseball bats of different weights at the same speed, requires advanced planning for necessary context-specific forces in addition to kinematic control. Here we show, for the first time, the control of a virtual arm with representative inertial parameters using real-time neural control of torques in non-human primates (M. radiata. We found that neural control of torques leads to ballistic, possibly more naturalistic movements than position control alone, and that adding the influence of position in a hybrid torque-position control changes the feedforward behavior of these BMI movements. In addition, this level of control was achievable utilizing the neural recordings from either contralateral or ipsilateral M1. We also observed changed behavior of hybrid torque-position control under novel external dynamic environments that was comparable to natural movements. Our results demonstrate that inclusion of torque control to drive a neuroprosthetic device gives the user a more direct handle on the movement execution, especially when dealing with novel or changing dynamic environments. We anticipate our results to be a starting point of more sophisticated algorithms for sensorimotor neuroprostheses, eliminating the need of fully automatic kinematic-to-dynamic transformations as currently used by traditional kinematic-based decoders. Thus, we propose that direct control of torques, or other force related variables, should allow for more natural

  8. A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes.

    Science.gov (United States)

    Stavisky, Sergey D; Kao, Jonathan C; Nuyujukian, Paul; Ryu, Stephen I; Shenoy, Krishna V

    2015-06-01

    Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.

  9. A Study on the Effect of Electrical Stimulation as a User Stimuli for Motor Imagery Classification in Brain-Machine Interface.

    Science.gov (United States)

    Bhattacharyya, Saugat; Clerc, Maureen; Hayashibe, Mitsuhiro

    2016-06-13

    Functional Electrical Stimulation (FES) provides a neuroprosthetic interface to non-recovered muscle groups by stimulating the affected region of the human body. FES in combination with Brain-machine interfacing (BMI) has a wide scope in rehabilitation because this system directly links the cerebral motor intention of the users with its corresponding peripheral muscle activations. In this paper, we examine the effect of FES on the electroencephalography (EEG) during motor imagery (left- and right-hand movement) training of the users. Results suggest a significant improvement in the classification accuracy when the subject was induced with FES stimuli as compared to the standard visual one.

  10. 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

  11. 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

  12. A pelvic implant orthosis in rodents, for spinal cord injury rehabilitation, and for brain machine interface research: construction, surgical implantation and validation.

    Science.gov (United States)

    Udoekwere, Ubong Ime; Oza, Chintan S; Giszter, Simon F

    2014-01-30

    Rodents are important model systems used to explore spinal cord injury (SCI) and rehabilitation, and brain machine interfaces (BMI). We present a new method to provide mechanical interaction for BMI and rehabilitation in rat models of SCI. We present the design and implantation procedures for a pelvic orthosis that allows direct force application to the skeleton in brain machine interface and robot rehabilitation applications in rodents. We detail the materials, construction, machining, surgery and validation of the device. We describe the statistical validation of the implant procedures by comparing stepping parameters of 8 rats prior to and after implantation and surgical recovery. An ANOVA showed no effects of the implantation on stepping. Paired tests in the individual rats also showed no effect in 7/8 rats and minor effects in the last rat, within the group's variance. Our method allows interaction with rats at the pelvis without any perturbation of normal stepping in the intact rat. The method bypasses slings, and cuffs, avoiding cuff or slings squeezing the abdomen, or other altered sensory feedback. Our implant osseointegrates, and thus allows an efficient high bandwidth mechanical coupling to a robot. The implants support quadrupedal training and are readily integrated into either treadmill or overground contexts. Our novel device and procedures support a range of novel experimental designs and motor tests for rehabilitative and augmentation devices in intact and SCI model rats, with the advantage of allowing direct force application at the pelvic bones. Copyright © 2013 Elsevier B.V. All rights reserved.

  13. Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces

    Science.gov (United States)

    Úbeda, Andrés; Azorín, José M.; Farina, Dario; Sartori, Massimo

    2018-01-01

    One of the current challenges in human motor rehabilitation is the robust application of Brain-Machine Interfaces to assistive technologies such as powered lower limb exoskeletons. Reliable decoding of motor intentions and accurate timing of the robotic device actuation is fundamental to optimally enhance the patient's functional improvement. Several studies show that it may be possible to extract motor intentions from electroencephalographic (EEG) signals. These findings, although notable, suggests that current techniques are still far from being systematically applied to an accurate real-time control of rehabilitation or assistive devices. Here we propose the estimation of spinal primitives of multi-muscle control from EEG, using electromyography (EMG) dimensionality reduction as a solution to increase the robustness of the method. We successfully apply this methodology, both to healthy and incomplete spinal cord injury (SCI) patients, to identify muscle contraction during periodical knee extension from the EEG. We then introduce a novel performance metric, which accurately evaluates muscle primitive activations. PMID:29422842

  14. 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…

  15. Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients.

    Science.gov (United States)

    Donati, Ana R C; Shokur, Solaiman; Morya, Edgard; Campos, Debora S F; Moioli, Renan C; Gitti, Claudia M; Augusto, Patricia B; Tripodi, Sandra; Pires, Cristhiane G; Pereira, Gislaine A; Brasil, Fabricio L; Gallo, Simone; Lin, Anthony A; Takigami, Angelo K; Aratanha, Maria A; Joshi, Sanjay; Bleuler, Hannes; Cheng, Gordon; Rudolph, Alan; Nicolelis, Miguel A L

    2016-08-11

    Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3-13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual reality training, enriched visual-tactile feedback, and walking with two EEG-controlled robotic actuators, including a custom-designed lower limb exoskeleton capable of delivering tactile feedback to subjects. Following 12 months of training with this paradigm, all eight patients experienced neurological improvements in somatic sensation (pain localization, fine/crude touch, and proprioceptive sensing) in multiple dermatomes. Patients also regained voluntary motor control in key muscles below the SCI level, as measured by EMGs, resulting in marked improvement in their walking index. As a result, 50% of these patients were upgraded to an incomplete paraplegia classification. Neurological recovery was paralleled by the reemergence of lower limb motor imagery at cortical level. We hypothesize that this unprecedented neurological recovery results from both cortical and spinal cord plasticity triggered by long-term BMI usage.

  16. Brain-machine interfaces and transcranial stimulation: future implications for directing functional movement and improving function after spinal injury in humans.

    Science.gov (United States)

    Carmena, Jose M; Cohen, Leonardo G

    2012-01-01

    Since its origins, the primary goal of transforming thought into action and sensation into perception has been to improve the quality of life for the physically impaired. Brain-machine interfaces (BMI) aim to improve the quality of life for large numbers of neurological patients. In particular, this novel technology is meant to play a major role in the near future as a serious contribution to spinal cord rehabilitation. During the last decade we have witnessed a dramatic increase in BMI research with impressive demonstrations of rodents, nonhuman primates, and humans controlling robots, wheelchairs, and graphical cursors in real time through signals collected from the brain. In this chapter we first review the different techniques used in the field of BMI, including electroencephalography (EEG), electrocorticography (ECoG), magnetoencephalography (MEG), and chronic multielectrode recordings. In addition we review the use of transcranial magnetic stimulation (TMS) for noninvasive modulation of excitability in relatively focal cortical areas. The chapter concludes with a discussion on the future implications of BMIs for directing functional movement and improving function after spinal injury in humans. Copyright © 2012 Elsevier B.V. All rights reserved.

  17. Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients

    Science.gov (United States)

    Donati, Ana R. C.; Shokur, Solaiman; Morya, Edgard; Campos, Debora S. F.; Moioli, Renan C.; Gitti, Claudia M.; Augusto, Patricia B.; Tripodi, Sandra; Pires, Cristhiane G.; Pereira, Gislaine A.; Brasil, Fabricio L.; Gallo, Simone; Lin, Anthony A.; Takigami, Angelo K.; Aratanha, Maria A.; Joshi, Sanjay; Bleuler, Hannes; Cheng, Gordon; Rudolph, Alan; Nicolelis, Miguel A. L.

    2016-01-01

    Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3–13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual reality training, enriched visual-tactile feedback, and walking with two EEG-controlled robotic actuators, including a custom-designed lower limb exoskeleton capable of delivering tactile feedback to subjects. Following 12 months of training with this paradigm, all eight patients experienced neurological improvements in somatic sensation (pain localization, fine/crude touch, and proprioceptive sensing) in multiple dermatomes. Patients also regained voluntary motor control in key muscles below the SCI level, as measured by EMGs, resulting in marked improvement in their walking index. As a result, 50% of these patients were upgraded to an incomplete paraplegia classification. Neurological recovery was paralleled by the reemergence of lower limb motor imagery at cortical level. We hypothesize that this unprecedented neurological recovery results from both cortical and spinal cord plasticity triggered by long-term BMI usage. PMID:27513629

  18. Boosting performance in brain-machine interface by classifier-level fusion based on accumulative training models from multi-day data.

    Science.gov (United States)

    Huijuan Yang; Libedinsky, Camilo; Cuntai Guan; Kai Keng Ang; So, Rosa Q

    2017-07-01

    The nonstationarity of neural signal is still an unresolved issue despite the rapid progress made in brain-machine interface (BMI). This paper investigates how to utilize the rich information and dynamics in multi-day data to address the variability in day-to-day signal quality and neural tuning properties. For this purpose, we propose a classifier-level fusion technique to build a robust decoding model by jointly considering the classifier outputs from multiple base-training models using multi-day data collected prior to test day. The data set used in this study consisted of recordings of 8 days from a non-human primate (NHP) during control of a mobile robot using a joystick. Offline analysis demonstrates the superior performance of the proposed method which results in 4.4% and 13.10% improvements in decoding (significant by one-way ANOVA and post hoc t-test) compared with the two baseline methods: 1) concatenating data from multiple days based on common effective channels, and 2) averaging accuracies across all base-training models. These results further validate the effectiveness of proposed method without recalibration of the model.

  19. 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.

  20. Demonstration of a Semi-Autonomous Hybrid Brain-Machine Interface using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic

    Science.gov (United States)

    McMullen, David P.; Hotson, Guy; Katyal, Kapil D.; Wester, Brock A.; Fifer, Matthew S.; McGee, Timothy G.; Harris, Andrew; Johannes, Matthew S.; Vogelstein, R. Jacob; Ravitz, Alan D.; Anderson, William S.; Thakor, Nitish V.; Crone, Nathan E.

    2014-01-01

    To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 seconds for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs. PMID:24760914

  1. Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces

    Directory of Open Access Journals (Sweden)

    Andrés Úbeda

    2018-01-01

    Full Text Available One of the current challenges in human motor rehabilitation is the robust application of Brain-Machine Interfaces to assistive technologies such as powered lower limb exoskeletons. Reliable decoding of motor intentions and accurate timing of the robotic device actuation is fundamental to optimally enhance the patient's functional improvement. Several studies show that it may be possible to extract motor intentions from electroencephalographic (EEG signals. These findings, although notable, suggests that current techniques are still far from being systematically applied to an accurate real-time control of rehabilitation or assistive devices. Here we propose the estimation of spinal primitives of multi-muscle control from EEG, using electromyography (EMG dimensionality reduction as a solution to increase the robustness of the method. We successfully apply this methodology, both to healthy and incomplete spinal cord injury (SCI patients, to identify muscle contraction during periodical knee extension from the EEG. We then introduce a novel performance metric, which accurately evaluates muscle primitive activations.

  2. Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic.

    Science.gov (United States)

    McMullen, David P; Hotson, Guy; Katyal, Kapil D; Wester, Brock A; Fifer, Matthew S; McGee, Timothy G; Harris, Andrew; Johannes, Matthew S; Vogelstein, R Jacob; Ravitz, Alan D; Anderson, William S; Thakor, Nitish V; Crone, Nathan E

    2014-07-01

    To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.

  3. A Brain Machine Interface for command based control of a wheelchair using conditioning of oscillatory brain activity.

    Science.gov (United States)

    Hamad, Eyad M; Al-Gharabli, Samer I; Saket, Munib M; Jubran, Omar

    2017-07-01

    In this research a new method of wheelchair control using a Brain Computer Interface (BCI) is proposed, in an attempt to bridge the gap between in-lab and real life applications, we believe it would provide a high level control over the BCI instead of the normal low level commands. It is anticipated to emphasis on mu rhythm to provide the control signals. The wheelchair is equipped with a mapping system, which scans the area and provides a map containing information about the user's current location and next possible destinations, then provides an optimized list of possible trajectories to reach the destination. The paradigm allows users to control the interface using motor imagery and issue commands to switch between possible trajectories and then confirm the choice. Commands trigger the motion of the wheelchair to the intended destination using a user selected path with speed up to 0.5 m/s. The interface also allows the user to interact with different robots through a common robotic system. Evaluation results indicate that this paradigm is indeed usable and could lead to promising outcomes.

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

    Directory of Open Access Journals (Sweden)

    Yoshio eSakurai

    2014-02-01

    Full Text Available 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.

  5. 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.

  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.

    2017-01-01

    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 Weiner 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 classifier 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. Impact of Shoulder Abduction Loading on Brain-Machine Interface in Predicting Hand Opening and Closing in Individuals With Chronic Stroke.

    Science.gov (United States)

    Yao, Jun; Sheaff, Clay; Carmona, Carolina; Dewald, Julius P A

    2016-05-01

    Many individuals with moderate and severe stroke are unable to use their paretic hand. Currently, the effect of conventional therapy on regaining meaningful hand function in this population is limited. Efforts have been made to use brain-machine interfaces (BMIs) to control hand function. To date, almost all BMI classification algorithms are designed for detecting hand movements with a resting arm. However, many functional movements require simultaneous movements of the arm and hand. Arm movement will possibly affect the detection of intended hand movements, specifically for individuals with chronic stroke who have muscle synergies. The most prevalent upper-extremity synergy-flexor synergy-is expressed as an abnormal coupling between shoulder abductors and elbow/wrist/finger flexors. We hypothesized that because of flexor synergy, shoulder abductor activity would affect the detection of the hand-opening (a movement inhibited by flexion synergy) but not the hand-closing task (a movement facilitated by the flexion synergy). We evaluated the accuracy of a BMI classification algorithm in detecting hand-opening versus closing after reaching a target with 2 different shoulder-abduction loads in 6 individuals with stroke. We found a decreased accuracy in detecting hand opening when an individual with stroke intends to open the hand while activating shoulder abductors. However, such decreased accuracy with increased shoulder loading was not shown while detecting a hand-closing task. This study supports the idea that one should consider the effect of shoulder abduction activity when designing BMI classification algorithms for the purpose of restoring hand function in individuals with moderate to severe stroke. © The Author(s) 2015.

  8. 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

  9. 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

    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. 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. 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. 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 through the evolutionary algorithms. In addition

  10. A supplementary system for a brain-machine interface based on jaw artifacts for the bidimensional control of a robotic arm.

    Science.gov (United States)

    Costa, Álvaro; Hortal, Enrique; Iáñez, Eduardo; Azorín, José M

    2014-01-01

    Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user's brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large training time is required and the system needs to be adjusted on each session. This paper presents a supplementary system that employs BMI sensors, allowing the use of 2 systems (the BMI system and the supplementary system) with the same data acquisition device. This supplementary system is designed to control a robotic arm in two dimensions using electromyographical (EMG) signals extracted from the electroencephalographical (EEG) recordings. These signals are voluntarily produced by users clenching their jaws. EEG signals (with EMG contributions) were registered and analyzed to obtain the electrodes and the range of frequencies which provide the best classification results for 5 different clenching tasks. A training stage, based on the 2-dimensional control of a cursor, was designed and used by the volunteers to get used to this control. Afterwards, the control was extrapolated to a robotic arm in a 2-dimensional workspace. Although the training performed by volunteers requires 70 minutes, the final results suggest that in a shorter period of time (45 min), users should be able to control the robotic arm in 2 dimensions with their jaws. The designed system is compared with a similar 2-dimensional system based on spontaneous BMIs, and our system shows faster and more accurate performance. This is due to the nature of the control signals. Brain potentials are much more difficult to control than the electromyographical signals produced by jaw clenches. Additionally, the presented system also shows an improvement in the results compared with an electrooculographic system in a similar environment.

  11. Prospect of brain-machine interface in motor disabilities: the future support for multiple sclerosis patient to improve quality of life.

    Science.gov (United States)

    Khare, M; Singh, Av; Zamboni, P

    2014-05-01

    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 experimental exploration. The present review briefly explains the anomaly in neuron anatomy and dysfunction in signal transmission arising in the context with the chronic cerebrospinal venous insufficiency (CCSVI), a recent hypothesis related with MS pathophysiology. Subsequently, it insights brain-machine interface (BMI) as an alternative approach to improve the HRQoL of MS subjects. Information sources were searched from peer-reviewed data bases (Medline, BioMed Central, PubMed) and grey-literature databases for data published in 2000 or later. We also did systemic search in edited books, articles in seminar papers, reports extracted from newspapers and scientific magazines, articles accessed from internet; mostly using PubMed, Google search engine and Wikipedia. Out of approximately 178, 240 research articles obtained using selected keywords, those articles were included in the present study which addresses the latest definitions of HRQol and latest scientific and ethical developments in the research of MS and BMI. The article presented a brief survey of CCSVI mediated MS and BMI-approach as a treatment to serve the patients suffering from disabilities as a result of MS, followed by successful precedence of BMI approach. Apart from these, the major findings of selected research articles including the development of parameters to define HRQoL, types and development of BMIs and its role in interconnecting brain with actuators, along with CCSVI being a possible cause of MS have formed the foundations to conclude the findings of the present review article. We propose a perspective BMI approach and promises it holds for future research to improve HRQoL in

  12. Residual Upper Arm Motor Function Primes Innervation of Paretic Forearm Muscles in Chronic Stroke after Brain-Machine Interface (BMI) Training

    Science.gov (United States)

    Curado, Marco Rocha; Cossio, Eliana Garcia; Broetz, Doris; Agostini, Manuel; Cho, Woosang; Brasil, Fabricio Lima; Yilmaz, Oezge; Liberati, Giulia; Lepski, Guilherme

    2015-01-01

    Background Abnormal upper arm-forearm muscle synergies after stroke are poorly understood. We investigated whether upper arm function primes paralyzed forearm muscles in chronic stroke patients after Brain-Machine Interface (BMI)-based rehabilitation. Shaping upper arm-forearm muscle synergies may support individualized motor rehabilitation strategies. Methods Thirty-two chronic stroke patients with no active finger extensions were randomly assigned to experimental or sham groups and underwent daily BMI training followed by physiotherapy during four weeks. BMI sessions included desynchronization of ipsilesional brain activity and a robotic orthosis to move the paretic limb (experimental group, n = 16). In the sham group (n = 16) orthosis movements were random. Motor function was evaluated with electromyography (EMG) of forearm extensors, and upper arm and hand Fugl-Meyer assessment (FMA) scores. Patients performed distinct upper arm (e.g., shoulder flexion) and hand movements (finger extensions). Forearm EMG activity significantly higher during upper arm movements as compared to finger extensions was considered facilitation of forearm EMG activity. Intraclass correlation coefficient (ICC) was used to test inter-session reliability of facilitation of forearm EMG activity. Results Facilitation of forearm EMG activity ICC ranges from 0.52 to 0.83, indicating fair to high reliability before intervention in both limbs. Facilitation of forearm muscles is higher in the paretic as compared to the healthy limb (parm FMA scores predict facilitation of forearm muscles after intervention in both groups (significant correlations ranged from R = 0.752, p = 0.002 to R = 0.779, p = 0.001), but only in the experimental group upper arm FMA scores predict changes in facilitation of forearm muscles after intervention (R = 0.709, p = 0.002; R = 0.827, parm motor function primes recruitment of paralyzed forearm muscles in chronic stroke patients and predicts changes in their recruitment

  13. Residual Upper Arm Motor Function Primes Innervation of Paretic Forearm Muscles in Chronic Stroke after Brain-Machine Interface (BMI Training.

    Directory of Open Access Journals (Sweden)

    Marco Rocha Curado

    Full Text Available Abnormal upper arm-forearm muscle synergies after stroke are poorly understood. We investigated whether upper arm function primes paralyzed forearm muscles in chronic stroke patients after Brain-Machine Interface (BMI-based rehabilitation. Shaping upper arm-forearm muscle synergies may support individualized motor rehabilitation strategies.Thirty-two chronic stroke patients with no active finger extensions were randomly assigned to experimental or sham groups and underwent daily BMI training followed by physiotherapy during four weeks. BMI sessions included desynchronization of ipsilesional brain activity and a robotic orthosis to move the paretic limb (experimental group, n = 16. In the sham group (n = 16 orthosis movements were random. Motor function was evaluated with electromyography (EMG of forearm extensors, and upper arm and hand Fugl-Meyer assessment (FMA scores. Patients performed distinct upper arm (e.g., shoulder flexion and hand movements (finger extensions. Forearm EMG activity significantly higher during upper arm movements as compared to finger extensions was considered facilitation of forearm EMG activity. Intraclass correlation coefficient (ICC was used to test inter-session reliability of facilitation of forearm EMG activity.Facilitation of forearm EMG activity ICC ranges from 0.52 to 0.83, indicating fair to high reliability before intervention in both limbs. Facilitation of forearm muscles is higher in the paretic as compared to the healthy limb (p<0.001. Upper arm FMA scores predict facilitation of forearm muscles after intervention in both groups (significant correlations ranged from R = 0.752, p = 0.002 to R = 0.779, p = 0.001, but only in the experimental group upper arm FMA scores predict changes in facilitation of forearm muscles after intervention (R = 0.709, p = 0.002; R = 0.827, p<0.001.Residual upper arm motor function primes recruitment of paralyzed forearm muscles in chronic stroke patients and predicts changes in

  14. 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.

  15. Residual Upper Arm Motor Function Primes Innervation of Paretic Forearm Muscles in Chronic Stroke after Brain-Machine Interface (BMI) Training.

    Science.gov (United States)

    Curado, Marco Rocha; Cossio, Eliana Garcia; Broetz, Doris; Agostini, Manuel; Cho, Woosang; Brasil, Fabricio Lima; Yilmaz, Oezge; Liberati, Giulia; Lepski, Guilherme; Birbaumer, Niels; Ramos-Murguialday, Ander

    2015-01-01

    Abnormal upper arm-forearm muscle synergies after stroke are poorly understood. We investigated whether upper arm function primes paralyzed forearm muscles in chronic stroke patients after Brain-Machine Interface (BMI)-based rehabilitation. Shaping upper arm-forearm muscle synergies may support individualized motor rehabilitation strategies. Thirty-two chronic stroke patients with no active finger extensions were randomly assigned to experimental or sham groups and underwent daily BMI training followed by physiotherapy during four weeks. BMI sessions included desynchronization of ipsilesional brain activity and a robotic orthosis to move the paretic limb (experimental group, n = 16). In the sham group (n = 16) orthosis movements were random. Motor function was evaluated with electromyography (EMG) of forearm extensors, and upper arm and hand Fugl-Meyer assessment (FMA) scores. Patients performed distinct upper arm (e.g., shoulder flexion) and hand movements (finger extensions). Forearm EMG activity significantly higher during upper arm movements as compared to finger extensions was considered facilitation of forearm EMG activity. Intraclass correlation coefficient (ICC) was used to test inter-session reliability of facilitation of forearm EMG activity. Facilitation of forearm EMG activity ICC ranges from 0.52 to 0.83, indicating fair to high reliability before intervention in both limbs. Facilitation of forearm muscles is higher in the paretic as compared to the healthy limb (p<0.001). Upper arm FMA scores predict facilitation of forearm muscles after intervention in both groups (significant correlations ranged from R = 0.752, p = 0.002 to R = 0.779, p = 0.001), but only in the experimental group upper arm FMA scores predict changes in facilitation of forearm muscles after intervention (R = 0.709, p = 0.002; R = 0.827, p<0.001). Residual upper arm motor function primes recruitment of paralyzed forearm muscles in chronic stroke patients and predicts changes in their

  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. Interfaces for instructional use of simulations

    NARCIS (Netherlands)

    de Hoog, Robert; de Jong, Anthonius J.M.; de Vries, Frits

    1991-01-01

    The learner interface is the component of an instructional system that mediates between a learner and the system. Two fundamentally different approaches for interfaces can be distinguished: conversational methapor and direct manipulation metaphor. Interfaces in both metaphors can be scaled on a

  18. User Interface Improvements in Computer-Assisted Instruction, the Challenge.

    Science.gov (United States)

    Chalmers, P. A.

