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Neural prosthetics

He Cui
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...
2016: Frontiers in Integrative Neuroscience
E Castaldi, G M Cicchini, L Cinelli, L Biagi, S Rizzo, M C Morrone
Retinal prosthesis technologies require that the visual system downstream of the retinal circuitry be capable of transmitting and elaborating visual signals. We studied the capability of plastic remodeling in late blind subjects implanted with the Argus II Retinal Prosthesis with psychophysics and functional MRI (fMRI). After surgery, six out of seven retinitis pigmentosa (RP) blind subjects were able to detect high-contrast stimuli using the prosthetic implant. However, direction discrimination to contrast modulated stimuli remained at chance level in all of them...
October 2016: PLoS Biology
Cyril G Eleftheriou, Jonas B Zimmermann, Henrik D Kjeldsen, Moshe David-Pur, Yael Hanein, Evelyne Sernagor
The choice of electrode material is of paramount importance in neural prosthetic devices. Electrodes must be biocompatible yet able to sustain repetitive current injections in a highly corrosive environment. We explored the suitability of carbon nanotube (CNT) electrodes to stimulate retinal ganglion cells (RGCs) in a mouse model of outer retinal degeneration. We investigated morphological changes at the bio-hybrid interface and changes in RGC responses to electrical stimulation following prolonged in vitro coupling to CNT electrodes...
January 2017: Biomaterials
Yi Su, Sudhamayee Routhu, Kee S Moon, Sung Q Lee, WooSub Youm, Yusuf Ozturk
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...
2016: Sensors
Zhaohui Wu, Nenggan Zheng, Shaowu Zhang, Xiaoxiang Zheng, Liqiang Gao, Lijuan Su
The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity...
2016: Scientific Reports
H A Agashe, A Y Paek, J L Contreras-Vidal
Upper limb amputation results in a severe reduction in the quality of life of affected individuals due to their inability to easily perform activities of daily living. Brain-machine interfaces (BMIs) that translate grasping intent from the brain's neural activity into prosthetic control may increase the level of natural control currently available in myoelectric prostheses. Current BMI techniques demonstrate accurate arm position and single degree-of-freedom grasp control but are invasive and require daily recalibration...
2016: Progress in Brain Research
Cosima Prahm, Korbinian Eckstein, Max Ortiz-Catalan, Georg Dorffner, Eugenijus Kaniusas, Oskar C Aszmann
BACKGROUND: Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models...
2016: BMC Research Notes
Ujwal Chaudhary, Niels Birbaumer, Ander Ramos-Murguialday
Brain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function...
September 2016: Nature Reviews. Neurology
Milos Radivojevic, David Jäckel, Michael Altermatt, Jan Müller, Vijay Viswam, Andreas Hierlemann, Douglas J Bakkum
A detailed, high-spatiotemporal-resolution characterization of neuronal responses to local electrical fields and the capability of precise extracellular microstimulation of selected neurons are pivotal for studying and manipulating neuronal activity and circuits in networks and for developing neural prosthetics. Here, we studied cultured neocortical neurons by using high-density microelectrode arrays and optical imaging, complemented by the patch-clamp technique, and with the aim to correlate morphological and electrical features of neuronal compartments with their responsiveness to extracellular stimulation...
2016: Scientific Reports
David A Moses, Nima Mesgarani, Matthew K Leonard, Edward F Chang
OBJECTIVE: The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography...
October 2016: Journal of Neural Engineering
Sindhu Anand, Swathy Sampath Kumar, Jit Muthuswamy
Emerging neural prosthetics require precise positional tuning and stable interfaces with single neurons for optimal function over a lifetime. In this study, we report an autonomous control to precisely navigate microscale electrodes in soft, viscoelastic brain tissue without visual feedback. The autonomous control optimizes signal-to-noise ratio (SNR) of single neuronal recordings in viscoelastic brain tissue while maintaining quasi-static mechanical stress conditions to improve stability of the implant-tissue interface...
August 2016: Biomedical Microdevices
Heather L Benz, Eugene F Civillico
Safe and effective neuroprosthetic systems are of great interest to both DARPA and CDRH, due to their innovative nature and their potential to aid severely disabled populations.By expanding technological boundaries in human-device interfaces, these devices introduce new potential benefits and risks. Therefore patient input, which is increasingly important in weighing benefits and risks, is particularly relevant for this class of devices. FDA has been a significant contributor to an ongoing stakeholder conversation about the inclusion of the patient voice, working collaboratively to create a new framework for a patient-centered approach to medical device development...
July 22, 2016: Experimental Neurology
Gretchen L Knaack, Daniel G McHail, German Borda, Beomseo Koo, Nathalia Peixoto, Stuart F Cogan, Theodore C Dumas, Joseph J Pancrazio
Implantable microelectrode arrays (MEAs) offer clinical promise for prosthetic devices by enabling restoration of communication and control of artificial limbs. While proof-of-concept recordings from MEAs have been promising, work in animal models demonstrates that the obtained signals degrade over time. Both material robustness and tissue response are acknowledged to have a role in device lifetime. Amorphous Silicon carbide (a-SiC), a robust material that is corrosion resistant, has emerged as an alternative encapsulation layer for implantable devices...
2016: Frontiers in Neuroscience
Karen E Schroeder, Cynthia A Chestek
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...
2016: Frontiers in Neuroscience
Ignacio Delgado-Martínez, Jordi Badia, Arán Pascual-Font, Alfonso Rodríguez-Baeza, Xavier Navarro
One of the most sought-after applications of neuroengineering is the communication between the arm and an artificial prosthetic device for the replacement of an amputated hand or the treatment of peripheral nerve injuries. For that, an electrode is placed around or inside the median nerve to serve as interface for recording and stimulation of nerve signals coming from the fascicles that innervate the muscles responsible for hand movements. Due to the lack of a standard procedure, the electrode implantation by the surgeon is strongly based on intuition, which may result in poor performance of the neuroprosthesis because of the suboptimal location of the neural interface...
2016: Frontiers in Neuroscience
Geoffrey W Lee, Fabio Zambetta, Xiaodong Li, Antonio G Paolini
OBJECTIVE: In this paper we propose a novel application of reinforcement learning to the area of auditory neural stimulation. We aim to develop a simulation environment which is based off real neurological responses to auditory and electrical stimulation in the cochlear nucleus (CN) and inferior colliculus (IC) of an animal model. Using this simulator we implement closed loop reinforcement learning algorithms to determine which methods are most effective at learning effective acoustic neural stimulation strategies...
August 2016: Journal of Neural Engineering