    2000-01-01

    Identifies user interface problems as they relate to computer-assisted instruction (CAI); reviews the learning theories and instructional theories related to CAI user interface; and presents potential CAI user interface improvements for research and development based on learning and instructional theory. Focuses on screen design improvements.…

  19. A brain-machine interface to navigate a mobile robot in a planar workspace: enabling humans to fly simulated aircraft with EEG.

    Science.gov (United States)

    Akce, Abdullah; Johnson, Miles; Dantsker, Or; Bretl, Timothy

    2013-03-01

    This paper presents an interface for navigating a mobile robot that moves at a fixed speed in a planar workspace, with noisy binary inputs that are obtained asynchronously at low bit-rates from a human user through an electroencephalograph (EEG). The approach is to construct an ordered symbolic language for smooth planar curves and to use these curves as desired paths for a mobile robot. The underlying problem is then to design a communication protocol by which the user can, with vanishing error probability, specify a string in this language using a sequence of inputs. Such a protocol, provided by tools from information theory, relies on a human user's ability to compare smooth curves, just like they can compare strings of text. We demonstrate our interface by performing experiments in which twenty subjects fly a simulated aircraft at a fixed speed and altitude with input only from EEG. Experimental results show that the majority of subjects are able to specify desired paths despite a wide range of errors made in decoding EEG signals.

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

    Science.gov (United States)

    Brauchle, Daniel; Vukelić, Mathias; Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    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 reaching movements with a multi-joint exoskeleton during motor imagery (MI)-related desynchronization of sensorimotor oscillations in the β-band. 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 (BRIs) 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, a proposal that warrants

  1. 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.

  2. A new therapeutic application of brain-machine interface (BMI) training followed by hybrid assistive neuromuscular dynamic stimulation (HANDS) therapy for patients with severe hemiparetic stroke: A proof of concept study.

    Science.gov (United States)

    Kawakami, Michiyuki; Fujiwara, Toshiyuki; Ushiba, Junichi; Nishimoto, Atsuko; Abe, Kaoru; Honaga, Kaoru; Nishimura, Atsuko; Mizuno, Katsuhiro; Kodama, Mitsuhiko; Masakado, Yoshihisa; Liu, Meigen

    2016-09-21

    Hybrid assistive neuromuscular dynamic stimulation (HANDS) therapy improved paretic upper extremity motor function in patients with severe to moderate hemiparesis. We hypothesized that brain machine interface (BMI) training would be able to increase paretic finger muscle activity enough to apply HANDS therapy in patients with severe hemiparesis, whose finger extensor was absent. The aim of this study was to assess the efficacy of BMI training followed by HANDS therapy in patients with severe hemiparesis. Twenty-nine patients with chronic stroke who could not extend their paretic fingers were participated this study. We applied BMI training for 10 days at 40 min per day. The BMI detected the patients' motor imagery of paretic finger extension with event-related desynchronization (ERD) over the affected primary sensorimotor cortex, recorded with electroencephalography. Patients wore a motor-driven orthosis, which extended their paretic fingers and was triggered with ERD. When muscle activity in their paretic fingers was detected with surface electrodes after 10 days of BMI training, we applied HANDS therapy for the following 3 weeks. In HANDS therapy, participants received closed-loop, electromyogram-controlled, neuromuscular electrical stimulation (NMES) combined with a wrist-hand splint for 3 weeks at 8 hours a day. Before BMI training, after BMI training, after HANDS therapy and 3month after HANDS therapy, we assessed Fugl-Meyer Assessment upper extremity motor score (FMA) and the Motor Activity Log14-Amount of Use (MAL-AOU) score. After 10 days of BMI training, finger extensor activity had appeared in 21 patients. Eighteen of 21 patients then participated in 3 weeks of HANDS therapy. We found a statistically significant improvement in the FMA and the MAL-AOU scores after the BMI training, and further improvement was seen after the HANDS therapy. Combining BMI training with HANDS therapy could be an effective therapeutic strategy for severe UE paralysis after

  3. Acquistion of High Resolution Electroencephalogram Systems for Advancing Brain-Machine Interaction Research

    Science.gov (United States)

    2015-12-21

    a strong research and education center on brain machine interaction (BMI) at the University of Texas at San Antonio (UTSA). By acquiring this system...for Public Release; Distribution Unlimited Final Report: Acquistion of High Resolution Electroencephalogram Systems for Advancing Brain - Machine ...ES) U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 electroencephalogram (EEG), brain -computer interface (BCI

  4. Brain-machine interface for eye movements

    National Research Council Canada - National Science Library

    Arnulf B. A. Graf; Richard A. Andersen

    2014-01-01

    ...), a cortical node in the NHP saccade system. Eye movement plans were predicted in real time using Bayesian inference from small ensembles of UP neurons without the animal making an eye movement...

  5. Intention estimation in brain-machine interfaces

    Science.gov (United States)

    Fan, Joline M.; Nuyujukian, Paul; Kao, Jonathan C.; Chestek, Cynthia A.; Ryu, Stephen I.; Shenoy, Krishna V.

    2014-02-01

    Objective. The objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported ‘recalibrated feedback intention-trained Kalman Filter’ (ReFIT-KF). Approach. This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural basis of this improvement. We focused on assessing the contribution of two training set innovations of the ReFIT-KF algorithm: intention estimation and the two-stage training paradigm. Main results. Within the two-stage training paradigm, we found that intention estimation independently increased target acquisition rates by 37% and 59%, respectively, across two monkeys implanted with multiunit intracortical arrays. Intention estimation improved performance by enhancing the tuning properties and the mutual information between the kinematic and neural training data. Furthermore, intention estimation led to fewer shifts in channel tuning between the training set and online control, suggesting that less adaptation was required during online control. Retraining the decoder with online BMI training data also reduced shifts in tuning, suggesting a benefit of training a decoder in the same behavioral context; however, retraining also led to slower online decode velocities. Finally, we demonstrated that one- and two-stage training paradigms performed comparably when intention estimation is applied. Significance. These findings highlight the utility of intention estimation in reducing the need for adaptive strategies and improving the online performance of BMIs, helping to guide future BMI design decisions.

  6. Brain machine interfaces for serious gaming application

    NARCIS (Netherlands)

    Erp, J.B.F. van; Werkhoven, P.J.

    2007-01-01

    Serious games are intended to provide an engaging, self reinforcing context to motivate and educate the players. One of the challenges in serious gaming is to provide intuitive interaction techniques. Serious games are meant to facilitate creative and educational processes and so they should

  7. Brain-machine interface circuits and systems

    CERN Document Server

    Zjajo, Amir

    2016-01-01

    This book provides a complete overview of significant design challenges in respect to circuit miniaturization and power reduction of the neural recording system, along with circuit topologies, architecture trends, and (post-silicon) circuit optimization algorithms. The introduced novel circuits for signal conditioning, quantization, and classification, as well as system configurations focus on optimized power-per-area performance, from the spatial resolution (i.e. number of channels), feasible wireless data bandwidth and information quality to the delivered power of implantable system.

  8. Brain-Machine Collaboration for Cyborg Intelligence

    OpenAIRE

    Shi, Zhongzhi; Ma, Gang; Wang, Shu; Li, Jianqing

    2016-01-01

    Part 7: Brain-Machine Collaboration; International audience; Cyborg intelligence integrates the best of both machine and biological intelligences via brain-machine integration. To make this integration effective and co-adaptive biological brain and machine should work collaboratively. Both environment awareness based collaboration and motivation based collaboration will be presented in the paper. Motivation is the cause of action and plays important roles in collaboration. The motivation lean...

  9. Three Principles of Perception for Instructional Interface Design.

    Science.gov (United States)

    Lohr, Linda L.

    2000-01-01

    Discusses graphical user interfaces used for instructional purposes in educational environments, which promote learning goals, and in support environments, which promote performance goals. Explains three key principles of perception and gives guidelines for their use, including the figure/ground principle, the hierarchy principle, and the gestalt…

  10. Brain-computer interface control along instructed paths.

    Science.gov (United States)

    Sadtler, P T; Ryu, S I; Tyler-Kabara, E C; Yu, B M; Batista, A P

    2015-02-01

    Brain-computer interfaces (BCIs) are being developed to assist paralyzed people and amputees by translating neural activity into movements of a computer cursor or prosthetic limb. Here we introduce a novel BCI task paradigm, intended to help accelerate improvements to BCI systems. Through this task, we can push the performance limits of BCI systems, we can quantify more accurately how well a BCI system captures the user's intent, and we can increase the richness of the BCI movement repertoire. We have implemented an instructed path task, wherein the user must drive a cursor along a visible path. The instructed path task provides a versatile framework to increase the difficulty of the task and thereby push the limits of performance. Relative to traditional point-to-point tasks, the instructed path task allows more thorough analysis of decoding performance and greater richness of movement kinematics. We demonstrate that monkeys are able to perform the instructed path task in a closed-loop BCI setting. We further investigate how the performance under BCI control compares to native arm control, whether users can decrease their movement variability in the face of a more demanding task, and how the kinematic richness is enhanced in this task. The use of the instructed path task has the potential to accelerate the development of BCI systems and their clinical translation.

  11. Building Coping Skills on a Firm Foundation: Using a Metaphorical Interface To Deliver Stress Management Instruction.

    Science.gov (United States)

    Berkley, Jeannette; Cates, Ward Mitchell

    This paper examines the benefits of a metaphorical graphical user interface (GUI) and discusses how metaphorical interfaces can be used to deliver instruction on stress management. A computer-based instructional (CBI) program for college students was developed on the fundamentals of stress and the role of time management as a coping strategy. The…

  12. High-accuracy brain-machine interfaces using feedback information.

    Directory of Open Access Journals (Sweden)

    Hong Gi Yeom

    Full Text Available Sensory feedback is very important for movement control. However, feedback information has not been directly used to update movement prediction model in the previous BMI studies, although the closed-loop BMI system provides the visual feedback to users. Here, we propose a BMI framework combining image processing as the feedback information with a novel prediction method. The feedback-prediction algorithm (FPA generates feedback information from the positions of objects and modifies movement prediction according to the information. The FPA predicts a target among objects based on the movement direction predicted from the neural activity. After the target selection, the FPA modifies the predicted direction toward the target and modulates the magnitude of the predicted vector to easily reach the target. The FPA repeats the modification in every prediction time points. To evaluate the improvements of prediction accuracy provided by the feedback, we compared the prediction performances with feedback (FPA and without feedback. We demonstrated that accuracy of movement prediction can be considerably improved by the FPA combining feedback information. The accuracy of the movement prediction was significantly improved for all subjects (P<0.001 and 32.1% of the mean error was reduced. The BMI performance will be improved by combining feedback information and it will promote the development of a practical BMI system.

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

    Science.gov (United States)

    Libedinsky, Camilo; So, Rosa; Xu, Zhiming; Kyar, Toe K; Ho, Duncun; Lim, Clement; Chan, Louiza; Chua, Yuanwei; Yao, Lei; Cheong, Jia Hao; Lee, Jung Hyup; Vishal, Kulkarni Vinayak; Guo, Yongxin; Chen, Zhi Ning; Lim, Lay K; Li, Peng; Liu, Lei; Zou, Xiaodan; Ang, Kai K; Gao, Yuan; Ng, Wai Hoe; Han, Boon Siew; Chng, Keefe; Guan, Cuntai; Je, Minkyu; Yen, Shih-Cheng

    2016-01-01

    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.

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

    African Journals Online (AJOL)

    Annals of Medical and Health Sciences Research. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives · Journal Home > Vol 4, No 3 (2014) >. Log in or Register to get access to full text downloads.

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

    African Journals Online (AJOL)

    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 ...

  17. The Attitudes of Field Dependence Learners for Learner Interface Design (LID) in e-Learning Instruction

    Science.gov (United States)

    Sözcü, Ömer Faruk; Ipek, Ismail; Kinay, Hüseyin

    2016-01-01

    The purpose of the study is to explore relationships between learners' cognitive styles of field dependence and learner variables in the preference of learner Interface design, attitudes in e-Learning instruction and experience with e-Learning in distance education. Cognitive style has historically referred to a psychological dimension…

  18. Rating User Interface and Universal Instructional Design in MOOC Course Design

    Directory of Open Access Journals (Sweden)

    Richard Meyer

    2015-01-01

    Full Text Available This study examines how college students rate Massive Open Online Courses (MOOCs in terms of User Interface Design and Universal Instructional Design. The research participants were 115 undergraduate students from a public midwestern university in the United States. Each participant evaluated three randomly chosen MOOCs, all of which were developed on the Coursera platform, using rubrics for User Interface Design and Universal Instructional Design. The results indicated that students had an overall positive impression of each MOOC’s course design. This study concludes that overall course design strategies are not associated with the massive dropout rates currently documented in MOOC learning environments. The authors suggest the use of appropriate instructional design principles be further explored

  19. Visual Feedback Dominates the Sense of Agency for Brain-Machine Actions

    Science.gov (United States)

    Evans, Nathan; Gale, Steven; Schurger, Aaron; Blanke, Olaf

    2015-01-01

    Recent advances in neuroscience and engineering have led to the development of technologies that permit the control of external devices through real-time decoding of brain activity (brain-machine interfaces; BMI). Though the feeling of controlling bodily movements (sense of agency; SOA) has been well studied and a number of well-defined sensorimotor and cognitive mechanisms have been put forth, very little is known about the SOA for BMI-actions. Using an on-line BMI, and verifying that our subjects achieved a reasonable level of control, we sought to describe the SOA for BMI-mediated actions. Our results demonstrate that discrepancies between decoded neural activity and its resultant real-time sensory feedback are associated with a decrease in the SOA, similar to SOA mechanisms proposed for bodily actions. However, if the feedback discrepancy serves to correct a poorly controlled BMI-action, then the SOA can be high and can increase with increasing discrepancy, demonstrating the dominance of visual feedback on the SOA. Taken together, our results suggest that bodily and BMI-actions rely on common mechanisms of sensorimotor integration for agency judgments, but that visual feedback dominates the SOA in the absence of overt bodily movements or proprioceptive feedback, however erroneous the visual feedback may be. PMID:26066840

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

    Science.gov (United States)

    2011-01-01

    bionics /dean-kamens-luke- arm -prosth- esis-readies-for-clinical-trials. [12] S. Adee, “Winner: the revolution will be prosthetized,” IEEE Spectrum, January... arm and hand system developed by the Revolutionizing Prosthetics project [11, 12]. These advancements call for an open- source software framework that...cursor task)may be retained and immediately used for a new application (e.g., the control of a robotic arm ). Table 1 provides a list of the current

  1. A systems approach for data compression and latency reduction in cortically controlled brain machine interfaces.

    Science.gov (United States)

    Oweiss, Karim G

    2006-07-01

    This paper suggests a new approach for data compression during extracutaneous transmission of neural signals recorded by high-density microelectrode array in the cortex. The approach is based on exploiting the temporal and spatial characteristics of the neural recordings in order to strip the redundancy and infer the useful information early in the data stream. The proposed signal processing algorithms augment current filtering and amplification capability and may be a viable replacement to on chip spike detection and sorting currently employed to remedy the bandwidth limitations. Temporal processing is devised by exploiting the sparseness capabilities of the discrete wavelet transform, while spatial processing exploits the reduction in the number of physical channels through quasi-periodic eigendecomposition of the data covariance matrix. Our results demonstrate that substantial improvements are obtained in terms of lower transmission bandwidth, reduced latency and optimized processor utilization. We also demonstrate the improvements qualitatively in terms of superior denoising capabilities and higher fidelity of the obtained signals.

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

    Science.gov (United States)

    2011-09-01

    Eisner-Janowicz et al., 2008), as well as the cortex of the uninjured hemisphere (Re- inecke et al., 2003; Rema and Ebner , 2003). Neural reorgani...in rats. Eur. J. Neurosci. 17, 623–627. Rema, V., and Ebner , F.F. (2003). Lesions of mature barrel field cortex interfere with sensory processing and

  3. 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

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

    Science.gov (United States)

    2014-09-01

    2014. • Nudo, RJ, “Neuroprosthetic tools for repair of the injured brain”, 4th Scientific Conference "Restauración Neurológica 2014, Havana , Cuba...experiments. This top view shows the battery compartment, on-board Omnetics connector for wired programming, gold pin as the antenna holder, and hole for

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

    Science.gov (United States)

    2015-11-01

    T. Hashimoto , C. M. Elder, and J. L. Vitek, “A template subtraction method for stimulus artifact removal in high-frequency deep brain stimulation...Neuroscience Methods, 120(2), 113–120. 14. Hashimoto , T., Elder, C. M., & Vitek, J. L. (2002). A template subtraction method for stimulus artifact removal in...voluntary changes in pathological brain activity and improve handwriting for a patient suffering chronic writer’s cramps ( Hashimoto et al., 2014). A

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

    Science.gov (United States)

    2015-11-01

    Nudo, Harry Scott Barbay, David J. Guggenmos, Heather Hudson, Maxwell Murphy, Gustaf Van Acker Conclusion Rapid progress was made toward developing...potentials: Segmented versus subthreshold training,” IEEE Trans. Biomed. Eng., vol. 51, no. 7, pp. 1187–1195, Jul. 2004. [5] D. A. Wagenaar and S. M. Potter ...A., & Potter , S. M. (2002). Real-time multichannel stimulus artifact suppression by local curve fitting. Journal of Neuroscience Methods, 120(2), 113

  7. RFID-Inspired Miniature Antennas for Circular Polarization Tags and Brain-Machine Interface Applications

    OpenAIRE

    Song, Lingnan

    2016-01-01

    Radio frequency identification (RFID) technology in the ultra-high frequency (UHF) band has become the mainstream applications that help the speed of handling manufactured goods and materials in recent years. The mechanism of RFID backscattering technique has also been utilized for many novel wireless sensing applications, such as remote health sensing, and biomedical signal recoding. In an RFID system, the tag antenna plays a key role in the overall system performance. In this sense, the stu...

  8. The Identification, Implementation, and Evaluation of Critical User Interface Design Features of Computer-Assisted Instruction Programs in Mathematics for Students with Learning Disabilities

    Science.gov (United States)

    Seo, You-Jin; Woo, Honguk

    2010-01-01

    Critical user interface design features of computer-assisted instruction programs in mathematics for students with learning disabilities and corresponding implementation guidelines were identified in this study. Based on the identified features and guidelines, a multimedia computer-assisted instruction program, "Math Explorer", which delivers…

  9. A Framework and Implementation of User Interface and Human-Computer Interaction Instruction

    Science.gov (United States)

    Peslak, Alan

    2005-01-01

    Researchers have suggested that up to 50 % of the effort in development of information systems is devoted to user interface development (Douglas, Tremaine, Leventhal, Wills, & Manaris, 2002; Myers & Rosson, 1992). Yet little study has been performed on the inclusion of important interface and human-computer interaction topics into a current…

  10. Interface design and cognitive style in learning an instructional computer simulation.

    Science.gov (United States)

    Effken, J A; Doyle, M

    2001-01-01

    An experimental design was used to investigate how cognitive style interacts with interface design to affect users' abilities to learn to use a computer simulation. Eighteen nursing students were assigned to two groups, based on their cognitive style, and asked to solve 3 physiologic problems using 3 interface designs: a strip-chart display, an integrated balloon display, and an etiologic display. Students were given up to 2 minutes to solve each problem by administering 6 different hypothetical drugs targeted at different aspects of the simulated hemodynamic system. A mixed-design analysis of variance was used to determine the effects of interface design and cognitive style on number of problems solved, time to initiate treatment, percentage of time system maintained within normal parameters, and number of drugs used. We found that the effects of cognitive style on performance were mediated by interface design and tended to decrease with practice.

  11. A Graphical User-Interface Development Tool for Intelligent Computer- Assisted Instruction Systems

    Science.gov (United States)

    1993-09-01

    Assisted Instruction Systems by Francius Suwono Lieutenant Colonel, Indonesian AirForce B. S Aeronautics, Indonesian Air Force Academy, 1969 Submitted in...MORA 81] Moran, T P. , The Command Language Grammar : A representation for the user inerface of interactive computer systems, International Journal

  12. Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design.

    Science.gov (United States)

    Lotte, Fabien; Larrue, Florian; Mühl, Christian

    2013-01-01

    While recent research on Brain-Computer Interfaces (BCI) has highlighted their potential for many applications, they remain barely used outside laboratories. The main reason is their lack of robustness. Indeed, with current BCI, mental state recognition is usually slow and often incorrect. Spontaneous BCI (i.e., mental imagery-based BCI) often rely on mutual learning efforts by the user and the machine, with BCI users learning to produce stable ElectroEncephaloGraphy (EEG) patterns (spontaneous BCI control being widely acknowledged as a skill) while the computer learns to automatically recognize these EEG patterns, using signal processing. Most research so far was focused on signal processing, mostly neglecting the human in the loop. However, how well the user masters the BCI skill is also a key element explaining BCI robustness. Indeed, if the user is not able to produce stable and distinct EEG patterns, then no signal processing algorithm would be able to recognize them. Unfortunately, despite the importance of BCI training protocols, they have been scarcely studied so far, and used mostly unchanged for years. In this paper, we advocate that current human training approaches for spontaneous BCI are most likely inappropriate. We notably study instructional design literature in order to identify the key requirements and guidelines for a successful training procedure that promotes a good and efficient skill learning. This literature study highlights that current spontaneous BCI user training procedures satisfy very few of these requirements and hence are likely to be suboptimal. We therefore identify the flaws in BCI training protocols according to instructional design principles, at several levels: in the instructions provided to the user, in the tasks he/she has to perform, and in the feedback provided. For each level, we propose new research directions that are theoretically expected to address some of these flaws and to help users learn the BCI skill more

  13. An empirical appraisal of the effectiveness of adaptive interfaces for instructional systems

    Directory of Open Access Journals (Sweden)

    Ken Sinclair

    2000-01-01

    Full Text Available Navigating an information space, particularly in educational hypermedia, has its difficulties. Users may become spatially disoriented, they may be distracted, lose sight of educational objectives, or fail to relate important items of content. The predominant approaches to aid navigation in this in a well-defined information space such as educational software, involves the provision of a range of advanced navigation tools, to employ a strong metaphor and maintain interest through multimedia sequences, or to semantically structure the knowledge in the space according to some cognitively-based theory. However, none of these techniques can account for an individual learner's needs, knowledge, preferences or cognitive abilities. Adaptivity is a particular functionality that may be implemented in educational hypermedia systems in a variety of ways to recognise the importance of an individual discourse with an information space, and to alleviate navigational difficulties on that basis. This paper seeks to provide a broad understanding of some of the instructional and design principles implicit in adaptive educational hypermedia systems, those that use adaptive navigation support techniques and in particular adaptive link annotation. The claim that adaptive techniques can help solve navigation problems is examined through a review of two recent empirical studies that were undertaken to determine the effect of adaptive navigation support on user paths and learning, and a third study, the results of which is being introduced to the literature in this paper. These studies taken together have not shown a clear link between adaptivity and an improvement in learning, but offer some guidance for ongoing productive research in this field.

  14. Shaping the dynamics of a bidirectional neural interface.

    Directory of Open Access Journals (Sweden)

    Alessandro Vato

    Full Text Available Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a a motor interface decoding signals from a motor cortical area, and (b a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected

  15. Shaping the Dynamics of a Bidirectional Neural Interface

    Science.gov (United States)

    Vato, Alessandro; Semprini, Marianna; Maggiolini, Emma; Szymanski, Francois D.; Fadiga, Luciano; Panzeri, Stefano; Mussa-Ivaldi, Ferdinando A.

    2012-01-01

    Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations. PMID

  16. Brain-machine interface control of a manipulator using small-world neural network and shared control strategy.

    Science.gov (United States)

    Li, Ting; Hong, Jun; Zhang, Jinhua; Guo, Feng

    2014-03-15

    The improvement of the resolution of brain signal and the ability to control external device has been the most important goal in BMI research field. This paper describes a non-invasive brain-actuated manipulator experiment, which defined a paradigm for the motion control of a serial manipulator based on motor imagery and shared control. The techniques of component selection, spatial filtering and classification of motor imagery were involved. Small-world neural network (SWNN) was used to classify five brain states. To verify the effectiveness of the proposed classifier, we replace the SWNN classifier by a radial basis function (RBF) networks neural network, a standard multi-layered feed-forward backpropagation network (SMN) and a multi-SVM classifier, with the same features for the classification. The results also indicate that the proposed classifier achieves a 3.83% improvement over the best results of other classifiers. We proposed a shared control method consisting of two control patterns to expand the control of BMI from the software angle. The job of path building for reaching the 'end' point was designated as an assessment task. We recorded all paths contributed by subjects and picked up relevant parameters as evaluation coefficients. With the assistance of two control patterns and series of machine learning algorithms, the proposed BMI originally achieved the motion control of a manipulator in the whole workspace. According to experimental results, we confirmed the feasibility of the proposed BMI method for 3D motion control of a manipulator using EEG during motor imagery. Copyright © 2013 Elsevier B.V. All rights reserved.

  17. Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials.

    Science.gov (United States)

    Iturrate, Iñaki; Grizou, Jonathan; Omedes, Jason; Oudeyer, Pierre-Yves; Lopes, Manuel; Montesano, Luis

    2015-01-01

    This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an ad-hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid. The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach.

  18. Exploring the Interface: Explicit Focus-on-Form Instruction and Learned Attentional Biases in L2 Latin

    Science.gov (United States)

    Cintrón-Valentín, Myrna; Ellis, Nick C.

    2015-01-01

    Eye-tracking was used to investigate the attentional processes whereby different types of focus on form (FonF) instruction assist learners in overcoming learned attention and blocking effects in their online processing of second language input. English native speakers viewed Latin utterances combining lexical and morphological cues to temporality…

  19. Examining the Relationships of Different Cognitive Load Types Related to User Interface in Web-Based Instruction

    Science.gov (United States)

    Cheon, Jongpil; Grant, Michael

    2012-01-01

    This study proposes a new instrument to measure cognitive load types related to user interface and demonstrates theoretical assumptions about different load types. In reconsidering established cognitive load theory, the inadequacies of the theory are criticized in terms of the adaption of learning efficiency score and distinction of cognitive load…

  20. Cognitive State Monitoring and the Design of Adaptive Instruction in Digital Environments: Lessons Learned from Cognitive Workload Assessment using a Passive Brain-Computer Interface Approach

    Directory of Open Access Journals (Sweden)

    Peter eGerjets

    2014-12-01

    Full Text Available According to Cognitive Load Theory, one of the crucial factors for successful learning is the type and amount of working-memory load (WML learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presenting instruction in a way that WML is constantly held within an optimal range with regard to learners’ current working-memory capacity might be a good method to provide these optimal conditions. The current paper elaborates how digital learning environments, which achieve this goal can be developed by combining approaches from Cognitive Psychology, Neuroscience, and Computer Science. One of the biggest obstacles that needs to be overcome is the lack of an unobtrusive method of continuously assessing learners’ WML in real-time. We propose to solve this problem by applying passive Brain-Computer Interface (BCI approaches to realistic learning scenarios in digital environments. In this paper we discuss the methodological and theoretical prospects and pitfalls of this approach based on results from the literature and from our own research. We present a strategy on how several inherent challenges of applying BCIs to WML and learning can be met by refining the psychological constructs behind WML, by exploring their neural signatures, by using these insights for sophisticated task designs, and by optimizing algorithms for analyzing EEG data. Based on this strategy we applied machine-learning algorithms for cross-task classifications of different levels of WML to tasks that involve studying realistic instructional materials. We obtained very promising results that yield several recommendations for future work.

  1. Cognitive state monitoring and the design of adaptive instruction in digital environments: lessons learned from cognitive workload assessment using a passive brain-computer interface approach.

    Science.gov (United States)

    Gerjets, Peter; Walter, Carina; Rosenstiel, Wolfgang; Bogdan, Martin; Zander, Thorsten O

    2014-01-01

    According to Cognitive Load Theory (CLT), one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presenting instruction in a way that WML is constantly held within an optimal range with regard to learners' working-memory capacity might be a good method to provide these optimal conditions. The current paper elaborates how digital learning environments, which achieve this goal can be developed by combining approaches from Cognitive Psychology, Neuroscience, and Computer Science. One of the biggest obstacles that needs to be overcome is the lack of an unobtrusive method of continuously assessing learners' WML in real-time. We propose to solve this problem by applying passive Brain-Computer Interface (BCI) approaches to realistic learning scenarios in digital environments. In this paper we discuss the methodological and theoretical prospects and pitfalls of this approach based on results from the literature and from our own research. We present a strategy on how several inherent challenges of applying BCIs to WML and learning can be met by refining the psychological constructs behind WML, by exploring their neural signatures, by using these insights for sophisticated task designs, and by optimizing algorithms for analyzing electroencephalography (EEG) data. Based on this strategy we applied machine-learning algorithms for cross-task classifications of different levels of WML to tasks that involve studying realistic instructional materials. We obtained very promising results that yield several recommendations for future work.

  2. Cognitive state monitoring and the design of adaptive instruction in digital environments: lessons learned from cognitive workload assessment using a passive brain-computer interface approach

    Science.gov (United States)

    Gerjets, Peter; Walter, Carina; Rosenstiel, Wolfgang; Bogdan, Martin; Zander, Thorsten O.

    2014-01-01

    According to Cognitive Load Theory (CLT), one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presenting instruction in a way that WML is constantly held within an optimal range with regard to learners' working-memory capacity might be a good method to provide these optimal conditions. The current paper elaborates how digital learning environments, which achieve this goal can be developed by combining approaches from Cognitive Psychology, Neuroscience, and Computer Science. One of the biggest obstacles that needs to be overcome is the lack of an unobtrusive method of continuously assessing learners' WML in real-time. We propose to solve this problem by applying passive Brain-Computer Interface (BCI) approaches to realistic learning scenarios in digital environments. In this paper we discuss the methodological and theoretical prospects and pitfalls of this approach based on results from the literature and from our own research. We present a strategy on how several inherent challenges of applying BCIs to WML and learning can be met by refining the psychological constructs behind WML, by exploring their neural signatures, by using these insights for sophisticated task designs, and by optimizing algorithms for analyzing electroencephalography (EEG) data. Based on this strategy we applied machine-learning algorithms for cross-task classifications of different levels of WML to tasks that involve studying realistic instructional materials. We obtained very promising results that yield several recommendations for future work. PMID:25538544

  3. Instantaneous Liquid Interfaces

    OpenAIRE

    Willard, Adam P.; Chandler, David

    2009-01-01

    We describe and illustrate a simple procedure for identifying a liquid interface from atomic coordinates. In particular, a coarse grained density field is constructed, and the interface is defined as a constant density surface for this coarse grained field. In applications to a molecular dynamics simulation of liquid water, it is shown that this procedure provides instructive and useful pictures of liquid-vapor interfaces and of liquid-protein interfaces.

  4. Direct Growth of Carbon Nanotubes on New High-Density 3D Pyramid-Shaped Microelectrode Arrays for Brain-Machine Interfaces

    Directory of Open Access Journals (Sweden)

    Bahareh Ghane Motlagh

    2016-09-01

    Full Text Available Silicon micromachined, high-density, pyramid-shaped neural microelectrode arrays (MEAs have been designed and fabricated for intracortical 3D recording and stimulation. The novel architecture of this MEA has made it unique among the currently available micromachined electrode arrays, as it has provided higher density contacts between the electrodes and targeted neural tissue facilitating recording from different depths of the brain. Our novel masking technique enhances uniform tip-exposure for variable-height electrodes and improves process time and cost significantly. The tips of the electrodes have been coated with platinum (Pt. We have reported for the first time a selective direct growth of carbon nanotubes (CNTs on the tips of 3D MEAs using the Pt coating as a catalyzer. The average impedance of the CNT-coated electrodes at 1 kHz is 14 kΩ. The CNT coating led to a 5-fold decrease of the impedance and a 600-fold increase in charge transfer compared with the Pt electrode.

  5. Decoding Local Field Potentials for Neural Interfaces.

    Science.gov (United States)

    Jackson, Andrew; Hall, Thomas M

    2017-10-01

    The stability and frequency content of local field potentials (LFPs) offer key advantages for long-term, low-power neural interfaces. However, interpreting LFPs may require new signal processing techniques which should be informed by a scientific understanding of how these recordings arise from the coordinated activity of underlying neuronal populations. We review current approaches to decoding LFPs for brain-machine interface (BMI) applications, and suggest several directions for future research. To facilitate an improved understanding of the relationship between LFPs and spike activity, we share a dataset of multielectrode recordings from monkey motor cortex, and describe two unsupervised analysis methods we have explored for extracting a low-dimensional feature space that is amenable to biomimetic decoding and biofeedback training.

  6. Interfacing with the brain using organic electronics (Presentation Recording)

    Science.gov (United States)

    Malliaras, George G.

    2015-10-01

    Implantable electrodes are being used for diagnostic purposes, for brain-machine interfaces, and for delivering electrical stimulation to alleviate the symptoms of diseases such as Parkinson's. The field of organic electronics made available devices with a unique combination of attractive properties, including mixed ionic/electronic conduction, mechanical flexibility, enhanced biocompatibility, and capability for drug delivery. I will present examples of organic electrodes, transistors and other devices for recording and stimulation of brain activity and discuss how they can improve our understanding of brain physiology and pathology, and how they can be used to deliver new therapies.

  7. Electronic dura mater for long-term multimodal neural interfaces

    Science.gov (United States)

    Minev, Ivan R.; Musienko, Pavel; Hirsch, Arthur; Barraud, Quentin; Wenger, Nikolaus; Moraud, Eduardo Martin; Gandar, Jérôme; Capogrosso, Marco; Milekovic, Tomislav; Asboth, Léonie; Torres, Rafael Fajardo; Vachicouras, Nicolas; Liu, Qihan; Pavlova, Natalia; Duis, Simone; Larmagnac, Alexandre; Vörös, Janos; Micera, Silvestro; Suo, Zhigang; Courtine, Grégoire; Lacour, Stéphanie P.

    2015-01-01

    The mechanical mismatch between soft neural tissues and stiff neural implants hinders the long-term performance of implantable neuroprostheses. Here, we designed and fabricated soft neural implants with the shape and elasticity of dura mater, the protective membrane of the brain and spinal cord. The electronic dura mater, which we call e-dura, embeds interconnects, electrodes, and chemotrodes that sustain millions of mechanical stretch cycles, electrical stimulation pulses, and chemical injections. These integrated modalities enable multiple neuroprosthetic applications. The soft implants extracted cortical states in freely behaving animals for brain-machine interface and delivered electrochemical spinal neuromodulation that restored locomotion after paralyzing spinal cord injury.

  8. Endpoint-based parallel data processing with non-blocking collective instructions in a parallel active messaging interface of a parallel computer

    Science.gov (United States)

    Archer, Charles J; Blocksome, Michael A; Cernohous, Bob R; Ratterman, Joseph D; Smith, Brian E

    2014-11-11

    Endpoint-based parallel data processing with non-blocking collective instructions in a PAMI of a parallel computer is disclosed. The PAMI is composed of data communications endpoints, each including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task. The compute nodes are coupled for data communications through the PAMI. The parallel application establishes a data communications geometry specifying a set of endpoints that are used in collective operations of the PAMI by associating with the geometry a list of collective algorithms valid for use with the endpoints of the geometry; registering in each endpoint in the geometry a dispatch callback function for a collective operation; and executing without blocking, through a single one of the endpoints in the geometry, an instruction for the collective operation.

  9. Instructional Media

    African Journals Online (AJOL)

    Experiments on using Instructional Television. Most experiments compare instruction using TV, with the conventional classroom instruction by the teacher. The findings are clear. ... scientific reliability, and all of these showed no significant difference.

  10. A hybrid brain interface for a humanoid robot assistant.

    Science.gov (United States)

    Finke, Andrea; Knoblauch, Andreas; Koesling, Hendrik; Ritter, Helge

    2011-01-01

    We present an advanced approach towards a semi-autonomous, robotic personal assistant for handicapped people. We developed a multi-functional hybrid brain-robot interface that provides a communication channel between humans and a state-of-the-art humanoid robot, Honda's Humanoid Research Robot. Using cortical signals, recorded, processed and translated by an EEG-based brain-machine interface (BMI), human-robot interaction functions independently of users' motor control deficits. By exploiting two distinct cortical activity patterns, P300 and event-related desynchronization (ERD), the interface provides different dimensions for robot control. An empirical study demonstrated the functionality of the BMI guided humanoid robot. All participants could successfully control the robot that accomplished a shopping task.

  11. An integrated neuro-robotic interface for stroke rehabilitation using the NASA X1 powered lower limb exoskeleton.

    Science.gov (United States)

    He, Yongtian; Nathan, Kevin; Venkatakrishnan, Anusha; Rovekamp, Roger; Beck, Christopher; Ozdemir, Recep; Francisco, Gerard E; Contreras-Vidal, Jose L

    2014-01-01

    Stroke remains a leading cause of disability, limiting independent ambulation in survivors, and consequently affecting quality of life (QOL). Recent technological advances in neural interfacing with robotic rehabilitation devices are promising in the context of gait rehabilitation. Here, the X1, NASA's powered robotic lower limb exoskeleton, is introduced as a potential diagnostic, assistive, and therapeutic tool for stroke rehabilitation. Additionally, the feasibility of decoding lower limb joint kinematics and kinetics during walking with the X1 from scalp electroencephalographic (EEG) signals--the first step towards the development of a brain-machine interface (BMI) system to the X1 exoskeleton--is demonstrated.

  12. 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.

  13. Kinetic Interface

    DEFF Research Database (Denmark)

    2009-01-01

    A kinetic interface for orientation detection in a video training system is disclosed. The interface includes a balance platform instrumented with inertial motion sensors. The interface engages a participant's sense of balance in training exercises.......A kinetic interface for orientation detection in a video training system is disclosed. The interface includes a balance platform instrumented with inertial motion sensors. The interface engages a participant's sense of balance in training exercises....

  14. Brain-computer interface in paralysis.

    Science.gov (United States)

    Birbaumer, Niels; Murguialday, Ander Ramos; Cohen, Leonardo

    2008-12-01

    Communication with patients suffering from locked-in syndrome and other forms of paralysis is an unsolved challenge. Movement restoration for patients with chronic stroke or other brain damage also remains a therapeutic problem and available treatments do not offer significant improvements. This review considers recent research in brain-computer interfaces (BCIs) as promising solutions to these challenges. Experimentation with nonhuman primates suggests that intentional goal directed movements of the upper limbs can be reconstructed and transmitted to external manipulandum or robotic devices controlled from a relatively small number of microelectrodes implanted into movement-relevant brain areas after some training, opening the door for the development of BCI or brain-machine interfaces in humans. Although noninvasive BCIs using electroencephalographic recordings or event-related-brain-potentials in healthy individuals and patients with amyotrophic lateral sclerosis or stroke can transmit up to 80 bits/min of information, the use of BCIs - invasive or noninvasive - in severely or totally paralyzed patients has met some unforeseen difficulties. Invasive and noninvasive BCIs using recordings from nerve cells, large neuronal pools such as electrocorticogram and electroencephalography, or blood flow based measures such as functional magnetic resonance imaging and near-infrared spectroscopy show potential for communication in locked-in syndrome and movement restoration in chronic stroke, but controlled phase III clinical trials with larger populations of severely disturbed patients are urgently needed.

  15. 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 3-7th, 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.

  16. 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 3–7th, 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. PMID:25485284

  17. Development of bioactive conducting polymers for neural interfaces.

    Science.gov (United States)

    Poole-Warren, Laura; Lovell, Nigel; Baek, Sungchul; Green, Rylie

    2010-01-01

    Bioelectrodes for neural recording and neurostimulation are an integral component of a number of neuroprosthetic devices, including the commercially available cochlear implant, and developmental devices, such as the bionic eye and brain-machine interfaces. Current electrode designs limit the application of such devices owing to suboptimal material properties that lead to minimal interaction with the target neural tissue and the formation of fibrotic capsules. In designing an ideal bioelectrode, a number of design criteria must be considered with respect to physical, mechanical, electrical and biological properties. Conducting polymers have the potential to address the synergistic interaction of these properties and show promise as superior coatings for next-generation electrodes in implant devices.

  18. Interface Consistency

    DEFF Research Database (Denmark)

    Staunstrup, Jørgen

    1998-01-01

    This paper proposes that Interface Consistency is an important issue for the development of modular designs. Byproviding a precise specification of component interfaces it becomes possible to check that separately developedcomponents use a common interface in a coherent matter thus avoiding a very...

  19. Instructional Ventures

    Directory of Open Access Journals (Sweden)

    Robert Prus

    2015-04-01

    Full Text Available Beyond promoting a mode of ethnographic inquiry that is conceptually informed and rigorously attentive to the actualities of human lived experience, this article encourages a more sustained, comparative analysis of the ways that administrators and instructors deal with education as a collectively developed venture. After (a establishing an analytic frame for a more comprehensive approach to education as a socially engaged process, this article focuses on (b the administration of educational programs and (c providing instruction as activity “in the making,” using an ethnographic study of two Protestant Christian seminaries as an empirical, illustrative case. While providing an agenda for examining the ways that people generate and sustain instructional ventures in any educational context, the material presented here also represents an important focal point for theoretically, conceptually, and methodologically integrating research that attends to the ways that instructional (administrative and teaching activities are accomplished in practice.

  20. Change in brain activity through virtual reality-based brain-machine communication in a chronic tetraplegic subject with muscular dystrophy

    OpenAIRE

    Liu Meigen; Kimura Akio; Ushiba Junichi; Hashimoto Yasunari; Tomita Yutaka

    2010-01-01

    Abstract Background For severely paralyzed people, a brain-computer interface (BCI) provides a way of re-establishing communication. Although subjects with muscular dystrophy (MD) appear to be potential BCI users, the actual long-term effects of BCI use on brain activities in MD subjects have yet to be clarified. To investigate these effects, we followed BCI use by a chronic tetraplegic subject with MD over 5 months. The topographic changes in an electroencephalogram (EEG) after long-term use...

  1. Editorial - Instructions

    DEFF Research Database (Denmark)

    Kastberg, Peter; Grinsted, Annelise

    2007-01-01

    Why you may wonder - have we chosen a topic which at first glance may seem trivial, and even a bit dull? Well, looks can be deceiving, and in this case they are! There are many good reasons for taking a closer look at instructions.......Why you may wonder - have we chosen a topic which at first glance may seem trivial, and even a bit dull? Well, looks can be deceiving, and in this case they are! There are many good reasons for taking a closer look at instructions....

  2. Interface Realisms

    DEFF Research Database (Denmark)

    Pold, Søren

    2005-01-01

    This article argues for seeing the interface as an important representational and aesthetic form with implications for postmodern culture and digital aesthetics. The interface emphasizes realism due in part to the desire for transparency in Human-Computer Interaction (HCI) and partly to the devel......This article argues for seeing the interface as an important representational and aesthetic form with implications for postmodern culture and digital aesthetics. The interface emphasizes realism due in part to the desire for transparency in Human-Computer Interaction (HCI) and partly...

  3. Interface models

    DEFF Research Database (Denmark)

    Ravn, Anders P.; Staunstrup, Jørgen

    1994-01-01

    This paper proposes a model for specifying interfaces between concurrently executing modules of a computing system. The model does not prescribe a particular type of communication protocol and is aimed at describing interfaces between both software and hardware modules or a combination of the two...

  4. Organic interfaces

    NARCIS (Netherlands)

    Poelman, W.A.; Tempelman, E.

    2014-01-01

    This paper deals with the consequences for product designers resulting from the replacement of traditional interfaces by responsive materials. Part 1 presents a theoretical framework regarding a new paradigm for man-machine interfacing. Part 2 provides an analysis of the opportunities offered by new

  5. Fluid Interfaces

    DEFF Research Database (Denmark)

    Hansen, Klaus Marius

    2001-01-01

    Fluid interaction, interaction by the user with the system that causes few breakdowns, is essential to many user interfaces. We present two concrete software systems that try to support fluid interaction for different work practices. Furthermore, we present specificity, generality, and minimality...... as design goals for fluid interfaces....

  6. Microprocessor interfacing

    CERN Document Server

    Vears, R E

    2014-01-01

    Microprocessor Interfacing provides the coverage of the Business and Technician Education Council level NIII unit in Microprocessor Interfacing (syllabus U86/335). Composed of seven chapters, the book explains the foundation in microprocessor interfacing techniques in hardware and software that can be used for problem identification and solving. The book focuses on the 6502, Z80, and 6800/02 microprocessor families. The technique starts with signal conditioning, filtering, and cleaning before the signal can be processed. The signal conversion, from analog to digital or vice versa, is expl

  7. Manufacturing Interfaces

    NARCIS (Netherlands)

    van Houten, Frederikus J.A.M.

    1992-01-01

    The paper identifies the changing needs and requirements with respect to the interfacing of manufacturing functions. It considers the manufacturing system, its components and their relationships from the technological and logistic point of view, against the background of concurrent engineering.

  8. Change in brain activity through virtual reality-based brain-machine communication in a chronic tetraplegic subject with muscular dystrophy.

    Science.gov (United States)

    Hashimoto, Yasunari; Ushiba, Junichi; Kimura, Akio; Liu, Meigen; Tomita, Yutaka

    2010-09-16

    For severely paralyzed people, a brain-computer interface (BCI) provides a way of re-establishing communication. Although subjects with muscular dystrophy (MD) appear to be potential BCI users, the actual long-term effects of BCI use on brain activities in MD subjects have yet to be clarified. To investigate these effects, we followed BCI use by a chronic tetraplegic subject with MD over 5 months. The topographic changes in an electroencephalogram (EEG) after long-term use of the virtual reality (VR)-based BCI were also assessed. Our originally developed BCI system was used to classify an EEG recorded over the sensorimotor cortex in real time and estimate the user's motor intention (MI) in 3 different limb movements: feet, left hand, and right hand. An avatar in the internet-based VR was controlled in accordance with the results of the EEG classification by the BCI. The subject was trained to control his avatar via the BCI by strolling in the VR for 1 hour a day and then continued the same training twice a month at his home. After the training, the error rate of the EEG classification decreased from 40% to 28%. The subject successfully walked around in the VR using only his MI and chatted with other users through a voice-chat function embedded in the internet-based VR. With this improvement in BCI control, event-related desynchronization (ERD) following MI was significantly enhanced (p < 0.01) for feet MI (from -29% to -55%), left-hand MI (from -23% to -42%), and right-hand MI (from -22% to -51%). These results show that our subject with severe MD was able to learn to control his EEG signal and communicate with other users through use of VR navigation and suggest that an internet-based VR has the potential to provide paralyzed people with the opportunity for easy communication.

  9. Change in brain activity through virtual reality-based brain-machine communication in a chronic tetraplegic subject with muscular dystrophy

    Directory of Open Access Journals (Sweden)

    Liu Meigen

    2010-09-01

    Full Text Available Abstract Background For severely paralyzed people, a brain-computer interface (BCI provides a way of re-establishing communication. Although subjects with muscular dystrophy (MD appear to be potential BCI users, the actual long-term effects of BCI use on brain activities in MD subjects have yet to be clarified. To investigate these effects, we followed BCI use by a chronic tetraplegic subject with MD over 5 months. The topographic changes in an electroencephalogram (EEG after long-term use of the virtual reality (VR-based BCI were also assessed. Our originally developed BCI system was used to classify an EEG recorded over the sensorimotor cortex in real time and estimate the user's motor intention (MI in 3 different limb movements: feet, left hand, and right hand. An avatar in the internet-based VR was controlled in accordance with the results of the EEG classification by the BCI. The subject was trained to control his avatar via the BCI by strolling in the VR for 1 hour a day and then continued the same training twice a month at his home. Results After the training, the error rate of the EEG classification decreased from 40% to 28%. The subject successfully walked around in the VR using only his MI and chatted with other users through a voice-chat function embedded in the internet-based VR. With this improvement in BCI control, event-related desynchronization (ERD following MI was significantly enhanced (p Conclusions These results show that our subject with severe MD was able to learn to control his EEG signal and communicate with other users through use of VR navigation and suggest that an internet-based VR has the potential to provide paralyzed people with the opportunity for easy communication.

  10. Designing Interfaces

    CERN Document Server

    Tidwell, Jenifer

    2010-01-01

    Despite all of the UI toolkits available today, it's still not easy to design good application interfaces. This bestselling book is one of the few reliable sources to help you navigate through the maze of design options. By capturing UI best practices and reusable ideas as design patterns, Designing Interfaces provides solutions to common design problems that you can tailor to the situation at hand. This updated edition includes patterns for mobile apps and social media, as well as web applications and desktop software. Each pattern contains full-color examples and practical design advice th

  11. 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

  12. EMG-based and gaze-tracking-based man-machine interfaces.

    Science.gov (United States)

    Carpi, Federico; Rossi, Danilo De

    2009-01-01

    A great demand for brain-machine and, more generally, man-machine interfaces is arising nowadays, pushed by several promising scientific and technological results, which are encouraging the concentration of efforts in this field. The possibility of measuring, processing and decoding brain activity, so as to interpret neural signals, is often looked at as a possibility to bypass lost or damaged neural and/or motor structures. Beyond that, such interfaces currently show a potential for applications in other fields, space science being certainly one of them. At present, the concept of "reading" the brain to detect intended actions and use these to control external devices is being studied with several technical and methodological approaches; among these, interfaces based on electroencephalographic signals play today a prominent role. Within such a context, the aim of this section is to present a brief survey on two types of noninvasive man-machine interfaces based on a different approach. In particular, they rely on the extraction of control signals from the user with techniques that adopt electromyography and gaze tracking. Working principles, implementations, typical features, and applications of these two types of interfaces are reported.

  13. Testing Interfaces

    DEFF Research Database (Denmark)

    Holbøll, Joachim T.; Henriksen, Mogens; Nilson, Jesper K.

    1999-01-01

    The wide use of solid insulating materials combinations in combinations has introduced problems in the interfaces between components. The most common insulating materials are cross-linked polyethylene (XLPE), silicone rubber (SIR) and ethylene-propylene rubbers (EPR). Assemblies of these materials...

  14. Basic Operational Robotics Instructional System

    Science.gov (United States)

    Todd, Brian Keith; Fischer, James; Falgout, Jane; Schweers, John

    2013-01-01

    The Basic Operational Robotics Instructional System (BORIS) is a six-degree-of-freedom rotational robotic manipulator system simulation used for training of fundamental robotics concepts, with in-line shoulder, offset elbow, and offset wrist. BORIS is used to provide generic robotics training to aerospace professionals including flight crews, flight controllers, and robotics instructors. It uses forward kinematic and inverse kinematic algorithms to simulate joint and end-effector motion, combined with a multibody dynamics model, moving-object contact model, and X-Windows based graphical user interfaces, coordinated in the Trick Simulation modeling environment. The motivation for development of BORIS was the need for a generic system for basic robotics training. Before BORIS, introductory robotics training was done with either the SRMS (Shuttle Remote Manipulator System) or SSRMS (Space Station Remote Manipulator System) simulations. The unique construction of each of these systems required some specialized training that distracted students from the ideas and goals of the basic robotics instruction.

  15. Audible Computer-Assisted Instruction: Toward the Compleat CAI.

    Science.gov (United States)

    Hertzler, Emanuel C.

    1979-01-01

    Presents the technical details of the hardware and software used with two kinds of computers, the Radio Shack TRS-80 and the Apple II, in developing personalized computer-assisted instruction with the addition of the instructor's voice interfaced with the computer. Programing controls for this interface are listed. (RAO)

  16. Interface learning

    DEFF Research Database (Denmark)

    Thorhauge, Sally

    2014-01-01

    "Interface learning - New goals for museum and upper secondary school collaboration" investigates and analyzes the learning that takes place when museums and upper secondary schools in Denmark work together in local partnerships to develop and carry out school-related, museum-based coursework...... for students. The research focuses on the learning that the students experience in the interface of the two learning environments: The formal learning environment of the upper secondary school and the informal learning environment of the museum. Focus is also on the learning that the teachers and museum...... professionals experience as a result of their collaboration. The dissertation demonstrates how a given partnership’s collaboration affects the students’ learning experiences when they are doing the coursework. The dissertation presents findings that museum-school partnerships can use in order to develop...

  17. iPhone User Interface Cookbook

    CERN Document Server

    Banga, Cameron

    2011-01-01

    Written in a cookbook style, this book offers solutions using a recipe based approach. Each recipe contains step-by-step instructions followed by an analysis of what was done in each task and other useful information. The cookbook approach means you can dive into whatever recipes you want in no particular order. The iPhone Interface Cookbook is written from the ground up for people who are new to iOS or application interface design in general. Each chapter discusses the reasoning and design strategy behind critical interface components, as well as how to best integrate each into any iPhone or

  18. 'Interfaces' 4

    Directory of Open Access Journals (Sweden)

    Paolo Borsa

    2018-01-01

    Full Text Available Issue No. 4 is the first open issue of Interfaces: A Journal of Medieval European Literatures. It contains contributions by Henry Bainton (12th-century historiography, Lucie Doležalová (parabiblical texts and the canon, Máire Ní Mhaonaigh (Irish literary culture in Latin and Irish, Isabel Varillas Sánchez (legends of composition of canonical texts, Septuaginta, Wim Verbaal (letter collections, Bernard of Clairvaux, and Jonas Wellendorf (canons of skaldic poets in the 12th/13th century, preceded by a brief Introduction by the editors.

  19. User Interface Design for E-Learning System

    OpenAIRE

    Suteja, Bernard Renaldy; Harjoko, Agus

    2008-01-01

    With the demand for e-Learning steadily growing and the ongoing struggle to convince the skeptics of thepotential of e-Learning and online virtual classrooms, quality design is the foundation for a successful DEprogram. The design of the instruction and the design of the user interface are critical elements in providingquality education with a virtual, e-Learning model. This White Paper will focus on the design of the e-Learninguser interface (UI). This paper provide examples of user interfac...

  20. Interpenetrating Conducting Hydrogel Materials for Neural Interfacing Electrodes.

    Science.gov (United States)

    Goding, Josef; Gilmour, Aaron; Martens, Penny; Poole-Warren, Laura; Green, Rylie

    2017-05-01

    Conducting hydrogels (CHs) are an emerging technology in the field of medical electrodes and brain-machine interfaces. The greatest challenge to the fabrication of CH electrodes is the hybridization of dissimilar polymers (conductive polymer and hydrogel) to ensure the formation of interpenetrating polymer networks (IPN) required to achieve both soft and electroactive materials. A new hydrogel system is developed that enables tailored placement of covalently immobilized dopant groups within the hydrogel matrix. The role of immobilized dopant in the formation of CH is investigated through covalent linking of sulfonate doping groups to poly(vinyl alcohol) (PVA) macromers. These groups control the electrochemical growth of the conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) and subsequent material properties. The effect of dopant density and interdopant spacing on the physical, electrochemical, and mechanical properties of the resultant CHs is examined. Cytocompatible PVA hydrogels with PEDOT penetration throughout the depth of the electrode are produced. Interdopant spacing is found to be the key factor in the formation of IPNs, with smaller interdopant spacing producing CH electrodes with greater charge storage capacity and lower impedance due to increased PEDOT growth throughout the network. This approach facilitates tailorable, high-performance CH electrodes for next generation, low impedance neuroprosthetic devices. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Classes of Instructional Variables.

    Science.gov (United States)

    Reigeluth, Charles M.; Merrill, M. David

    1979-01-01

    Describes three classes of variables which should be considered when one is designing instructional materials, doing research on instruction, or developing better methods of instruction, and proposes a classification scheme which is summarized in the last of the 13 figures that illustrate the article. A blbliography is included. (Author/RAO)

  2. Cursor control by Kalman filter with a non-invasive body-machine interface

    Science.gov (United States)

    Seáñez-González, Ismael; Mussa-Ivaldi, Ferdinando A.

    2014-10-01

    Objective. We describe a novel human-machine interface for the control of a two-dimensional (2D) computer cursor using four inertial measurement units (IMUs) placed on the user’s upper-body. Approach. A calibration paradigm where human subjects follow a cursor with their body as if they were controlling it with their shoulders generates a map between shoulder motions and cursor kinematics. This map is used in a Kalman filter to estimate the desired cursor coordinates from upper-body motions. We compared cursor control performance in a centre-out reaching task performed by subjects using different amounts of information from the IMUs to control the 2D cursor. Main results. Our results indicate that taking advantage of the redundancy of the signals from the IMUs improved overall performance. Our work also demonstrates the potential of non-invasive IMU-based body-machine interface systems as an alternative or complement to brain-machine interfaces for accomplishing cursor control in 2D space. Significance. The present study may serve as a platform for people with high-tetraplegia to control assistive devices such as powered wheelchairs using a joystick.

  3. Information technology in veterinary pharmacology instruction.

    Science.gov (United States)

    Kochevar, Deborah T

    2003-01-01

    Veterinary clinical pharmacology encompasses all interactions between drugs and animals and applies basic and clinical knowledge to improve rational drug use and patient outcomes. Veterinary pharmacology instructors set educational goals and objectives that, when mastered by students, lead to improved animal health. The special needs of pharmacology instruction include establishing a functional interface between basic and clinical knowledge, managing a large quantity of information, and mastering quantitative skills essential to successful drug administration and analysis of drug action. In the present study, a survey was conducted to determine the extent to which veterinary pharmacology instructors utilize information technology (IT) in their teaching. Several IT categories were investigated, including Web-based instructional aids, stand-alone pharmacology software, interactive videoconferencing, databases, personal digital assistants (PDAs), and e-book applications. Currently IT plays a largely ancillary role in pharmacology instruction. IT use is being expanded primarily through the efforts of two veterinary professional pharmacology groups, the American College of Veterinary Clinical Pharmacology (ACVCP) and the American Academy of Veterinary Pharmacology and Therapeutics (AAVPT). The long-term outcome of improved IT use in pharmacology instruction should be to support the larger educational mission of active learning and problem solving. Creation of high-quality IT resources that promote this goal has the potential to improve veterinary pharmacology instruction within and across institutions.

  4. Differentiating Mathematics Instruction

    OpenAIRE

    Walter, Brett

    2016-01-01

    The importance of teaching students based on their levels of development and ability, or differentiated instruction, has been used in Language Arts classes increasingly over the last decade. However, it is only recently that attention in research has been given to the use of differentiated instruction in a mathematics lesson. This paper aims to explore what research is being done to not only improve mathematics instruction in the classroom, but to better prepare future teachers for teaching m...

  5. GRIZZLY/FAVOR Interface Project Report

    Energy Technology Data Exchange (ETDEWEB)

    Dickson, Terry L [ORNL; Williams, Paul T [ORNL; Yin, Shengjun [ORNL; Klasky, Hilda B [ORNL; Tadinada, Sashi [ORNL; Bass, Bennett Richard [ORNL

    2013-06-01

    As part of the Light Water Reactor Sustainability (LWRS) Program, the objective of the GRIZZLY/FAVOR Interface project is to create the capability to apply GRIZZLY 3-D finite element (thermal and stress) analysis results as input to FAVOR probabilistic fracture mechanics (PFM) analyses. The one benefit of FAVOR to Grizzly is the PROBABILISTIC capability. This document describes the implementation of the GRIZZLY/FAVOR Interface, the preliminary verification and tests results and a user guide that provides detailed step-by-step instructions to run the program.

  6. Instructional Software for Biochemistry Education

    Directory of Open Access Journals (Sweden)

    Marson Guilherme Andrade

    2004-05-01

    Full Text Available In the last decades the world has witnessed a revolution in the expansion and access to knowledge, whi-ch has dramatically changed the relationship between labor and production. According to UNESCO(United Nations Educational, Scientical and Cultural Organization and ILO (International LaborOrganization, in the of information it is fundamental that Higher Education Institutes educate pro-fessionals capable to update their knowledge in the course of professional life. The so-called life-longlearning s pointed out as a request for creating and maintaining jobs, and for supporting the develop-ment of nations as well. In such context, Biochemistry is a eld of knowledge which has outstandinglyexpanded its boundaries. Preparing the next generation of biochemists for the age of informationrequires the development of cognitive skills as an essential educational goal concerning graduationcourses, which have been historically limited to the exposition of contents. The achievement of suchobjective depends on many factors, including the development of suitable instructional materials thatcan improve the teaching and learning experience. This conference deals with the development ofinstructional software at the crossroad of Educational research, Informatics and Biochemistry. Theinvestigative approach leading to the development and improvement of instructional software for Bi-ochemical education will be discussed on the basis of the following issues: i motivating questionsto software development - teaching and learning problems; ii development of digital content: speci-c content, interface and interactivity; iii evaluation of the software s instructional eciency; ivexamples of softwares which have been conceived according to the discussed methodology.

  7. Instructional Leadership Handbook.

    Science.gov (United States)

    Keefe, James W., Ed.; Jenkins, John M., Ed.

    Instructional leadership is the principal's role in providing direction, resources, and support to teachers and students for the improvement of teaching and learning in the school. This handbook reviews factors affecting that role in four broad domains: keeping informed about trends, planning for instructional improvement, implementing…

  8. Supplemental instruction in chemistry

    Science.gov (United States)

    Lundeberg, Mary A.

    This study was designed to measure some effects of supplemental instruction in chemistry. Supplemental instruction is a peer-led cooperative learning program that encourages students to develop conceptual understanding by articulating both understandings and misconceptions in a think-aloud fashion. Supplemental instruction was offered three hours weekly outside of class and lab time for students in four classes of General Organic and Biological Chemistry. Over a two-year period 108 students volunteered to participate in this program; 45 students did not participate. As measured by final grades in chemistry and responses to a questionnaire, supplemental instruction was effective in increasing students' achievement in chemistry. Further research is needed to determine the in-depth effects of supplemental instruction on students' learning, problem solving, and self-esteem.

  9. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

    Science.gov (United States)

    Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-08-01

    Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

  10. Sound's Use in Instructional Software to Enhance Learning: A Theory-to-Practice Content Analysis

    Science.gov (United States)

    Bishop, M. J.; Amankwatia, Tonya B.; Cates, Ward Mitchell

    2008-01-01

    Sound may hold great promise for instructional software by supporting learning in a variety of ways. Conceptual and preconceptual barriers, however, still appear to prevent software designers from using sound more effectively in their instructional products. Interface books seldom discuss the use of sound and when they do, it is most often simple…

  11. Interface mobility from interface random walk

    Science.gov (United States)

    Trautt, Zachary; Upmanyu, Moneesh; Karma, Alain

    2007-03-01

    Computational studies aimed at extracting interface mobilities require driving forces orders of magnitude higher than those occurring experimentally. We present a computational methodology that extracts the absolute interface mobility in the zero driving force limit by monitoring the one-dimensional random walk of the mean interface position along the interface normal. The method exploits a fluctuation-dissipation relation similar to the Stokes-Einstein relation, which relates the diffusion coefficient of this Brownian-like random walk to the interface mobility. Atomic-scale simulations of grain boundaries in model crystalline systems validate the theoretical predictions, and also highlight the profound effect of impurities. The generality of this technique combined with its inherent spatial-temporal efficiency should allow computational studies to effectively complement experiments in understanding interface kinetics in diverse material systems.

  12. Interface solutions for interface side effects?

    Directory of Open Access Journals (Sweden)

    Stoffregen Thomas A.

    2011-12-01

    Full Text Available Human-computer interfaces often give rise to a variety of side effects, including eyestrain, headache, fatigue, and motion sickness (aka cybersickness, simulator sickness. We might hope that improvements in interface design would tend to reduce these side effects. Unfortunately, history reveals just the opposite: The incidence and severity of motion sickness (for example is positively related to the progressive sophistication of display technology and systems. In this presentation, I enquire about the future of interface technologies in relation to side effects. I review the types of side effects that occur and what is known about the causes of interface side effects. I suggest new ways of understanding relations between interface technologies and side effects, and new ways to approach the problem of interface side effects.

  13. Polymer Composite with Carbon Nanofibers Aligned during Thermal Drawing as a Microelectrode for Chronic Neural Interfaces.

    Science.gov (United States)

    Guo, Yuanyuan; Jiang, Shan; Grena, Benjamin J B; Kimbrough, Ian F; Thompson, Emily G; Fink, Yoel; Sontheimer, Harald; Yoshinobu, Tatsuo; Jia, Xiaoting

    2017-07-25

    Microelectrodes provide a direct pathway to investigate brain activities electrically from the external world, which has advanced our fundamental understanding of brain functions and has been utilized for rehabilitative applications as brain-machine interfaces. However, minimizing the tissue response and prolonging the functional durations of these devices remain challenging. Therefore, the development of next-generation microelectrodes as neural interfaces is actively progressing from traditional inorganic materials toward biocompatible and functional organic materials with a miniature footprint, good flexibility, and reasonable robustness. In this study, we developed a miniaturized all polymer-based neural probe with carbon nanofiber (CNF) composites as recording electrodes via the scalable thermal drawing process. We demonstrated that in situ CNF unidirectional alignment can be achieved during the thermal drawing, which contributes to a drastic improvement of electrical conductivity by 2 orders of magnitude compared to a conventional polymer electrode, while still maintaining the mechanical compliance with brain tissues. The resulting neural probe has a miniature footprint, including a recording site with a reduced size comparable to a single neuron and maintained impedance that was able to capture neural activities. Its stable functionality as a chronic implant has been demonstrated with the long-term reliable electrophysiological recording with single-spike resolution and the minimal tissue response over the extended period of implantation in wild-type mice. Technology developed here can be applied to basic chronic electrophysiological studies as well as clinical implementation for neuro-rehabilitative applications.

  14. Braided Multi-Electrode Probes (BMEPs) for Neural Interfaces

    Science.gov (United States)

    Kim, Tae Gyo

    Although clinical use of invasive neural interfaces is very limited, due to safety and reliability concerns, the potential benefits of their use in brain machine interfaces (BMIs) seem promising and so they have been widely used in the research field. Microelectrodes as invasive neural interfaces are the core tool to record neural activities and their failure is a critical issue for BMI systems. Possible sources of this failure are neural tissue motions and their interactions with stiff electrode arrays or probes fixed to the skull. To overcome these tissue motion problems, we have developed novel braided multi-electrode probes (BMEPs). By interweaving ultra-fine wires into a tubular braid structure, we obtained a highly flexible multi-electrode probe. In this thesis we described BMEP designs and how to fabricate BMEPs, and explore experiments to show the advantages of BMEPs through a mechanical compliance comparison and a chronic immunohistological comparison with single 50microm nichrome wires used as a reference electrode type. Results from the mechanical compliance test showed that the bodies of BMEPs have 4 to 21 times higher compliance than the single 50microm wire and the tethers of BMEPs were 6 to 96 times higher compliance, depending on combinations of the wire size (9.6microm or 12.7microm), the wire numbers (12 or 24), and the length of tether (3, 5 or 10 mm). Results from the immunohistological comparison showed that both BMEPs and 50microm wires anchored to the skull caused stronger tissue reactions than unanchored BMEPs and 50microm wires, and 50microm wires caused stronger tissue reactions than BMEPs. In in-vivo tests with BMEPs, we succeeded in chronic recordings from the spinal cord of freely jumping frogs and in acute recordings from the spinal cord of decerebrate rats during air stepping which was evoked by mesencephalic locomotor region (MLR) stimulation. This technology may provide a stable and reliable neural interface to spinal cord

  15. Interface Simulation Distances

    Directory of Open Access Journals (Sweden)

    Pavol Černý

    2012-10-01

    Full Text Available The classical (boolean notion of refinement for behavioral interfaces of system components is the alternating refinement preorder. In this paper, we define a distance for interfaces, called interface simulation distance. It makes the alternating refinement preorder quantitative by, intuitively, tolerating errors (while counting them in the alternating simulation game. We show that the interface simulation distance satisfies the triangle inequality, that the distance between two interfaces does not increase under parallel composition with a third interface, and that the distance between two interfaces can be bounded from above and below by distances between abstractions of the two interfaces. We illustrate the framework, and the properties of the distances under composition of interfaces, with two case studies.

  16. At the Interface of Disciplines

    DEFF Research Database (Denmark)

    Madsen, Dorte

    2017-01-01

    with language instruction, whose features have endured and diffused throughout the business school, ends the presentation set. Symposium discussion will be designed to invite participants, from within the EU and beyond, to join in collaborative practitioner research for the EHEA future."......This Symposium presents curriculum design and content issues in a Scandinavian business school at its Centenary. The aim is an exploration of an educational institution at the interface of the European Higher Education Area (EHEA) within the historical trends of the European Union. We hope...... of interdisciplinarity, use of text production as a tool in support of project and thesis writing, and the use of plurilingual content based teaching in a cooperative learning model for European studies. The history of one curriculum model initiated to educate better citizens, combining interdisciplinary methods...

  17. Instructional Design and the Importance of Instructional Alignment

    Science.gov (United States)

    Martin, Florence

    2011-01-01

    This paper highlights the instructional design process followed by the Maricopa Community College faculty in the creation of instructional modules in Digital Visual Literacy. The paper categorizes 10 tasks that an instructional designer, a teacher, or a trainer performs during the design phase of the instructional design process. The importance of…

  18. Bibliographic Instruction : A Webliography

    Directory of Open Access Journals (Sweden)

    2004-09-01

    Full Text Available A Webliography about the Bibliographic Instruction, it collects a variety of internet resources divided to main categories; directories, articles, bibliographies, organization, mailing lists, and interest groups.

  19. Instructional Technology and Objectification

    National Research Council Canada - National Science Library

    Gur, Bekir S; Wiley, David A

    2008-01-01

    .... A critique of objectification in instructional technology is presented. In the context of Heidegger’s critique of technology, the authors claim that objectification in education is metaphysical in the sense that the intelligibility...

  20. Microcomputer interfacing and applications

    CERN Document Server

    Mustafa, M A

    1990-01-01

    This is the applications guide to interfacing microcomputers. It offers practical non-mathematical solutions to interfacing problems in many applications including data acquisition and control. Emphasis is given to the definition of the objectives of the interface, then comparing possible solutions and producing the best interface for every situation. Dr Mustafa A Mustafa is a senior designer of control equipment and has written many technical articles and papers on the subject of computers and their application to control engineering.

  1. Water at Interfaces

    DEFF Research Database (Denmark)

    Björneholm, Olle; Hansen, Martin Hangaard; Hodgson, Andrew

    2016-01-01

    The interfaces of neat water and aqueous solutions play a prominent role in many technological processes and in the environment. Examples of aqueous interfaces are ultrathin water films that cover most hydrophilic surfaces under ambient relative humidities, the liquid/solid interface which drives...

  2. Perancangan User Interface E-learning Berbasis Web

    OpenAIRE

    Suteja, Bernard Renaldy; Harjoko, Agus

    2008-01-01

    E-Learning steadily growing and the ongoing struggle to convince the skeptics of the potential of e-Learning and online virtual classrooms, quality design is the foundation for a successful distance learning program. The design of the instruction and the design of the user interface are critical elements in providing quality education with a virtual, e-Learning model. This White Paper will focus on the design of the e-Learning user interface (UI). This paper provides examples of user interfac...

  3. Circumventing Graphical User Interfaces in Chemical Engineering Plant Design

    Science.gov (United States)

    Romey, Noel; Schwartz, Rachel M.; Behrend, Douglas; Miao, Peter; Cheung, H. Michael; Beitle, Robert

    2007-01-01

    Graphical User Interfaces (GUIs) are pervasive elements of most modern technical software and represent a convenient tool for student instruction. For example, GUIs are used for [chemical] process design software (e.g., CHEMCAD, PRO/II and ASPEN) typically encountered in the senior capstone course. Drag and drop aspects of GUIs are challenging for…

  4. Electrical stimulation of the proprioceptive cortex (area 3a) used to instruct a behaving monkey.

    Science.gov (United States)

    London, Brian M; Jordan, Luke R; Jackson, Christopher R; Miller, Lee E

    2008-02-01

    A growing number of brain-machine interfaces have now been developed that allow movements of an external device to be controlled using recordings from the brain. This work has been undertaken with a number of different animal models, as well as several human patients with quadriplegia. The resulting movements, whether of computer cursors or robotic limbs, remain quite slow and unstable compared to normal limb movements. It is an open question, how much of this instability is the result of the limited forward control path, and how much has to do with the total lack of normal proprioceptive feedback. We have begun preliminary studies of the effectiveness of electrical stimulation in the proprioceptive area of the primary somatosensory cortex (area 3a) as a potential means to deliver an artificial sense of proprioception to a monkey. We have demonstrated that it is possible for the monkey to detect brief stimulus trains at relatively low current levels, and to discriminate between trains of different frequencies. These observations need to be expanded to include more complex, time-varying waveforms that could potentially convey information about the state of the limb.

  5. Applying learning theories and instructional design models for effective instruction.

    Science.gov (United States)

    Khalil, Mohammed K; Elkhider, Ihsan A

    2016-06-01

    Faculty members in higher education are involved in many instructional design activities without formal training in learning theories and the science of instruction. Learning theories provide the foundation for the selection of instructional strategies and allow for reliable prediction of their effectiveness. To achieve effective learning outcomes, the science of instruction and instructional design models are used to guide the development of instructional design strategies that elicit appropriate cognitive processes. Here, the major learning theories are discussed and selected examples of instructional design models are explained. The main objective of this article is to present the science of learning and instruction as theoretical evidence for the design and delivery of instructional materials. In addition, this article provides a practical framework for implementing those theories in the classroom and laboratory. Copyright © 2016 The American Physiological Society.

  6. Quantization of interface currents

    Energy Technology Data Exchange (ETDEWEB)

    Kotani, Motoko [AIMR, Tohoku University, Sendai (Japan); Schulz-Baldes, Hermann [Department Mathematik, Universität Erlangen-Nürnberg, Erlangen (Germany); Villegas-Blas, Carlos [Instituto de Matematicas, Cuernavaca, UNAM, Cuernavaca (Mexico)

    2014-12-15

    At the interface of two two-dimensional quantum systems, there may exist interface currents similar to edge currents in quantum Hall systems. It is proved that these interface currents are macroscopically quantized by an integer that is given by the difference of the Chern numbers of the two systems. It is also argued that at the interface between two time-reversal invariant systems with half-integer spin, one of which is trivial and the other non-trivial, there are dissipationless spin-polarized interface currents.

  7. Cross Cultural Instruction: An Instructional Design Case

    Directory of Open Access Journals (Sweden)

    Monica W. Tracey

    2010-01-01

    Full Text Available In an authentic example of linking design and development with learning and performance, an international real estate development firm defined a problem; implementing a cleaning system in the largest mall in the world with a cross-cultural unskilled work force in Dubai, UAE. Partnering with a university instructional design team employing a rapid prototyping methodology and the constructivist ID approach, Layers of Negotiation Model, a comprehensive curriculum was designed. This article describes the project background, initial design, the ID team's work in Dubai, illustrates the product, and summarizes the design experience.

  8. Inclusive differentiated instruction

    Directory of Open Access Journals (Sweden)

    Jerković Ljiljana S.

    2017-01-01

    Full Text Available Inclusive differentiated instruction is a new model of didactic instruction, theoretically described and established in this paper for the first time, after being experimentally verified through teaching of the mother tongue (instruction in reading and literature. Inclusive individually planned instruction is based on a phenomenological and constructivist didactic instructional paradigm. This type of teaching is essentially developmental and person-oriented. The key stages of inclusive differentiated instruction of literature are: 1 recognition of individual students' potential and educational needs regarding reading and work on literary texts; 2 planning and preparation of inclusive individually planned instruction in reading and literature; 3 actual class teaching of lessons thus prepared; and 4 evaluation of the student achievement following inclusive differentiated instruction in reading and literature. A highly important element of the planning and preparation of inclusive differentiated instruction is the creation of student profiles and inclusive individualized syllabi. Individualized syllabi specify the following: 1. a brief student profile; 2. the student position on the continuum of the learning outcomes of instruction in the Serbian language; 3. reverse-engineered macro-plan stages of instruction in the Serbian language (3.1. identifying expected outcomes and fundamental qualities of learners' work, 3.2. defining acceptable proofs of their realisation, 3.3. planning learning and teaching experiences, and 3.4. providing material and technical requisites for teaching; 4 the contents and procedure of individualized lessons targeting the student; 5 a plan of syllabus implementation monitoring and evaluation. The continuum of the learning outcomes of inclusive differentiated instruction in literature exists at three main levels, A, B and C. The three levels are: A reading techniques and learning about the main literary theory concepts; B

  9. The Human-Computer Interface and Information Literacy: Some Basics and Beyond.

    Science.gov (United States)

    Church, Gary M.

    1999-01-01

    Discusses human/computer interaction research, human/computer interface, and their relationships to information literacy. Highlights include communication models; cognitive perspectives; task analysis; theory of action; problem solving; instructional design considerations; and a suggestion that human/information interface may be a more appropriate…

  10. Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task

    Science.gov (United States)

    Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A.

    2014-12-01

    Objective. To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like ‘Face in a Crowd’ task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the ‘Crowd’) using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a ‘Crowd Off’ condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet

  11. Advanced aerosense display interfaces

    Science.gov (United States)

    Hopper, Darrel G.; Meyer, Frederick M.

    1998-09-01

    High-resolution display technologies are being developed to meet the ever-increasing demand for realistic detail. The requirement for evermore visual information exceeds the capacity of fielded aerospace display interfaces. In this paper we begin an exploration of display interfaces and evolving aerospace requirements. Current and evolving standards for avionics, commercial, and flat panel displays are summarized and compared to near term goals for military and aerospace applications. Aerospace and military applications prior to 2005 up to UXGA and digital HDTV resolution can be met by using commercial interface standard developments. Advanced aerospace requirements require yet higher resolutions (2560 X 2048 color pixels, 5120 X 4096 color pixels at 85 Hz, etc.) and necessitate the initiation of discussion herein of an 'ultra digital interface standard (UDIS)' which includes 'smart interface' features such as large memory and blazingly fast resizing microcomputer. Interface capacity, IT, increased about 105 from 1973 to 1998; 102 more is needed for UDIS.

  12. Enhancing Instructional Design Efficiency: Methodologies Employed by Instructional Designers

    Science.gov (United States)

    Roytek, Margaret A.

    2010-01-01

    Instructional systems design (ISD) has been frequently criticised as taking too long to implement, calling for a reduction in cycle time--the time that elapses between project initiation and delivery. While instructional design research has historically focused on increasing "learner" efficiencies, the study of what instructional designers do to…

  13. Applying Learning Theories and Instructional Design Models for Effective Instruction

    Science.gov (United States)

    Khalil, Mohammed K.; Elkhider, Ihsan A.

    2016-01-01

    Faculty members in higher education are involved in many instructional design activities without formal training in learning theories and the science of instruction. Learning theories provide the foundation for the selection of instructional strategies and allow for reliable prediction of their effectiveness. To achieve effective learning…

  14. Instructional Conceptions: Analysis from an Instructional Design Perspective

    Science.gov (United States)

    Lowyck, Joost; Elen, Jan; Clarebout, Geraldine

    2004-01-01

    Learners are active actors in learning environments and not mere consumers of instructional designers' products. In line with mediating paradigm instructional conceptions of students are analysed. These conceptions act as cognitive filters that affect students' use of both instructional interventions and support in learning environments. To gain…

  15. Instructional Guidelines. Welding.

    Science.gov (United States)

    Fordyce, H. L.; Doshier, Dale

    Using the standards of the American Welding Society and the American Society of Mechanical Engineers, this welding instructional guidelines manual presents a course of study in accordance with the current practices in industry. Intended for use in welding programs now practiced within the Federal Prison System, the phases of the program are…

  16. Revitalizing Strategy Instruction

    Science.gov (United States)

    Vitalone-Raccaro, Nancy A.

    2017-01-01

    The national focus on teacher accountability and the resulting emphasis on raising the bar for teacher evaluations challenge teachers of students with learning disabilities (LD) to rethink instructional design and delivery. In response to these challenges, this article introduces a two-part protocol for planning and teaching strategy instruction…

  17. Psychologism and Instructional Technology

    Science.gov (United States)

    Gur, Bekir S.; Wiley, David A.

    2009-01-01

    Little of the work in critical and hermeneutical psychology has been linked to instructional technology (IT). This article provides a discussion in order to fill the gap in this direction. The article presents a brief genealogy of American IT in relation to the influence of psychology. It also provides a critical and hermeneutical framework for…

  18. Reviews in instructional video

    NARCIS (Netherlands)

    van der Meij, Hans

    2017-01-01

    This study investigates the effectiveness of a video tutorial for software training whose construction was based on a combination of insights from multimedia learning and Demonstration-Based Training. In the videos, a model of task performance was enhanced with instructional features that were

  19. Grammar Instruction and Technology

    Science.gov (United States)

    Lacina, Jan

    2005-01-01

    Much of the research literature from the past 25 years has supported the importance of teaching grammar in the context of writing instruction (Calkins, 1980; DiStefano & Killion, 1984; Weaver, 1996,1998). Unlike other content areas, practice does not make perfect when learning grammar. While isolated drill and practice of grammatical concepts may…

  20. Instructional Psychology 1976 - 1981,

    Science.gov (United States)

    1982-06-01

    includes evaluative essays on mental measurement and the contributions of E. L. Thorndike, Piaget, Freud , Skinner, and others. A recent book edited by...as a private audio visual aid. Instructional Science, 1980, 9, 295-309. Paolitto, D. P. The effect of cross-age tutoring on adolescence : An inquiry

  1. Instructional Guide for Cosmetology.

    Science.gov (United States)

    Virginia Polytechnic Inst. and State Univ., Blacksburg. Dept. of Education.

    Intended as a tool for cosmetology teachers in Virginia public and private schools, the document is an instructional guide which offers 12 units of study, arranged in a three year course. Materials covered help prepare students for licensure in the State of Virginia and the guide is designed to cover the 1,500 hours required to be spent in the…

  2. Safety Instruction No 43

    CERN Document Server

    2004-01-01

    Please note that the Safety Instruction No 43 (IS 43) entitled "ASBESTOS - DANGERS AND PRECAUTIONS" is available on the web at the following URL: https://edms.cern.ch/document/335809/LAST_RELEASED/ Paper copies can also be obtained from the SC secretariat, e-mail: tis.secretariat@cern.ch. SC Secretariat

  3. Computers in writing instruction

    NARCIS (Netherlands)

    Schwartz, Helen J.; van der Geest, Thea; Smit-Kreuzen, Marlies

    1992-01-01

    For computers to be useful in writing instruction, innovations should be valuable for students and feasible for teachers to implement. Research findings yield contradictory results in measuring the effects of different uses of computers in writing, in part because of the methodological complexity of

  4. New instructional technology.

    Science.gov (United States)

    Martino, Sal; Odle, Teresa

    2008-09-01

    An ASRT task force on new educational delivery methods produced research and resources to guide publication of a white paper titled New Models, New Tools: The Role of Instructional Technology in Radiologic Science Education. This special report summarizes the white paper findings.

  5. Windows into Instructional Practice

    Science.gov (United States)

    Steinbacher-Reed, Christina; Rotella, Sam A.

    2017-01-01

    Administrators are often removed from the daily instructional realities in classrooms, while teachers aren't given enough opportunities to lead in their schools, write Christina Steinbacher-Reed and Sam A. Rotella Jr. The result is a wall that prevents the two parties from collaborating in a way that improves school culture, teaching practices,…

  6. Nuclear Energy. Instructional Materials.

    Science.gov (United States)

    Jordan, Kenneth; Thessing, Dan

    This document is one of five learning packets on alternative energy (see note) developed as part of a descriptive curriculum research project in Arkansas. The overall objectives of the learning packets are to improve the level of instruction in the alternative energies by vocational exploration teachers, and to facilitate the integration of new…

  7. Learning, Instruction, and Hypermedia.

    Science.gov (United States)

    Nelson, Wayne A.; Palumbo, David B.

    1992-01-01

    Examines the psychological basis of hypermedia as a medium for learning, surveys the characteristics of current hypermedia systems, and suggests ways to make hypermedia systems more valuable as instructional environments. Highlights include parallels between human memory and hypermedia architectures; and hypermedia as knowledge presentation,…

  8. Wind Power. Instructional Materials.

    Science.gov (United States)

    Jordan, Kenneth; Thessing, Dan

    This document is one of five learning packets on alternative energy developed as part of a descriptive curriculum research project in Arkansas (see note). The overall objectives of the learning packets are to improve the level of instruction in the alternative energies by vocational exploration teachers, and to facilitate the integration of new…

  9. Developing Effective Instructional Programs.

    Science.gov (United States)

    Sizemore, Barbara; And Others

    A group of three conference papers, all addressing effective instructional programs, is presented in this document. The first paper, entitled "The Organization--A Viable Instrument for Progress" (Barbara Sizemore), addresses the subject of high-achieving, predominantly black elementary schools. Routines in these schools not present in…

  10. Guide to Instructional Videoconferencing.

    Science.gov (United States)

    Matthews, Denise; Reiss, John G.

    An instructional videoconference (IVC) is an interactive delivery mechanism for long-distance communication and education, which uses 2-way audio and 1- or 2-way video to facilitate interaction between presenters and learners who are separated by significant distance. This guide, intended for the staff of federal, state, and local programs that…

  11. Characteristics of Instructional Videos

    Science.gov (United States)

    Beheshti, Mobina; Taspolat, Ata; Kaya, Omer Sami; Sapanca, Hamza Fatih

    2018-01-01

    Nowadays, video plays a significant role in education in terms of its integration into traditional classes, the principal delivery system of information in classes particularly in online courses as well as serving as a foundation of many blended classes. Hence, education is adopting a modern approach of instruction with the target of moving away…

  12. Computer-assisted instruction

    NARCIS (Netherlands)

    Voogt, J.; Fisser, P.; Wright, J.D.

    2015-01-01

    Since the early days of computer technology in education in the 1960s, it was claimed that computers can assist instructional practice and hence improve student learning. Since then computer technology has developed, and its potential for education has increased. In this article, we first discuss

  13. Gaze Interactive Building Instructions

    DEFF Research Database (Denmark)

    Hansen, John Paulin; Ahmed, Zaheer; Mardanbeigi, Diako

    We combine eye tracking technology and mobile tablets to support hands-free interaction with digital building instructions. As a proof-of-concept we have developed a small interactive 3D environment where one can interact with digital blocks by gaze, keystroke and head gestures. Blocks may be moved...

  14. Revisiting "Beyond Instructional Design"

    Science.gov (United States)

    Sims, Rod

    2015-01-01

    Since the article "Beyond Instructional Design: Making Learning Design a Reality" (Sims, 2006) was published, much has changed in the opportunities we have for learning, and Professor Rod Sims's thinking has evolved. In this article, Professor Rod Sims reflects upon his original article, and he offers an evolved model of learning design,…

  15. Individualistic Instructional Design

    Science.gov (United States)

    Sahin, Mehmet Can

    2007-01-01

    This study proposes a new approach to the Instructional Design field. By the constructivism, education systems are moving from a massive structure to the more learner centered and more individualist structure. So far, ID field has adopted and digested the individualism notion partly. This paper proposes an individualistic approach to the…

  16. Differentiated Instruction for Girls

    Science.gov (United States)

    Goebel, Kym

    2010-01-01

    Gender based learning has become an acceptable facet of the differentiated classroom. The female learner has unique needs that can be addressed through differentiated instruction. There are distinct differences between men and women. These differences effect how females approach learning as well as how they retain information. "A fundamental…

  17. Paratransit: An Instructional Module.

    Science.gov (United States)

    Scalici, Anthony

    A concept-based introduction to paratransit is provided in this instructional module for undergraduate and graduate transportation-related courses for disciplines such as engineering, business, marketing, and technology. The concept of paratransit generally refers to modes of transportation other than mass transit and solo-driven automobiles. The…

  18. Orwell's Instructive Errors

    Science.gov (United States)

    Julian, Liam

    2009-01-01

    In this article, the author talks about George Orwell, his instructive errors, and the manner in which Orwell pierced worthless theory, faced facts and defended decency (with fluctuating success), and largely ignored the tradition of accumulated wisdom that has rendered him a timeless teacher--one whose inadvertent lessons, while infrequently…

  19. Coordinating Supplemental Reading Instruction

    Science.gov (United States)

    Deeney, Theresa A.

    2008-01-01

    Although supplemental reading services are meant to improve reading achievement of struggling readers and students with reading disabilities, without concerted effort to ensure communication and coordination with in-school instruction, they may fall short of their desired mark. To promote learning, it is critical that any services provided outside…

  20. Universal computer interfaces

    CERN Document Server

    Dheere, RFBM

    1988-01-01

    Presents a survey of the latest developments in the field of the universal computer interface, resulting from a study of the world patent literature. Illustrating the state of the art today, the book ranges from basic interface structure, through parameters and common characteristics, to the most important industrial bus realizations. Recent technical enhancements are also included, with special emphasis devoted to the universal interface adapter circuit. Comprehensively indexed.

  1. Popeye Project: ROV interface

    Energy Technology Data Exchange (ETDEWEB)

    Scates, C.R.; Hernandez, D.A.; Hickok, D.D.

    1996-12-31

    This paper discusses the Remote Operated Vehicle (ROV) interface with the Popeye Project Subsea System. It describes the ROV-related plans, design philosophies, intervention tasks, tooling/equipment requirements, testing activities, and offshore installation experiences. Early identification and continuous consideration of the ROV interfaces significantly improved the overall efficiency of equipment designs and offshore operations. The Popeye Project helped advance the technology and standardization of ROV interfaces for deep water subsea production systems.

  2. PEDOT:PSS interfaces support the development of neuronal synaptic networks with reduced neuroglia response in vitro

    Directory of Open Access Journals (Sweden)

    Giada eCellot

    2016-01-01

    Full Text Available The design of electrodes based on conductive polymers in brain-machine interface technology offers the opportunity to exploit variably manufactured materials to reduce gliosis, indeed the most common brain response to chronically implanted neural electrodes. In fact, the use of conductive polymers, finely tailored in their physical-chemical properties, might result in electrodes with improved adaptability to the brain tissue and increased charge-transfer efficiency. Here we interfaced poly(3,4-ethylenedioxythiophene:poly(styrene sulfonate (PEDOT:PSS doped with different amounts of ethylene glycol (EG with rat hippocampal primary cultures grown for 3 weeks on these synthetic substrates. We used immunofluorescence and scanning electron microscopy combined to single cell electrophysiology to assess the biocompatibility of PEDOT:PSS in terms of neuronal growth and synapse formation. We investigated neuronal morphology, density and electrical activity. We reported the novel observation that opposite to neurons, glial cell density was progressively reduced, hinting at the ability of this material to down regulate glial reaction. Thus PEDOT:PSS is an attractive candidate for the design of new implantable electrodes, controlling the extent of glial reactivity without affecting neuronal viability and function.

  3. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans

    Science.gov (United States)

    Wolpaw, Jonathan R.; McFarland, Dennis J.

    2004-12-01

    Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains. brain-machine interface | electroencephalography

  4. Electromagnetic Interface Testing Facility

    Data.gov (United States)

    Federal Laboratory Consortium — The Electromagnetic Interface Testing facilitysupports such testing asEmissions, Field Strength, Mode Stirring, EMP Pulser, 4 Probe Monitoring/Leveling System, and...

  5. Individual Differences, Computers, and Instruction.

    Science.gov (United States)

    Ayersman, David J.; Minden, Avril von

    1995-01-01

    Provides a conceptual foundation for the development of hypermedia as an instructional tool for addressing individual differences in learning styles. Highlights include a literature review; computers and instruction; individual differences, computers, and instruction; cognitive controls; cognitive styles and learning; personality types; and future…

  6. Motivational elements in user instructions

    NARCIS (Netherlands)

    Loorbach, N.R.

    2013-01-01

    Concerning the design of user instructions, two view can be distinguished. The traditional view considers instructions as purely instrumental documents. The more and more emerging affective view still assumes that above all, instructions should enable readers to perform tasks. But in order to

  7. Intelligent Frameworks for Instructional Design.

    Science.gov (United States)

    Spector, J. Michael; And Others

    1992-01-01

    Presents a taxonomy describing various uses of artificial intelligence techniques in automated instructional development systems. Instructional systems development is discussed in relation to the design of computer-based instructional courseware; two systems being developed at the Air Force Armstrong Laboratory are reviewed; and further research…

  8. Instructional Leadership Practices in Singapore

    Science.gov (United States)

    Ng, Foo Seong David; Nguyen, Thanh Dong; Wong, Koon Siak Benjamin; Choy, Kim Weng William

    2015-01-01

    This paper presents a review of the literature on principal instructional leadership in Singapore. The authors investigated the dimensions of instructional leadership in the practices of Singapore principals and highlighted the strategies these leaders adopt to enact their instructional roles. Singapore principals were found to play an active role…

  9. Interface colloidal robotic manipulator

    Science.gov (United States)

    Aronson, Igor; Snezhko, Oleksiy

    2015-08-04

    A magnetic colloidal system confined at the interface between two immiscible liquids and energized by an alternating magnetic field dynamically self-assembles into localized asters and arrays of asters. The colloidal system exhibits locomotion and shape change. By controlling a small external magnetic field applied parallel to the interface, structures can capture, transport, and position target particles.

  10. Interfaces in nanoscale photovoltaics

    NARCIS (Netherlands)

    Öner, S.Z.

    2016-01-01

    This thesis deals with material interfaces in nanoscale photovoltaics. Interface properties between the absorbing semiconductor and other employed materials are crucial for an efficient solar cell. While the optical properties are largely unaffected by a few nanometer thin layer, the electronic

  11. User Interface Technology Survey.

    Science.gov (United States)

    1987-04-01

    Interface can be manufactured. The user Interface bulder may be provided with tools to enhance the building block set, e.g.. icon and font editor to add...ity and easy extensiblity of the command set. t supports command history , execu- tion of previous commands, and editing of commands. Through the

  12. Interface, a dispersed architecture

    NARCIS (Netherlands)

    Vissers, C.A.

    1976-01-01

    Past and current specification techniques use timing diagrams and written text to describe the phenomenology of an interface. This paper treats an interface as the architecture of a number of processes, which are dispersed over the related system parts and the message path. This approach yields a

  13. Entanglement and topological interfaces

    Energy Technology Data Exchange (ETDEWEB)

    Brehm, E.; Brunner, I.; Jaud, D.; Schmidt-Colinet, C. [Arnold Sommerfeld Center, Ludwig-Maximilians-Universitaet, Theresienstrasse 37, 80333, Muenchen (Germany)

    2016-06-15

    In this paper we consider entanglement entropies in two-dimensional conformal field theories in the presence of topological interfaces. Tracing over one side of the interface, the leading term of the entropy remains unchanged. The interface however adds a subleading contribution, which can be interpreted as a relative (Kullback-Leibler) entropy with respect to the situation with no defect inserted. Reinterpreting boundaries as topological interfaces of a chiral half of the full theory, we rederive the left/right entanglement entropy in analogy with the interface case. We discuss WZW models and toroidal bosonic theories as examples. (copyright 2016 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  14. User Interface History

    DEFF Research Database (Denmark)

    Jørgensen, Anker Helms; Myers, Brad A

    2008-01-01

    User Interfaces have been around as long as computers have existed, even well before the field of Human-Computer Interaction was established. Over the years, some papers on the history of Human-Computer Interaction and User Interfaces have appeared, primarily focusing on the graphical interface era...... and early visionaries such as Bush, Engelbart and Kay. With the User Interface being a decisive factor in the proliferation of computers in society and since it has become a cultural phenomenon, it is time to paint a more comprehensive picture of its history. This SIG will investigate the possibilities...... of  launching a concerted effort towards creating a History of User Interfaces. ...

  15. After Rigid Interfaces

    DEFF Research Database (Denmark)

    Troiano, Giovanni Maria

    Deformable and shape-changing interfaces are rapidly emerging in the field of human-computer interaction (HCI). Deformable interfaces provide users with newer input possibilities such as bending, squeezing, or stretching, which were impossible to achieve with rigid interfaces. Shape...... sensors in the five preferred objects and programmed them for controlling sounds with computer software. Finally, we ran a performance study where six musicians performed music with deformable interfaces at their studios. Results from the performance study show that musicians systematically map......, Transformation, Adaptation and Physicalization. In synthesis, the work presented in this thesis shows (1) implications of usefulness for deformable interfaces and how their new input modalities can redefine the way users interact with computers, and (2) how a systematic understanding of conventional design...

  16. Safety instruction No. 36

    CERN Multimedia

    SC Secretariat

    2005-01-01

    Please note that a revised version of Safety Instruction No. 36 (IS 36), entitled "Safety rules for the use of static magnetic fields at CERN" is available on the Web at the following url: https://edms.cern.ch/document/335801/LAST_RELEASED Paper copies can also be obtained from the SC unit secretariat (e-mail : sc.secretariat@cern.ch) SC Secretariat

  17. Effective Multicultural Instruction

    Directory of Open Access Journals (Sweden)

    Franklin T. Thompson

    2014-02-01

    Full Text Available The reason why the Trayvon Martin murder trial and similar court cases create a philosophical rift in our nation is due in part to flaws in the delivery of multicultural education. Traditional multicultural instruction does not prepare citizens for the subtleties and complexities of race relations. This study investigates critical strategies and practices that address multicultural missing gaps. I also seek to fill a void in the literature created by a lack of student input regarding teaching strategies that encourage lifelong learning. Students (N = 337 enrolled at a Midwestern university were asked to rate the efficacy of selected instructional strategies. Utilizing a 9-point Likert-type scale, students gave themselves a personal growth rating of 7.15 (SD = 1.47. Variables important to predicting that growth (R2 = .56, p < .0005 were a six-factor variable known as a non-color-blind instructional approach (t = 10.509, p ≤ .0005, allowing students an opportunity to form their own opinions apart from the instructor (t = 4.797, p ≤ .0005, and a state law that mandated multicultural training (t = 3.234, p = .001. Results demonstrated that utilizing a 35% traditional and 65% critical pedagogy mixture when teaching multicultural education helped promote win/win scenarios for education candidates hoping to become difference makers.

  18. Operator interface for vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Bissontz, Jay E

    2015-03-10

    A control interface for drivetrain braking provided by a regenerative brake and a non-regenerative brake is implemented using a combination of switches and graphic interface elements. The control interface comprises a control system for allocating drivetrain braking effort between the regenerative brake and the non-regenerative brake, a first operator actuated control for enabling operation of the drivetrain braking, and a second operator actuated control for selecting a target braking effort for drivetrain braking. A graphic display displays to an operator the selected target braking effort and can be used to further display actual braking effort achieved by drivetrain braking.

  19. Ecological Interface Design

    DEFF Research Database (Denmark)

    Vicente, Kim J.; Rasmussen, Jens

    1992-01-01

    A theoretical framework for designing interfaces for complex human-machine systems is proposed. The framework, called ecological interface design (EID), is based on the skills, rules, knowledge taxonomy of cognitive control. The basic goal of EID is twofold: first, not to force processing...... of other approaches to interface design indicates that EID has a unique and significant contribution to make. Third, the results of an initial empirical evaluation also provide some preliminary support for the EID framework. Some issues for future research are outlined....

  20. The computer graphics interface

    CERN Document Server

    Steinbrugge Chauveau, Karla; Niles Reed, Theodore; Shepherd, B

    2014-01-01

    The Computer Graphics Interface provides a concise discussion of computer graphics interface (CGI) standards. The title is comprised of seven chapters that cover the concepts of the CGI standard. Figures and examples are also included. The first chapter provides a general overview of CGI; this chapter covers graphics standards, functional specifications, and syntactic interfaces. Next, the book discusses the basic concepts of CGI, such as inquiry, profiles, and registration. The third chapter covers the CGI concepts and functions, while the fourth chapter deals with the concept of graphic obje

  1. The interface effect

    CERN Document Server

    Galloway, Alexander R

    2013-01-01

    Interfaces are back, or perhaps they never left. The familiar Socratic conceit from the Phaedrus, of communication as the process of writing directly on the soul of the other, has returned to center stage in today's discussions of culture and media. Indeed Western thought has long construed media as a grand choice between two kinds of interfaces. Following the optimistic path, media seamlessly interface self and other in a transparent and immediate connection. But, following the pessimistic path, media are the obstacles to direct communion, disintegrating self and other into misunderstanding

  2. The Java Legacy Interface

    DEFF Research Database (Denmark)

    Korsholm, Stephan

    2007-01-01

    The Java Legacy Interface is designed to use Java for encapsulating native legacy code on small embedded platforms. We discuss why existing technologies for encapsulating legacy code (JNI) is not sufficient for an important range of small embedded platforms, and we show how the Java Legacy...... Interface offers this previously missing functionality. We describe an implementation of the Java Legacy Interface for a particular virtual machine, and how we have used this virtual machine to integrate Java with an existing, commercial, soft real-time, C/C++ legacy platform....

  3. Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm

    Science.gov (United States)

    Dura-Bernal, Salvador; Zhou, Xianlian; Neymotin, Samuel A.; Przekwas, Andrzej; Francis, Joseph T.; Lytton, William W.

    2015-01-01

    Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of

  4. Cortical spiking network interfaced with virtual musculoskeletal arm and robotic arm

    Directory of Open Access Journals (Sweden)

    Salvador eDura-Bernal

    2015-11-01

    Full Text Available Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm.This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuro-prosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility

  5. Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm.

    Science.gov (United States)

    Dura-Bernal, Salvador; Zhou, Xianlian; Neymotin, Samuel A; Przekwas, Andrzej; Francis, Joseph T; Lytton, William W

    2015-01-01

    Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of

  6. Introduction to interfaces 3

    DEFF Research Database (Denmark)

    Mortensen, Lars Boje; Høgel, Christian; Borsa, Paolo

    2017-01-01

    The Editors introduce Issue No. 3 of Interfaces: A Journal of Medieval European Literatures, dedicated to "Rediscovery and Canonization: The Roman Classics in the Middle Ages," and offer a general overview of the matter and contents of the contributions....

  7. Pattern formation at interfaces

    CERN Document Server

    Maier, Giulio; Nepomnyashchy, Alexander

    2010-01-01

    Applying modern nonlinear stability theory to problems of continuous media mechanics in the presence of interfaces, this text is relevant to materials science, chemical engineering, and heat transfer technologies, as well as to reaction-diffusion systems.

  8. Natural gesture interfaces

    Science.gov (United States)

    Starodubtsev, Illya

    2017-09-01

    The paper describes the implementation of the system of interaction with virtual objects based on gestures. The paper describes the common problems of interaction with virtual objects, specific requirements for the interfaces for virtual and augmented reality.

  9. User interface development

    Science.gov (United States)

    Aggrawal, Bharat

    1994-01-01

    This viewgraph presentation describes the development of user interfaces for OS/2 versions of computer codes for the analysis of seals. Current status, new features, work in progress, and future plans are discussed.

  10. Interface Anywhere Project

    Data.gov (United States)

    National Aeronautics and Space Administration — To illustrate the viability of this technology, a prototype Natural User Interface (NUI) was developed as a proof-of-concept for system control.  Gesture and...

  11. USING GOOGLE+ FOR INSTRUCTION

    Directory of Open Access Journals (Sweden)

    Kevin YEE

    Full Text Available Introduced in July, 2011 in a beta test of invited users only, the new social media service Google+ (or G+ quickly spread by word of mouth, and Google leader Larry Page (2011 blogged that within sixteen days it had 10 million users. By August, it had 25 million users (Cashmore, 2011. Even with slower growth ahead (still with no marketing budget, the service looks likely to crest 100 million users perhaps as early as ten months, a feat that took Facebook three years. Other social networks, most notably Facebook and Twitter, have been used increasingly as instructional tools, since they are platforms with which students are already familiar (Maloney, 2007; McLoughlin & Lee, 2007. Selwyn (2009 found that students often eschew official channels for communication in favor of less formal community-based formats such as Facebook, implying a growing need for instructional communication tools that will be used willingly by students. The question is whether Google+ can be used like Twitter or Facebook to augment instruction, or even, perhaps, to improve upon those predecessors for academic purposes. Google+ is like Twitter in that anyone can follow a given user’s posts. There is no direct “friend” relationship required to read the posts written by others. However, it also approximates some features of Facebook. Rather than friends sorted into “lists” like in Facebook, Google+ allows users to place feeds into one or more “circles,” the better to monitor (or control the flow of information to and from different audiences. Circles are more intuitive, and more central to the experience, than the Facebook lists. They provide an explicit organizational structure, compared to the less-obvious listing functionality, which feels like an afterthought, found in Facebook.

  12. Lectures on random interfaces

    CERN Document Server

    Funaki, Tadahisa

    2016-01-01

    Interfaces are created to separate two distinct phases in a situation in which phase coexistence occurs. This book discusses randomly fluctuating interfaces in several different settings and from several points of view: discrete/continuum, microscopic/macroscopic, and static/dynamic theories. The following four topics in particular are dealt with in the book. Assuming that the interface is represented as a height function measured from a fixed-reference discretized hyperplane, the system is governed by the Hamiltonian of gradient of the height functions. This is a kind of effective interface model called ∇φ-interface model. The scaling limits are studied for Gaussian (or non-Gaussian) random fields with a pinning effect under a situation in which the rate functional of the corresponding large deviation principle has non-unique minimizers. Young diagrams determine decreasing interfaces, and their dynamics are introduced. The large-scale behavior of such dynamics is studied from the points of view of the hyd...

  13. Metabolic Instruction of Immunity.

    Science.gov (United States)

    Buck, Michael D; Sowell, Ryan T; Kaech, Susan M; Pearce, Erika L

    2017-05-04

    Choices have consequences. Immune cells survey and migrate throughout the body and sometimes take residence in niche environments with distinct communities of cells, extracellular matrix, and nutrients that may differ from those in which they matured. Imbedded in immune cell physiology are metabolic pathways and metabolites that not only provide energy and substrates for growth and survival, but also instruct effector functions, differentiation, and gene expression. This review of immunometabolism will reference the most recent literature to cover the choices that environments impose on the metabolism and function of immune cells and highlight their consequences during homeostasis and disease. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. EST Vocabulary Instruction

    Directory of Open Access Journals (Sweden)

    Célia D.S. Bell

    2012-05-01

    Full Text Available This study aims at contributing to the investigation on the instruction of EST (English for Science and Technology vocabulary, in terms of receptive use of the language. It evaluates the effectiveness of two teaching approaches to the acquisition of vocabulary. The first approach consisted of teaching vocabulary through the use of dictionaries, where the words were merely translated into the learners’ L1 or defined in the target language thus promoting superficial level of word processing. The second approach employed activities promoting deep level of word processing. Data were analysed quantitatively. Results indicated that the two approaches seem to have some equipotentiality, as far as EST vocabulary is concerned.

  15. Architecture of a Computer Based Instructional System

    Directory of Open Access Journals (Sweden)

    Emilia PECHEANU

    2000-12-01

    Full Text Available The paper describes the architecture of a tutorial system that can be used at various engineering graduate and postgraduate courses. The tutorial is using Internet-style WWW services to provide access to the teaching information and the evaluation exercises maintained with a RDMS. The tutorial will consist of server-side applications that process and present teaching material and assessing exercises to the student using the well-known Web interface. All information in the system will be stored in a relational database. By closely sticking to the ANSI SQL specifications, the system can take advantage of a free database managing system running on Linux, the mini-SQL. The tutorial can be used to on-line deliver any courses, creating new, continuing education opportunities. Taking advantage of the modern deployment techniques, the instructional/assessing tutorial offer high degrees of accessibility.

  16. Future development of instructional television

    Science.gov (United States)

    Barnett, H. J.; Denzau, A. T.; Dumolin, J. R.; Singh, J. P.

    1971-01-01

    The use of television in schools as an aid to instruction is proposed for individualized instruction, repetition for slow learners, acceleration for fast learners, and lectures from the best teachers for all students. A dedicated school district cable system, a 40 channel cable to each school and classroom, is considered. This innovation offers an opportunity for improving the quality and content of the school's instruction and for reducing the cost.

  17. Machine Learning in Proof General: Interfacing Interfaces

    Directory of Open Access Journals (Sweden)

    Ekaterina Komendantskaya

    2013-07-01

    Full Text Available We present ML4PG - a machine learning extension for Proof General. It allows users to gather proof statistics related to shapes of goals, sequences of applied tactics, and proof tree structures from the libraries of interactive higher-order proofs written in Coq and SSReflect. The gathered data is clustered using the state-of-the-art machine learning algorithms available in MATLAB and Weka. ML4PG provides automated interfacing between Proof General and MATLAB/Weka. The results of clustering are used by ML4PG to provide proof hints in the process of interactive proof development.

  18. Mass and charge transport in IPMC actuators with fractal interfaces

    Science.gov (United States)

    Chang, Longfei; Wu, Yucheng; Zhu, Zicai; Li, Heng

    2016-04-01

    Ionic Polymer-Metal Composite (IPMC) actuators have been attracting a growing interest in extensive applications, which consequently raises the demands on the accuracy of its theoretical modeling. For the last few years, rough landscape of the interface between the electrode and the ionic membrane of IPMC has been well-documented as one of the key elements to ensure a satisfied performance. However, in most of the available work, the interface morphology of IPMC was simplified with structural idealization, which lead to perplexity in the physical interpretation on its interface mechanism. In this paper, the quasi-random rough interface of IPMC was described with fractal dimension and scaling parameters. And the electro-chemical field was modeled by Poisson equation and a properly simplified Nernst-Planck equation set. Then, by simulation with Finite Element Method, a comprehensive analysis on he inner mass and charge transportation in IPMC actuators with different fractal interfaces was provided, which may be further adopted to instruct the performance-oriented interface design for ionic electro-active actuators. The results also verified that rough interface can impact the electrical and mechanical response of IPMC, not only from the respect of the real surface increase, but also from mass distribution difference caused by the complexity of the micro profile.

  19. Allocating instruction time: How language instruction can affect multiple skills

    NARCIS (Netherlands)

    Borghans, L.; Diris, R.E.M.

    2014-01-01

    There exists substantial variation in how schools allocate instruction time to school subjects. The effectiveness of that allocation depends on the immediate effect of instruction in one subject on achievement in the same subject, on how skills further develop over time, and on possible spillover

  20. The Instructional Network: Using Facebook to Enhance Undergraduate Mathematics Instruction

    Science.gov (United States)

    Gregory, Peter; Gregory, Karen; Eddy, Erik

    2014-01-01

    Facebook is a website with over one billion users worldwide that is synonymous with social-networking. However, in this study, Facebook is used as an "instructional network". Two sections of an undergraduate calculus course were used to study the effects of participating in a Facebook group devoted solely to instruction. One section was…

  1. Evaluation of Instructional Design Capabilities of Asynchronous and Synchronous Instruction

    Science.gov (United States)

    Garrett, Kristi N.; Benson, Angela D.

    2017-01-01

    From a quantitative perspective, this study examined the instructional design knowledge of higher education instructors and others within the instructional design/technology arena who are members of a global educational based Internet forum. Results showed significant difference in opinions between genders, where males were more inclined to…

  2. Interfacing Sensors To Micro Controllers

    KAUST Repository

    Norain, Mohamed

    2018-01-15

    This lecture will cover the most common interface and interface techniques between sensors and microcontrollers. The presentation will introduce the pros and cons of each interface type including analogue, digital and serial output sensors. It will also cover the basic required electronics knowledge to help you in selecting and designing your next sensor to microcontroller interface.

  3. High-bandwidth memory interface

    CERN Document Server

    Kim, Chulwoo; Song, Junyoung

    2014-01-01

    This book provides an overview of recent advances in memory interface design at both the architecture and circuit levels. Coverage includes signal integrity and testing, TSV interface, high-speed serial interface including equalization, ODT, pre-emphasis, wide I/O interface including crosstalk, skew cancellation, and clock generation and distribution. Trends for further bandwidth enhancement are also covered.   • Enables readers with minimal background in memory design to understand the basics of high-bandwidth memory interface design; • Presents state-of-the-art techniques for memory interface design; • Covers memory interface design at both the circuit level and system architecture level.

  4. Designing Instruction with Learning Objects.

    Science.gov (United States)

    Hamel, Cheryl J.; Ryan-Jones, David

    2002-01-01

    Discussion of online learning and standards for web-based and computer-based courseware focuses on learning objects, defined here as small, stand-alone units of instruction that can be tagged with descriptors and stored for reuse in various instructional contexts. Presents principles of learning object design and guidelines for assuring that…

  5. Instructional Model for Concept Acquisition.

    Science.gov (United States)

    Tennyson, Robert D.

    The purpose of this paper is to demonstrate the feasibility of applying research variables for concept acquisition into a generalized instructional model for teaching concepts. This paper does not present the methodology for the decision/selection stages in designing the actual instruction task, but offers references to other sources which give…

  6. Science Approach to Instructional Development.

    Science.gov (United States)

    Reigeluth, Charles M.; And Others

    1981-01-01

    Distinguishes between the process and products of science and describes the role of each in Instructional Development (ID). The observe, hypothesize, test components of the scientific process are examined as they apply to front-end analysis, instructional strategy description, and formative evaluation stages used in ID models. Thirty-four…

  7. Critical Analysis of Instructional Design.

    Science.gov (United States)

    Li, Ming-Fen; Reigeluth, Charles M.

    The discussion of the critical analysis of instructional design is framed within Habermas' three fundamental human interests: technical, practical, and emancipatory. The primary goal of this paper is to explore alternative approaches for instructional designers' reflection and critique. Ultimately, this reflection and critique should shed light on…

  8. Professional Cosmetology Practices. Instructional Units.

    Science.gov (United States)

    Hopcus, Sharron; Armstrong, Ivan J.

    This publication is designed to assist the instructor and students in understanding the latest concepts and techniques of the instructional phase of cosmetology programs. The instructional units are in five areas: (1) orientation, (2) professional practices: hair, (3) professional practices: skin and nails, (4) cosmetology science, and (5)…

  9. Unpacking Corrections in Mobile Instruction

    DEFF Research Database (Denmark)

    Levin, Lena; Cromdal, Jakob; Broth, Mathias

    2017-01-01

    This article deals with the organisation of correction in mobile instructional settings. Five sets of video data (>250 h) documenting how learners were instructed to fly aeroplanes, drive cars and ride bicycles in real life traffic were examined to reveal some common features of correction exchan...

  10. Instructional Theory for Teaching Statistics.

    Science.gov (United States)

    Atwood, Jan R.; Dinham, Sarah M.

    Metatheoretical analysis of Ausubel's Theory of Meaningful Verbal Learning and Gagne's Theory of Instruction using the Dickoff and James paradigm produced two instructional systems for basic statistics. The systems were tested with a pretest-posttest control group design utilizing students enrolled in an introductory-level graduate statistics…

  11. PROGRAMMED INSTRUCTION AND LANGUAGE LEARNING.

    Science.gov (United States)

    LUELSDORFF, PHILIP A.

    PROGRAMED INSTRUCTION, A TEACHING METHOD WHICH INCORPORATES (1) A DETAILED SPECIFICATION OF TERMINAL BEHAVIOR, (2) A CAREFUL SEQUENCING OF THE MATERIAL INTO GRADED STEPS, AND (3) THE REINFORCEMENT OF STUDENT RESPONSE, WORKS MORE FAVORABLY IN CERTAIN INSTRUCTIONAL MEDIA THAN IN OTHERS. CARROLL AND SKINNER BELIEVE THAT SUCCESS IN PROGRAMED…

  12. Using Microcomputers for Composition Instruction.

    Science.gov (United States)

    Cronnell, Bruce; Humes, Ann

    One of the most valuable uses of microcomputers and word processors in composition instruction is in the teaching of writing revision. A number of activities can be carried out with these tools; for example, (1) after appropriate instruction on revision, students can be given prewritten text and asked to revise it on the word processors; (2) after…

  13. Differentiated Instruction in the Classroom

    Science.gov (United States)

    Kelly, Gretchen

    2013-01-01

    Low achievement on standardized tests may be attributed to many factors, including teaching methods. Differentiated instruction has been identified as a teaching method using different learning modalities that appeal to varied student interests with individualized instruction. The purpose of this quantitative study was to compare whole-group…

  14. Workshop on Interface Phenomena

    CERN Document Server

    Kreuzer, Hans

    1987-01-01

    This book contains the proceedings of the first Workshop on Interface Phenomena, organized jointly by the surface science groups at Dalhousie University and the University of Maine. It was our intention to concentrate on just three topics related to the kinetics of interface reactions which, in our opinion, were frequently obscured unnecessarily in the literature and whose fundamental nature warranted an extensive discussion to help clarify the issues, very much in the spirit of the Discussions of the Faraday Society. Each session (day) saw two principal speakers expounding the different views; the session chairmen were asked to summarize the ensuing discussions. To understand the complexity of interface reactions, paradigms must be formulated to provide a framework for the interpretation of experimen­ tal data and for the construction of theoretical models. Phenomenological approaches have been based on a small number of rate equations for the concentrations or mole numbers of the various species involved i...

  15. Portraying User Interface History

    DEFF Research Database (Denmark)

    Jørgensen, Anker Helms

    2008-01-01

    The user interface is coming of age. Papers adressing UI history have appeared in fair amounts in the last 25 years. Most of them address particular aspects such as an in­novative interface paradigm or the contribution of a visionary or a research lab. Contrasting this, papers addres­sing UI...... history at large have been sparse. However, a small spate of publications appeared recently, so a reasonable number of papers are available. Hence this work-in-progress paints a portrait of the current history of user interfaces at large. The paper first describes a theoretical framework recruited from...... in that they largely address prevailing UI techno­logies, and thirdly history from above in that they focus on the great deeds of the visionaries. The paper then compares this state-of-art in UI history to the much more mature fields history of computing and history of technology. Based hereon, some speculations...

  16. Unaligned instruction relocation

    Energy Technology Data Exchange (ETDEWEB)

    Bertolli, Carlo; O' Brien, John K.; Sallenave, Olivier H.; Sura, Zehra N.

    2018-01-23

    In one embodiment, a computer-implemented method includes receiving source code to be compiled into an executable file for an unaligned instruction set architecture (ISA). Aligned assembled code is generated, by a computer processor. The aligned assembled code complies with an aligned ISA and includes aligned processor code for a processor and aligned accelerator code for an accelerator. A first linking pass is performed on the aligned assembled code, including relocating a first relocation target in the aligned accelerator code that refers to a first object outside the aligned accelerator code. Unaligned assembled code is generated in accordance with the unaligned ISA and includes unaligned accelerator code for the accelerator and unaligned processor code for the processor. A second linking pass is performed on the unaligned assembled code, including relocating a second relocation target outside the unaligned accelerator code that refers to an object in the unaligned accelerator code.

  17. Unaligned instruction relocation

    Energy Technology Data Exchange (ETDEWEB)

    Bertolli, Carlo; O' Brien, John K.; Sallenave, Olivier H.; Sura, Zehra N.

    2017-10-17

    In one embodiment, a computer-implemented method includes receiving source code to be compiled into an executable file for an unaligned instruction set architecture (ISA). Aligned assembled code is generated, by a computer processor. The aligned assembled code complies with an aligned ISA and includes aligned processor code for a processor and aligned accelerator code for an accelerator. A first linking pass is performed on the aligned assembled code, including relocating a first relocation target in the aligned accelerator code that refers to a first object outside the aligned accelerator code. Unaligned assembled code is generated in accordance with the unaligned ISA and includes unaligned accelerator code for the accelerator and unaligned processor code for the processor. A second linking pass is performed on the unaligned assembled code, including relocating a second relocation target outside the unaligned accelerator code that refers to an object in the unaligned accelerator code.

  18. Interface or Interlace?

    DEFF Research Database (Denmark)

    Hansen, Lone Koefoed; Wamberg, Jacob

    2005-01-01

    Departing from an analysis of the computer's indeterminate location between medium and machine, this paper problematises the idea of a clear-cut interface in complex computing, especially Augmented Reality. The idea and pratice of the interface is derived from the medium as a representational...... surface and thus demands the overview of an autonomous consciouness. Instead we introduce the term interlace, a mingling of representational and physical levels, thus describing the computer's ambiguous blending of imaginary and real. The proposition is demonstrated through analysis of different recent...

  19. UIL -User Interface Language

    CERN Document Server

    Lewis, J; CERN. Geneva

    1990-01-01

    Some widget examples, widget categories, the push button widget, menus, the FORM widget, using UIL for an application program, the MOTIF Resource Manager (MRM), execution thread of an application using UIL and MRM, opening hierarchies, binding UIL names to application addresses, fetching widget hierarchies and managing them, changing widget resources using UIL and MRM, fetching literal values from the UID file. Introduction to the User Interface Language, defining a user interface, advantages of using UIL, accessing UID files from the application, UIL Syntax, the UIL module structure, defining a widget instance hierarchy, declaration of literals colors, icons, fonts

  20. Politics at the interface

    DEFF Research Database (Denmark)

    Kannabiran, Gobinaath; Petersen, Marianne Graves

    2010-01-01

    the process of design and into how users interact with the designed product on a day-to-day basis. This paper is an attempt to call to attention the need for a new set of methods, attitudes and approaches, along with the existing, to discuss, analyze and reflect upon the politics at the interface....... By presenting a critical analysis of two design cases, we elicit the importance of such an agenda and the implications for design in doing so. We use the Foucauldian notion of power to analyze the power relationships in these two cases and to articulate the politics at the interface. We conclude by emphasizing...

  1. ARS-Media for excel instruction manual

    Science.gov (United States)

    ARS-Media for Excel Instruction Manual is the instruction manual that explains how to use the Excel spreadsheet ARS-Media for Excel application. ARS-Media for Excel Instruction Manual is provided as a pdf file....

  2. Intelligent Educational Systems for Anchored Instruction?

    Science.gov (United States)

    Kumar, David D.

    1995-01-01

    Explores the potential for using Intelligent Educational Systems (IES) for anchoring instruction in macro contexts in science education. Topics include anchored instruction; situated cognition; problem solving; cognitivism; interactive video environments; and examples of combining IES and anchored instruction. (LRW)

  3. Fencing direct memory access data transfers in a parallel active messaging interface of a parallel computer

    Science.gov (United States)

    Blocksome, Michael A.; Mamidala, Amith R.

    2013-09-03

    Fencing direct memory access (`DMA`) data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint including specifications of a client, a context, and a task, the endpoints coupled for data communications through the PAMI and through DMA controllers operatively coupled to segments of shared random access memory through which the DMA controllers deliver data communications deterministically, including initiating execution through the PAMI of an ordered sequence of active DMA instructions for DMA data transfers between two endpoints, effecting deterministic DMA data transfers through a DMA controller and a segment of shared memory; and executing through the PAMI, with no FENCE accounting for DMA data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all DMA instructions initiated prior to execution of the FENCE instruction for DMA data transfers between the two endpoints.

  4. Fencing data transfers in a parallel active messaging interface of a parallel computer

    Science.gov (United States)

    Blocksome, Michael A.; Mamidala, Amith R.

    2015-06-02

    Fencing data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task; the compute nodes coupled for data communications through the PAMI and through data communications resources including at least one segment of shared random access memory; including initiating execution through the PAMI of an ordered sequence of active SEND instructions for SEND data transfers between two endpoints, effecting deterministic SEND data transfers through a segment of shared memory; and executing through the PAMI, with no FENCE accounting for SEND data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all SEND instructions initiated prior to execution of the FENCE instruction for SEND data transfers between the two endpoints.

  5. Data communications in a parallel active messaging interface of a parallel computer

    Science.gov (United States)

    Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E

    2013-10-29

    Data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the parallel computer including a plurality of compute nodes that execute a parallel application, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes and the endpoints coupled for data communications through the PAMI and through data communications resources, including receiving in an origin endpoint of the PAMI a data communications instruction, the instruction characterized by an instruction type, the instruction specifying a transmission of transfer data from the origin endpoint to a target endpoint and transmitting, in accordance with the instruction type, the transfer data from the origin endpoint to the target endpoint.

  6. INSTRUCTIONAL DESIGN FOR TEACHERS: Improving classroom practice

    OpenAIRE

    Reviewed by Yavuz Akbulut

    2011-01-01

    The key to successful learning in most formal instructional settings is effective instructional design (ID). Instructional design for teachers serves as an organized source of directions, which can help classroom teachers to integrate available resources to improve students‘ acquisition of the instructional goals. The book is consisted of 151 pages (+xvii) covering eight chapters which address a commonsense model of instructional design to guide K-12 teachers during their unique instructional...

  7. Photochemistry at Interfaces

    Energy Technology Data Exchange (ETDEWEB)

    Eisenthal, Kenneth B [Columbia Univ., New York, NY (United States)

    2015-02-24

    We have advanced our capabilities to investigate ultrafast excited state dynamics at a liquid interface using a pump to excite molecules to higher electronic states and then probe the subsequent time evolution of the interfacial molecules with femtosecond time delayed vibrational SFG.

  8. The Liquid Vapour Interface

    DEFF Research Database (Denmark)

    Als-Nielsen, Jens Aage

    1985-01-01

    In this short review we are concerned with the density variation across the liquid-vapour interface, i.e. from the bulk density of the liquid to the essentially zero density of the vapour phase. This density variation can in principle be determined from the deviation of the reflectivity from...

  9. Source interface for ALICE

    CERN Multimedia

    Patrice Loïez

    2001-01-01

    This interface is part of the ALICE detector data link (DDL), which transmits data at 100 Mbytes/sec from the detectors to a host computer. A total of 400 DDLs will be installed on ALICE. These silicon devices have been developed especially for use in the high radiation levels produced in detector environments.

  10. Urban Sound Interfaces

    DEFF Research Database (Denmark)

    Breinbjerg, Morten

    2012-01-01

    This paper draws on the theories of Michel de Certeau and Gaston Bachelard to discuss how media architecture, in the form of urban sound interfaces, can help us perceive the complexity of the spaces we inhabit, by exploring the history and the narratives of the places in which we live...

  11. A Thermistor Interface.

    Science.gov (United States)

    Kamin, Gary D.; Dowden, Edward

    1987-01-01

    Describes the use of a precalibrated stainless steel thermistor, interfaced with an Apple computer, in chemistry experiments. Discusses the advantages of "instant" temperature readings in experiments requiring that readings be taken at certain intervals. Outlines such an experiment which investigates freezing point depressions. (TW)

  12. Interface transfer of equipment

    Energy Technology Data Exchange (ETDEWEB)

    Ashton, I.J.

    1989-04-01

    This article details the interface transfer of heavy-duty face equipment from 5's to 6's face in the Great Row Seam at Silverdale Colliery, British Coal, Western Area. The salvaged face was roofbolted using leg-mounted Wombat drilling rigs. All heavy-duty equipment was transported by FSV's. 5 figs.

  13. Introduction to 'Interfaces' 3

    Directory of Open Access Journals (Sweden)

    Paolo Borsa

    2017-06-01

    Full Text Available The Editors introduce Issue No. 3 of Interfaces: A Journal of Medieval European Literatures, dedicated to "Rediscovery and Canonization: The Roman Classics in the Middle Ages," and offer a general overview of the matter and contents of the contributions.

  14. Is the interface OK?

    Digital Repository Service at National Institute of Oceanography (India)

    Suresh, T.

    When a peripheral device fails, software methods can be initially resorted to before the usual hardware test procedures are used. A test program is presented here that allows various peripherals, inter-faced to a Norsk Data computer, to be tested...

  15. Articulation of Medium of Instruction Politics in the Malaysian Chinese Press

    Science.gov (United States)

    Samuel, Moses; Khan, Mahmud Hasan; Ng, Lee Luan; Cheang, Kin Wai

    2014-01-01

    In postcolonial multilingual societies, matters of education are deeply rooted in the discourse of ethnicity. In Malaysia, the interface between ethnicity and education is reflected in recent debates on the choice of medium of instruction (MOI). In 2002, the Malaysian government introduced English as MOI by replacing Malay, the national language,…

  16. Workflow User Interfaces Patterns

    Directory of Open Access Journals (Sweden)

    Jean Vanderdonckt

    2012-03-01

    Full Text Available Este trabajo presenta una colección de patrones de diseño de interfaces de usuario para sistemas de información para el flujo de trabajo; la colección incluye cuarenta y tres patrones clasificados en siete categorías identificados a partir de la lógica del ciclo de vida de la tarea sobre la base de la oferta y la asignación de tareas a los responsables de realizarlas (i. e. recursos humanos durante el flujo de trabajo. Cada patrón de la interfaz de usuario de flujo de trabajo (WUIP, por sus siglas en inglés se caracteriza por las propiedades expresadas en el lenguaje PLML para expresar patrones y complementado por otros atributos y modelos que se adjuntan a dicho modelo: la interfaz de usuario abstracta y el modelo de tareas correspondiente. Estos modelos se especifican en un lenguaje de descripción de interfaces de usuario. Todos los WUIPs se almacenan en una biblioteca y se pueden recuperar a través de un editor de flujo de trabajo que vincula a cada patrón de asignación de trabajo a su WUIP correspondiente.A collection of user interface design patterns for workflow information systems is presented that contains forty three resource patterns classified in seven categories. These categories and their corresponding patterns have been logically identified from the task life cycle based on offering and allocation operations. Each Workflow User Interface Pattern (WUIP is characterized by properties expressed in the PLML markup language for expressing patterns and augmented by additional attributes and models attached to the pattern: the abstract user interface and the corresponding task model. These models are specified in a User Interface Description Language. All WUIPs are stored in a library and can be retrieved within a workflow editor that links each workflow pattern to its corresponding WUIP, thus giving rise to a user interface for each workflow pattern.

  17. 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.

  18. 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.

  19. The Characteristics of Ernst Meyer's Group Instruction in Relation to Frontal Instruction

    OpenAIRE

    吉田, 茂孝

    2007-01-01

    The purpose of this paper is to clarify the characteristics of Ernst Meyer's group instruction in relation to frontal instruction. In this paper, the following three points are analyzed. (1) In the historical development of German didactics, how Ernst Meyer's group instruction is positioned is clarified. (2) Ernst Meyer's frontal instruction and the form of his instruction are clarified, and the position of Ernst Meyer's group instruction in the theory of the instructional form is analyzed. (...

  20. A New Robotics Instructional Laboratory.

    Science.gov (United States)

    Shahinpoor, Mohsen; Singer, Neal

    1985-01-01

    An instructional robotics laboratory that is unique in the United States was created in 1984 at the University of New Mexico. Descriptions of the laboratory, course work offered, student projects, and other areas are provided. (JN)

  1. Measurement control workshop instructional materials

    Energy Technology Data Exchange (ETDEWEB)

    Gibbs, Philip [Brookhaven National Lab. (BNL), Upton, NY (United States); Crawford, Cary [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); McGinnis, Brent [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Insolves LLC, Piketon, OH (United States)

    2014-04-01

    A workshop to teach the essential elements of an effective nuclear materials control and accountability (MC&A) programs are outlined, along with the modes of Instruction, and the roles and responsibilities of participants in the workshop.

  2. Intelligent Tools and Instructional Simulations

    National Research Council Canada - National Science Library

    Murray, William R; Sams, Michelle; Belleville, Michael

    2001-01-01

    This intelligent tools and instructional simulations project was an investigation into the utility of a knowledge-based performance support system to support learning and on-task performance for using...

  3. Zoology by Self-Instruction

    Science.gov (United States)

    Roach, Keith; Hammond, Roger

    1976-01-01

    A historical account is given of how a conventional university first-year undergraduate course in zoology has been replaced by a self-instructional one. Advantages and problems are weighed, and successful student achievement and interest are described. (LBH)

  4. Hypermedia Environments and Adaptive Instruction.

    Science.gov (United States)

    Federico, Pat-Anthony

    1999-01-01

    Reviews relevant professional literature concerning hypermedia environments and adaptive instruction for online learning for distance education and continuing education. Highlights include aptitude-treatment interaction; cognitive processes; navigational paths; log files; and intelligent tutors. Contains 125 references. (LRW)

  5. Designing Instruction for Distance Learning

    National Research Council Canada - National Science Library

    Main, Robert

    1998-01-01

    .... While distance learning has been demonstrated to be an effective and efficient tool for increased access it also requires greater emphasis on instructional design and instructor training to obtain satisfactory results...

  6. 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.

  7. 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

  8. User interface design considerations

    DEFF Research Database (Denmark)

    Andersen, Simon Engedal; Jakobsen, Arne; Rasmussen, Bjarne D.

    1999-01-01

    When designing a user interface for a simulation model there are several important issues to consider: Who is the target user group, and which a priori information can be expected. What questions do the users want answers to and what questions are answered using a specific model?When developing...... and output variables. This feature requires special attention when designing the user interface and a special approach for controlling the user selection of input and output variables are developed. To obtain a consistent system description the different input variables are grouped corresponding...... the consequence that the user does not have to specify any start guesses, etc.The design approach developed have resulted in a number of simulation tools which allow users with limited theoretical knowledge about refrigeration systems, mathematical models and simulation to use them while the expert users still...

  9. Popeye Project: ROV interfaces

    Energy Technology Data Exchange (ETDEWEB)

    Scates, C.R. [Shell Oil Inc., New Orleans, LA (United States); Hickok, D.D. [Dvaerner FSSL Inc., Houston, TX (United States); Hernandez, D.A.

    1997-04-01

    The Popeye Project in the Gulf of Mexico helped advance the technology and standardization of ROV interfaces for deepwater subsea production systems. Some of the many successful ROV operations during installation and completion were {open_quotes}first-of-it`s-kind{close_quotes} activities-enabled by many technical advances. The use and reliance upon ROV systems for support of deepwater drilling and installation operations significantly increased in the past 10 years. Shell Offshore Inc.`s (SOI) confidence in this increased capability was an important factor in many of the design decisions which characterized the innovative system. Technology advancements, which depended on effective ROV intervention, were implemented with no significant difficulties. These advancements, in particular the flying leads and seabed position methods, are available to the industry for other deepwater subsea systems. In addition, several Popeye ROV interfaces have helped advance the subsea standardization initiative; e.g., hot stabs, torque-tool end effectors, and paint color.

  10. Open Textbooks for Instructional Designers

    OpenAIRE

    Ernst, David

    2015-01-01

    Join a fellow instructional designer, Dr. Dave Ernst, now Chief Information Officer of the College of Education and Human Development at University of Minnesota, in exploring possibilities for incorporating open educational resources in your instructional design philosophy and work. Learn how you can make a difference by supporting faculty exploring or interested in developing or redesigning their courses. Discussion topics include: Open and OER - what it is and what it isn't; how to attribut...

  11. Embodiment and Interface

    DEFF Research Database (Denmark)

    Gregersen, Andreas Lindegaard; Grodal, Torben Kragh

    2008-01-01

    The article discusses – based on neurological and phenomenological theory - how the human embodiment supports and constrains the interaction between players and video games. It analyses embodied interaction with the specific hardware/software configuration of the Nintendo Wii and Wii Tennis as well...... of the player as patient, i.e. being the object of another agent’s actions.  Keywords: Video games, embodiment, interface, agency, action, control, cognition  ...

  12. Noise at the Interface

    DEFF Research Database (Denmark)

    Prior, Andrew

    2011-01-01

    The notion of noise occupies a contested territory, in which it is framed as pollution and detritus even as it makes its opposite a possibility - noise is always defined in opposition to something else, even if this ‘other’ is not quite clear. This paper explores noise in the context of ‘the...... interface’ asking what its affordances as an idea may contribute to our understanding of interface. I draw historically on information theory in particular to initiate this exploration....

  13. SNE Industrial Fieldbus Interface

    Science.gov (United States)

    Lucena, Angel; Raines, Matthew; Oostdyk, Rebecca; Mata, Carlos

    2011-01-01

    Programmable logic controllers (PLCs) have very limited diagnostic and no prognostic capabilities, while current smart sensor designs do not have the capability to communicate over Fieldbus networks. The aim is to interface smart sensors with PLCs so that health and status information, such as failure mode identification and measurement tolerance, can be communicated via an industrial Fieldbus such as ControlNet. The SNE Industrial Fieldbus Interface (SIFI) is an embedded device that acts as a communication module in a networked smart sensor. The purpose is to enable a smart sensor to communicate health and status information to other devices, such as PLCs, via an industrial Fieldbus networking protocol. The SNE (Smart Network Element) is attached to a commercial off-the-shelf Any bus-S interface module through the SIFI. Numerous Anybus-S modules are available, each one designed to interface with a specific Fieldbus. Development of the SIFI focused on communications using the ControlNet protocol, but any of the Anybus-S modules can be used. The SIFI communicates with the Any-bus module via a data buffer and mailbox system on the Anybus module, and supplies power to the module. The Anybus module transmits and receives data on the Fieldbus using the proper protocol. The SIFI is intended to be connected to other existing SNE modules in order to monitor the health and status of a transducer. The SIFI can also monitor aspects of its own health using an onboard watchdog timer and voltage monitors. The SIFI also has the hardware to drive a touchscreen LCD (liquid crystal display) unit for manual configuration and status monitoring.

  14. Standard interface file handbook

    Energy Technology Data Exchange (ETDEWEB)

    Shapiro, A.; Huria, H.C. (Cincinnati Univ., OH (United States))

    1992-10-01

    This handbook documents many of the standard interface file formats that have been adopted by the US Department of Energy to facilitate communications between and portability of, various large reactor physics and radiation transport software packages. The emphasis is on those files needed for use of the VENTURE/PC diffusion-depletion code system. File structures, contents and some practical advice on use of the various files are provided.

  15. Metaphors for Interface Design

    Science.gov (United States)

    1987-04-01

    00 00 METAPHORS FOR INTERFACE DESIGN Edwin Hutchins April 1987 ICS Report 8703 COGNITIVE SCIENCE IZET INTTT FOR CONTV SCINC UNIVERSITY OF CALIFORNIA...systems. Schneiderman (1982, 1983) coined the term " direct manipulation" to refer to these sys- tems. The technology on which these s)stems are based has...Weitsma., IA press) a reidr nlavigtiom tralnlng yam and a ’ direct manmpulatlon statistical analysis tuftt (Owam 1966) Until ncmady. bewevr we have

  16. Interfaces of Propylene Carbonate

    OpenAIRE

    You, Xinli; Chaudhari, Mangesh I.; Pratt, Lawrence R.; Pesika, Noshir; Aritakula, Kalika M.; Rick, Steven W.

    2012-01-01

    Propylene carbonate (PC) wets graphite with a contact angle of 31 deg at ambient conditions. Molecular dynamics simulations agree with this contact angle after 40% reduction of the strength of graphite-C atom Lennard-Jones interactions with the solvent, relative to the models used initially. A simulated nano-scale PC droplet on graphite displays a pronounced layering tendency and an Aztex pyramid structure for the droplet. Extrapolation of the computed tensions of PC liquid-vapor interface es...

  17. Interface Microstructures in Concrete

    Directory of Open Access Journals (Sweden)

    Puertas, Francisca

    1991-03-01

    Full Text Available This paper constitutes a compilation as well as an interpretation of the present state of knowledge about the different microstructures developed in the interface areas of concrete, that is, the cement paste-aggregates, the cement paste-reinforcement, the cement paste-fiber, etc. The Chemical reactions taking place in interface areas, the development and morphology of such areas and their strength ^since interfaces are taken as the weakest points of concrete are the aspects dealt with in some detail in this work.

    El presente trabajo constituye un resumen y también una interpretación del estado actual del conocimiento respecto de las diferentes microestructuras que se desarrollan en las zonas interfaciales de los hormigones, es decir: pasta de cemento-áridos, pasta de cemento-armaduras, pasta de cemento-fibras, etc. Las reacciones químicas que tienen lugar en la zona interfacial, el desarrollo y morfología de dicha zona y su resistencia (las interfases se consideran como uno de los puntos débiles del hormigón son los aspectos que con cierto detalle se tratan en el trabajo.

  18. Intelligent Instructional Systems in Military Training.

    Science.gov (United States)

    Fletcher, J.D.; Zdybel, Frank

    Intelligent instructional systems can be distinguished from more conventional approaches by the automation of instructional interaction and choice of strategy. This approach promises to reduce the costs of instructional materials preparation and to increase the adaptability and individualization of the instruction delivered. Tutorial simulation…

  19. Instructional Technology Must Contribute to Productivity

    Science.gov (United States)

    Molenda, Michael

    2009-01-01

    Those involved in instructional technology in higher education are urged to view instructional technology as a means of improving academic productivity. Instructional technology has been used for over forty years to analyze instructional problems and design solutions that reduce costs and improve learning outcomes. The Pew Program in Course…

  20. Changing Student Teachers' Views of Comprehension Instruction ...

    African Journals Online (AJOL)

    Reasons seem to include a lack of proper teacher training in comprehension instruction, teachers remaining unconvinced about the value of strategy instruction, and concerns that strategy instruction is time consuming and difficult to learn and teach. This article reports on the effect of a reading comprehension instruction ...

  1. Professional Development: Identifying Effective Instructional Coaching Activities

    Science.gov (United States)

    Mannino, Gina

    2014-01-01

    The purpose of this study was to identify the instructional coaching activities most used by instructional coaches in southeast Texas school districts and to test if there was a relationship between the use of instructional coaching and perceived improvement in the instructional practices of teachers and student achievement. The participants for…

  2. Improving Instructional Assistant Effectiveness in Inclusive Settings

    Science.gov (United States)

    Weiner, Kimberly Beth

    2010-01-01

    As of 2007, 718,119 instructional assistants were employed in the United States (National Center for Education Statistics, 2009b). Of those instructional assistants, 373,466 were classified as full-time special education instructional assistants (Data Accountability Center, 2009a). As the employment of instructional assistants continues to grow,…

  3. Content, Process, and Product: Modeling Differentiated Instruction

    Science.gov (United States)

    Taylor, Barbara Kline

    2015-01-01

    Modeling differentiated instruction is one way to demonstrate how educators can incorporate instructional strategies to address students' needs, interests, and learning styles. This article discusses how secondary teacher candidates learn to focus on content--the "what" of instruction; process--the "how" of instruction;…

  4. Instructions for minipill users.

    Science.gov (United States)

    Reese, M; Hatcher, R A

    1985-01-01

    Guidelines are provided for women who use minipills. Minipills are low dose, progestin only oral contraceptives (OC), which are frequently prescribed for women who 1) experience estrogen related side effects if they take combined OCs; 2) are 35 years of age or older; 3) are 30 years of age or aver and smoke; 4) have a history of headaches, hypertension, or varicose veins; 5) desire immediate postpartum protection; or 6) are lactating. Minipills prevent pregnancy by inhibiting ovulation and implantation and by making the cervical mucus more impervious to sperm penetration. Minipills can be effective if they are used properly. Women who take minipills should be advised to carefully read and follow the instructions provided in the OC packet, initiate pill taking on the 1st day of menstrual bleeding, and take 1 pill every day without and breaks. A backup method should be used during the 1st month and subsequently, during each midcycle phase. If a woman misses 1 pill, she should immediately, upon remembering, take a pill, take her next day's pill at regular time, and use a backup method until menstruation reoccurs. If a woman misses 2 pills, she should immediately, upon remembering, take 2 pills, take 2 pills the following day, and use a backup method until menstruation begins. Women should be advised that many minipill users experience irregular menstural cycles, including amenorrhea and spotting between periods. If menstruation is delayed for 45 days, a pregnancy test is advisable. Women should be advised to immediately seek medical attention if they experience severe chest pain, shortness breath, severe headaches, vision problems, or severe leg pain. Minipill users should let their clinicians know if they experience and changes in mood or sexual drive. These problems can frequently be avoided by switching to another brand of minipills.

  5. Thesaurus-Enhanced Search Interfaces.

    Science.gov (United States)

    Shiri, Ali Asghar; Revie, Crawford; Chowdhury, Gobinda

    2002-01-01

    Discussion of user interfaces to information retrieval systems focuses on interfaces that incorporate thesauri as part of their searching and browsing facilities. Discusses research literature related to information searching behavior, information retrieval interface evaluation, search term selection, and query expansion; and compares thesaurus…

  6. The effect of a script and a structured interface in grounding discussions

    NARCIS (Netherlands)

    Schoonenboom, J.

    2008-01-01

    This study focuses on how to support students who have to work together in small groups, and who do not know each other, in performing grounding discussion. It compares two different implementations of a script, one using a structured interface, one using a textual instruction, on students’

  7. A Comparison of Parallelism in Interface Designs for Computer-Based Learning Environments

    Science.gov (United States)

    Min, Rik; Yu, Tao; Spenkelink, Gerd; Vos, Hans

    2004-01-01

    In this paper we discuss an experiment that was carried out with a prototype, designed in conformity with the concept of parallelism and the Parallel Instruction theory (the PI theory). We designed this prototype with five different interfaces, and ran an empirical study in which 18 participants completed an abstract task. The five basic designs…

  8. 78 FR 77074 - Accessibility of User Interfaces, and Video Programming Guides and Menus; Accessible Emergency...

    Science.gov (United States)

    2013-12-20

    ... COMMISSION 47 CFR Part 79 Accessibility of User Interfaces, and Video Programming Guides and Menus... authority for requiring MVPDs to ensure that video programming guides and menus that ] provide channel and... the instructions for submitting comments. Federal Communications Commission's Web site: http...

  9. Explicit Instruction Elements in Core Reading Programs

    OpenAIRE

    Child, Angela R.

    2012-01-01

    Classroom teachers are provided instructional recommendations for teaching reading from their adopted core reading programs (CRPs). Explicit instruction elements or what is also called instructional moves, including direct explanation, modeling, guided practice, independent practice, discussion, feedback, and monitoring, were examined within CRP reading lessons. This study sought to answer the question: What elements of explicit instruction or instructional moves are included in the five most...

  10. Productivity issues at organizational interfaces

    Science.gov (United States)

    Holland, A. W.

    1985-01-01

    The need for close interdependence between large numbers of diverse and specialized work groups makes the Space Program extremely vulnerable to loss of productivity at organizational interfaces. Trends within the program also suggest that the number and diversity of interfaces will grow in the near term. Continued maintenance of R&D excellence will require that interface performance issues be included in any future productivity improvement effort. The types and characteristics of organizational interfaces are briefly presented, followed by a review of factors which impact their productivity. Approaches to assessing and improving interface effectiveness are also discussed.

  11. Standardized instructions urged for OCs.

    Science.gov (United States)

    1992-09-01

    FDA has developed standardized, simplified instructions for all brands of combined estrogen and progestogen oral contraceptives (OCs) to help reduce unplanned pregnancies. FDA asked manufacturers in April to incorporate these changes into patient package inserts as soon as possible. Since current instructions vary significantly from brand to brand, problems can occur when women switch brands and compare instructions. If they become confused, women may either take the pills incorrectly or stop altogether, risking an unwanted pregnancy. In addition to reducing patients' confusion about correct use, the new recommended instructions reflect current research on the effective use of OCs. An important change concerns when women should start taking pills. The new instructions provide only 2 options (current instructions provide more): either start on day 1 of the next normal menstrual cycle ("Day 1 Start") or on the 1st Sunday after the next cycle begins ("Sunday Start"). Although the "Sunday Start" option is popular, the "Day 1 Start" has been shown to be more effective since back-up contraceptive methods are not required for the 1st week, as they are for the "Sunday Start." Other changes in the patient package insert simplify and clarify the instructions when different numbers of pills are missed. Any patient who is unsure about what to do when pills are missed is told to use a back-up method of birth control and to keep taking pills with hormones until she van consult with a health professional. The new labeling also advises women to consult a health professional regarding other methods of contraception if taking a daily pill is a problem. These new directions for patients are for combination pills and do not apply to progestin-only OCs. FDA is still developing new labeling for them. FDA's Fertility and Maternal Health Advisory Committee recommended on FEb. 8, 1991, that the agency ask manufacturers of OCs to make these changes in the patient package insert. full text

  12. 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

  13. Transport processes at fluidic interfaces

    CERN Document Server

    Reusken, Arnold

    2017-01-01

    There are several physico-chemical processes that determine the behavior of multiphase fluid systems – e.g., the fluid dynamics in the different phases and the dynamics of the interface(s), mass transport between the fluids, adsorption effects at the interface, and transport of surfactants on the interface – and result in heterogeneous interface properties. In general, these processes are strongly coupled and local properties of the interface play a crucial role. A thorough understanding of the behavior of such complex flow problems must be based on physically sound mathematical models, which especially account for the local processes at the interface. This book presents recent findings on the rigorous derivation and mathematical analysis of such models and on the development of numerical methods for direct numerical simulations. Validation results are based on specifically designed experiments using high-resolution experimental techniques. A special feature of this book is its focus on an interdisciplina...

  14. Brain-computer interfaces in the continuum of consciousness.

    Science.gov (United States)

    Kübler, Andrea; Kotchoubey, Boris

    2007-12-01

    To summarize recent developments and look at important future aspects of brain-computer interfaces. Recent brain-computer interface studies are largely targeted at helping severely or even completely paralysed patients. The former are only able to communicate yes or no via a single muscle twitch, and the latter are totally nonresponsive. Such patients can control brain-computer interfaces and use them to select letters, words or items on a computer screen, for neuroprosthesis control or for surfing the Internet. This condition of motor paralysis, in which cognition and consciousness appear to be unaffected, is traditionally opposed to nonresponsiveness due to disorders of consciousness. Although these groups of patients may appear to be very alike, numerous transition states between them are demonstrated by recent studies. All nonresponsive patients can be regarded on a continuum of consciousness which may vary even within short time periods. As overt behaviour is lacking, cognitive functions in such patients can only be investigated using neurophysiological methods. We suggest that brain-computer interfaces may provide a new tool to investigate cognition in disorders of consciousness, and propose a hierarchical procedure entailing passive stimulation, active instructions, volitional paradigms, and brain-computer interface operation.

  15. Matched Interface and Boundary Method for Elasticity Interface Problems

    Science.gov (United States)

    Wang, Bao; Xia, Kelin; Wei, Guo-Wei

    2015-01-01

    Elasticity theory is an important component of continuum mechanics and has had widely spread applications in science and engineering. Material interfaces are ubiquity in nature and man-made devices, and often give rise to discontinuous coefficients in the governing elasticity equations. In this work, the matched interface and boundary (MIB) method is developed to address elasticity interface problems. Linear elasticity theory for both isotropic homogeneous and inhomogeneous media is employed. In our approach, Lamé’s parameters can have jumps across the interface and are allowed to be position dependent in modeling isotropic inhomogeneous material. Both strong discontinuity, i.e., discontinuous solution, and weak discontinuity, namely, discontinuous derivatives of the solution, are considered in the present study. In the proposed method, fictitious values are utilized so that the standard central finite different schemes can be employed regardless of the interface. Interface jump conditions are enforced on the interface, which in turn, accurately determines fictitious values. We design new MIB schemes to account for complex interface geometries. In particular, the cross derivatives in the elasticity equations are difficult to handle for complex interface geometries. We propose secondary fictitious values and construct geometry based interpolation schemes to overcome this difficulty. Numerous analytical examples are used to validate the accuracy, convergence and robustness of the present MIB method for elasticity interface problems with both small and large curvatures, strong and weak discontinuities, and constant and variable coefficients. Numerical tests indicate second order accuracy in both L∞ and L2 norms. PMID:25914439

  16. Films of bacteria at interfaces.

    Science.gov (United States)

    Vaccari, Liana; Molaei, Mehdi; Niepa, Tagbo H R; Lee, Daeyeon; Leheny, Robert L; Stebe, Kathleen J

    2017-09-01

    Bacteria are often discussed as active colloids, self-propelled organisms whose collective motion can be studied in the context of non-equilibrium statistical mechanics. In such studies, the behavior of bacteria confined to interfaces or in the proximity of an interface plays an important role. For instance, many studies have probed collective behavior of bacteria in quasi two-dimensional systems such as soap films. Since fluid interfaces can adsorb surfactants and other materials, the stress and velocity boundary conditions at interfaces can alter bacteria motion; hydrodynamic studies of interfaces with differing boundary conditions are reviewed. Also, bacteria in bulk can become trapped at or near fluid interfaces, where they colonize and form structures comprising secretions like exopolysaccharides, surfactants, living and dead bacteria, thereby creating Films of Bacteria at Interfaces (FBI). The formation of FBI is discussed at air-water, oil-water, and water-water interfaces, with an emphasis on film mechanics, and with some allusion to genetic functions guiding bacteria to restructure fluid interfaces. At air-water interfaces, bacteria form pellicles or interfacial biofilms. Studies are reviewed that reveal that pellicle material properties differ for different strains of bacteria, and that pellicle physicochemistry can act as a feedback mechanism to regulate film formation. At oil-water interfaces, a range of FBI form, depending on bacteria strain. Some bacteria-laden interfaces age from an initial active film, with dynamics dominated by motile bacteria, through viscoelastic states, to form an elastic film. Others remain active with no evidence of elastic film formation even at significant interface ages. Finally, bacteria can adhere to and colonize ultra-low surface tension interfaces such as aqueous-aqueous systems common in food industries. Relevant literature is reviewed, and areas of interest for potential application are discussed, ranging from health

  17. SOFC interface studies

    DEFF Research Database (Denmark)

    Jacobsen, Torben; Bay, Lasse; West, Keld

    performance and inductive hysteresis phenomena often observed in SOFC kinetic studies (\\ref{TJ01}). Fig.\\,\\ref{cath_laser} shows the YSZ surface developed below a Pt point electrode polarised at -0.10\\, V at $1000^\\circ$C for a period of 85 days. The structural as well as the compositional changes...... the anode (\\ref{Tine}) as well as the very high capacity contribution at low freqencies \\ref{Lasse97}. Considering the rough surface structure formed on the YSZ-cathode interface it is most likely that the reaction zone is not confined to the perimeter of the contact area. A significant contribution may...

  18. Interfacing with the Night

    OpenAIRE

    McLean, Alex; Parkinson, Adam

    2014-01-01

    In  this  paper,  the  authors  consider  the  interfaces  between academia and dance music. Dance music and club culture are, we argue, important to computer music and the live performance of electronic music, but there are many different difficulties encountered when trying to present electronic dance music within academic contexts. The authors draw upon their experiences as promoters, performers, researchers and audience members to discuss these difficulties and how and why we might negoti...

  19. Bubble and drop interfaces

    CERN Document Server

    Miller

    2011-01-01

    The book aims at describing the most important experimental methods for characterizing liquid interfaces, such as drop profile analysis, bubble pressure and drop volume tensiometry, capillary pressure technique, and oscillating drops and bubbles. Besides the details of experimental set ups, also the underlying theoretical basis is presented in detail. In addition, a number of applications based on drops and bubbles is discussed, such as rising bubbles and the very complex process of flotation. Also wetting, characterized by the dynamics of advancing contact angles is discussed critically. Spec

  20. Space as interface

    DEFF Research Database (Denmark)

    Lykke-Olesen, Andreas

    2006-01-01

    , social and cultural aspects towards space. As these dimensions are tied to the humans who experience the space, the designer can not directly influence and form the creation of place. This division puts attention to two gaps necessary to bridge. The first is the gap between physical and digital space...... multiple projects spanning over fields such as tangible user interfaces, augmented reality, and mobile computing, a conceptual framework characterizing camera-based mixed interaction spaces is developed. To show the applicability of the framework, it is deployed on one of the presented cases and discussed...

  1. Adhesion at metal interfaces

    Science.gov (United States)

    Banerjea, Amitava; Ferrante, John; Smith, John R.

    1991-01-01

    A basic adhesion process is defined, the theory of the properties influencing metallic adhesion is outlined, and theoretical approaches to the interface problem are presented, with emphasis on first-principle calculations as well as jellium-model calculations. The computation of the energies of adhesion as a function of the interfacial separation is performed; fully three-dimensional calculations are presented, and universality in the shapes of the binding energy curves is considered. An embedded-atom method and equivalent-crystal theory are covered in the framework of issues involved in practical adhesion.

  2. Craft Physics Interface

    OpenAIRE

    Hansson, Henrik

    2007-01-01

    This is a masters thesis (20p) in computer science at the University of Linköping. This thesis will give an introduction to what a physics engine is and what it consist of. It will put some engines under the magnifying glass and test them in a couple of runtime tests. Two cutting edge commercial physics engines have been examined, trying to predict the future of physics engines. From the research and test results, an interface for physics engine independency has been implemented for a company...

  3. 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...

  4. Instructional video for teaching venepuncture.

    Science.gov (United States)

    Pan, Michael; Harcharik, Sara; Moskalenko, Marina; Luber, Adam; Bernardo, Sebastian; Levitt, Jacob

    2014-10-01

    Safe venepuncture technique is a critical skill for health care professionals, to avoid accidental occupational injury. This study investigates whether watching an instructional video improves medical students' ability to perform venepuncture safely. This was a randomised, controlled, assessor-blinded trial that evaluated the utility of an instructional video, with the primary outcome of the ability to perform venepuncture safely. Forty-two second-year medical students were recruited and randomised to receive either video instruction (group A, n = 20) or no intervention (group B, n = 22). Prior to the study, all students attended an instructor-led workshop on venepuncture. During the study, students were paired and instructed to perform venepuncture on a partner. Performance was assessed using a points-based checklist. Pre- and post-study surveys were conducted to assess confidence with technique. The mean total checklist score was higher in group A than in group B, with values of 14.15 and 9.18, respectively (p video performed venepuncture more effectively and reported greater confidence with the technique. Medical students can benefit from having access to an instructional video on venepuncture as an adjunct to the standard curriculum. Safe venepuncture technique is a critical skill for health care professionals. © 2014 John Wiley & Sons Ltd.

  5. Multiple network interface core apparatus and method

    Science.gov (United States)

    Underwood, Keith D [Albuquerque, NM; Hemmert, Karl Scott [Albuquerque, NM

    2011-04-26

    A network interface controller and network interface control method comprising providing a single integrated circuit as a network interface controller and employing a plurality of network interface cores on the single integrated circuit.

  6. Evaluation of the Next-Gen Exercise Software Interface in the NEEMO Analog

    Science.gov (United States)

    Hanson, Andrea; Kalogera, Kent; Sandor, Aniko; Hardy, Marc; Frank, Andrew; English, Kirk; Williams, Thomas; Perera, Jeevan; Amonette, William

    2017-01-01

    NSBRI (National Space Biomedical Research Institute) funded research grant to develop the 'NextGen' exercise software for the NEEMO (NASA Extreme Environment Mission Operations) analog. Develop a software architecture to integrate instructional, motivational and socialization techniques into a common portal to enhance exercise countermeasures in remote environments. Increase user efficiency and satisfaction, and institute commonality across multiple exercise systems. Utilized GUI (Graphical User Interface) design principals focused on intuitive ease of use to minimize training time and realize early user efficiency. Project requirement to test the software in an analog environment. Top Level Project Aims: 1) Improve the usability of crew interface software to exercise CMS (Crew Management System) through common app-like interfaces. 2) Introduce virtual instructional motion training. 3) Use virtual environment to provide remote socialization with family and friends, improve exercise technique, adherence, motivation and ultimately performance outcomes.

  7. WAVES VHDL interface

    Science.gov (United States)

    Hanna, James P.

    1994-06-01

    The Waveform and Vector Exchange Specification (WAVES) is the Industry standard representation for digital stimulus and response for both the design and test communities. The VHSIC Hardware Description Language (VHDL) is the Industry standard language for the design, modeling, and simulation of digital electronics. Together VHDL and WAVES provide powerful support for top-down design and test methodologies and concurrent engineering practices. Although the syntax of WAVES is a subset of VHDL, no special support for using WAVES in a VHDL environment is defined within the language. This report will introduce and describe a VHDL package that was developed at Rome Laboratory to provide a software interface to support the use of WAVES in a VHDL environment. This VHDL package is referred to as the WAVES VHDL interface and has been proposed as a standard practice for a top-down design and test methodology using WAVES and VHDL. This report is not intended to provide a tutorial on VHDL or WAVES. It is assumed that the reader has an adequate understanding of the VHDL language and some modeling techniques. Further, it is assumed that the reader has an understanding of the WAVES language and can follow a simple Level 1 dataset description.

  8. Mercury Shopping Cart Interface

    Science.gov (United States)

    Pfister, Robin; McMahon, Joe

    2006-01-01

    Mercury Shopping Cart Interface (MSCI) is a reusable component of the Power User Interface 5.0 (PUI) program described in another article. MSCI is a means of encapsulating the logic and information needed to describe an orderable item consistent with Mercury Shopping Cart service protocol. Designed to be used with Web-browser software, MSCI generates Hypertext Markup Language (HTML) pages on which ordering information can be entered. MSCI comprises two types of Practical Extraction and Report Language (PERL) modules: template modules and shopping-cart logic modules. Template modules generate HTML pages for entering the required ordering details and enable submission of the order via a Hypertext Transfer Protocol (HTTP) post. Shopping cart modules encapsulate the logic and data needed to describe an individual orderable item to the Mercury Shopping Cart service. These modules evaluate information entered by the user to determine whether it is sufficient for the Shopping Cart service to process the order. Once an order has been passed from MSCI to a deployed Mercury Shopping Cart server, there is no further interaction with the user.

  9. Interface Biology of Implants

    Science.gov (United States)

    Nebe, Barbara

    2009-01-01

    Implants are widely used in various clinical disciplines to replace or stabilize organs. The challenge for the future is to apply implant materials to specifically control the biology of the surrounding tissue for repair and regeneration. This field of research is highly interdisciplinary and combines scientists from technical and life sciences disciplines. To successfully apply materials for regenerative processes in the body, the understanding of the mechanisms at the interface between cells or tissues and the artificial material is of critical importance. The research focuses on stem cells, design of material surfaces, and mechanisms of cell adhesion. For the third time around 200 scientists met in Rostock, Germany for the international symposium “Interface Biology of Implants.” The aim of the symposium is to promote the interdisciplinary dialogue between the scientists from the different disciplines to develop smart implants for medical use. In addition, researchers from basic sciences, notably cell biology presented new findings concerning mechanisms of cell adhesion to stimulate research in the applied field of implant technology. PMID:19690468

  10. Mysteries at Ice Interfaces

    Science.gov (United States)

    Fain, Samuel C., Jr.

    1996-03-01

    Michael Faraday noted that ``two pieces of thawing ice, if put together, adhere and become one...the effect will take place in air, or in water, or in vacuo." Why? He proposed that ``a particle of water, which could retain the liquid state whilst touching ice only on one side, could not retain the liquid state if it were touched by ice on both sides."footnote M. Faraday, Proc. Roy. Soc. London 10, 440 (1860) The existence of special properties at interfaces of ice is generally agreed and has important environmental consequences.(J. G. Dash, H. Fu, and J. S. Wettlaufer, Rep. Prog. Phys. 58), 115 (1995) Why do different experiments infer different properties for this layer? Impurities and electric fields at the interfaces may be responsible for some of the variations in experimental results.footnote V. F. Petrenko, U. S. Army Cold Regions Research and Engineering Laboratory Report 94-22 (1994) Some background on the physical properties of ice will be discussed, including recent force microscopy measurements done at the University of Washington.footnote C.R. Slaughterbeck, E.W. Kukes, B. Pittenger, D.J. Cook, P.C. Williams, V.L. Eden, S.C. Fain, Jr., J. Vac. Sci. Technol. (in press) Supported by NSF Grant DMR-91-19701.

  11. Data communications for a collective operation in a parallel active messaging interface of a parallel computer

    Science.gov (United States)

    Faraj, Daniel A

    2013-07-16

    Algorithm selection for data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI, including associating in the PAMI data communications algorithms and bit masks; receiving in an origin endpoint of the PAMI a collective instruction, the instruction specifying transmission of a data communications message from the origin endpoint to a target endpoint; constructing a bit mask for the received collective instruction; selecting, from among the associated algorithms and bit masks, a data communications algorithm in dependence upon the constructed bit mask; and executing the collective instruction, transmitting, according to the selected data communications algorithm from the origin endpoint to the target endpoint, the data communications message.

  12. Best practices in writing instruction

    CERN Document Server

    Fitzgerald, Jill; MacArthur, Charles A

    2014-01-01

    An indispensable teacher resource and course text, this book presents evidence-based practices for helping all K-12 students develop their skills as writers. Every chapter draws clear connections to the Common Core State Standards (CCSS). Leading authorities describe how to teach the skills and strategies that students need to plan, draft, evaluate, and revise multiple types of texts. Also addressed are ways for teachers to integrate technology into the writing program, use assessment to inform instruction, teach writing in the content areas, and tailor instruction for English language learner

  13. The Role of Prosody and Explicit Instruction in Processing Instruction

    Science.gov (United States)

    Henry, Nick; Jackson, Carrie N.; Dimidio, Jack

    2017-01-01

    This study investigates the role of prosodic cues and explicit information (EI) in the acquisition of German accusative case markers. We compared 4 groups of 3rd-semester learners (low intermediate level) who completed 1 of 4 Processing Instruction (PI) treatments that manipulated the presence or absence of EI and focused prosody. The results…

  14. Using Videodiscs in Instruction: Realizing Their Potential through Instructional Design.

    Science.gov (United States)

    Reigeluth, Charles M.; Garfield, Joanne M.

    1984-01-01

    Examines the state-of-the-art of intelligent videodisc systems and of the aspects of instructional theory that have implications for design of hardware, software, and courseware for such systems. Some problems inhibiting the introduction of videodisc systems into education are discussed along with solutions to these inhibiting factors. (MBR)

  15. TRUPACT-II Operating and Maintenance Instructions

    Energy Technology Data Exchange (ETDEWEB)

    Westinghouse Electric Corporation, Waste Isolation Division

    1999-12-31

    ) Utilizing these instructions as is, or (2) Attaching a site-specific cover page/letter to this document stating that these are the instructions to be used at their location, or (3) Sites may prepare their own document using the steps in this document word-for-word, in-sequence, including Notes and Cautions. Site specific information may be included as deemed necessary. Submit the document to WID National TRU Programs for approval. Any revision made subsequent to WID TRU Program's approval shall be reviewed and approved by WID TRU Programs. A copy of the approval letter from WID National TRU Programs should be available for audit purposes. Users shall develop site-specific procedures addressing leak testing, preoperational activities, quality assurance, hoisting and rigging, and radiation health physics to be used in conjunction with the instructions contained in this document. Users desiring to recommend changes to this document may submit their recommendations to the WID National TRU Programs for evaluation. If approved, the change(s) will be incorporated into this document for use by all TRUPACT-II users. User sites will be audited to this document to ensure compliance within one year from the effective date of this revision. This document discusses operating instructions, required inspections and maintenance for the following: TRUPACT-II packaging, and Miscellaneous packaging, special tools, and equipment. Packaging and payload handling equipment and transport trailers have been specifically designed for use with the TRUPACT-II Packaging. This document discusses the required instructions for use of the following equipment in conjunction with the TRUPACT-II Packaging: TRUPACT-II Mobile Loading Unit (MLU), Adjustable Center-of-Gravity Lift Fixture (ACGLF), and TRUPACT-II Transport Trailer. Attachment E contains the various TRUPACT-II packaging interface control drawings, leak-test and vent-port tool drawings, ACGLF drawings, and tie-down drawings that identify the

  16. Oscars and Interfaces

    Directory of Open Access Journals (Sweden)

    Antony Unwin

    2012-06-01

    Full Text Available Graphical user interfaces (GUIs are gradually becoming more powerful and more accepted. They are the standard way of interacting with the web and play an increasing role in many software applications. Nevertheless, they have not been generally adopted, and critics point to particular weaknesses and disadvantages. Many of these are due more to flaws in design and implementation than to the basic concepts of GUIs. More attention could be paid to what users want to do and how a GUI might be developed to support these goals. Using a dataset about Oscar nominees and winners, this paper considers what analyses statisticians might carry out and what kind of GUI would be appropriate for these tasks. (It also offers some insights into the Oscars dataset.

  17. Nuclear data interface retrospective

    Energy Technology Data Exchange (ETDEWEB)

    Gray, Mark G [Los Alamos National Laboratory

    2008-01-01

    The Nuclear Data Interface (NDI) code library and data formats are the standards for multigroup nuclear data at Los Alamos National Laboratory. NDI's analysis, design, implementation, testing, integration, and maintenance required a ten person-year and ongoing effort by the Nuclear Data Team. Their efforts provide a unique, contemporary experience in producing a standard component library. In reflection upon that experience at NDI's decennial, we have identified several factors critical to NDI's success: it addressed real problems with appropriate simplicity, it fully supported all users, it added extra value through the code to the raw nuclear data, and its team went the distance from analysis through maintenance. In this report we review these critical success factors and discuss their implications for future standardization projects.

  18. Human-computer interface

    Science.gov (United States)

    Anderson, Thomas G.

    2004-12-21

    The present invention provides a method of human-computer interfacing. Force feedback allows intuitive navigation and control near a boundary between regions in a computer-represented space. For example, the method allows a user to interact with a virtual craft, then push through the windshield of the craft to interact with the virtual world surrounding the craft. As another example, the method allows a user to feel transitions between different control domains of a computer representation of a space. The method can provide for force feedback that increases as a user's locus of interaction moves near a boundary, then perceptibly changes (e.g., abruptly drops or changes direction) when the boundary is traversed.

  19. Porphyrins at interfaces

    Science.gov (United States)

    Auwärter, Willi; Écija, David; Klappenberger, Florian; Barth, Johannes V.

    2015-02-01

    Porphyrins and other tetrapyrrole macrocycles possess an impressive variety of functional properties that have been exploited in natural and artificial systems. Different metal centres incorporated within the tetradentate ligand are key for achieving and regulating vital processes, including reversible axial ligation of adducts, electron transfer, light-harvesting and catalytic transformations. Tailored substituents optimize their performance, dictating their arrangement in specific environments and mediating the assembly of molecular nanoarchitectures. Here we review the current understanding of these species at well-defined interfaces, disclosing exquisite insights into their structural and chemical properties, and also discussing methods by which to manipulate their intramolecular and organizational features. The distinct characteristics arising from the interfacial confinement offer intriguing prospects for molecular science and advanced materials. We assess the role of surface interactions with respect to electronic and physicochemical characteristics, and describe in situ metallation pathways, molecular magnetism, rotation and switching. The engineering of nanostructures, organized layers, interfacial hybrid and bio-inspired systems is also addressed.

  20. Interfacing Ada and other languages

    Science.gov (United States)

    Baffes, Paul; West, Brian

    1986-01-01

    Interfacing two separately developed compilers is a complex task. The complexity arises because few design standards exist for compiler development. This, coupled with the many complicated design decisions inherent in compiler construction, usually guarantees noncompatibility. The interface subroutine which would link the two different run time environments would resolve as many of the dissimilarities as possible. The differences that could not be resolved would be responsible for the restrictions placed on the interface. Albeit restrictions would exist, the resulting interface may be well worthwhile.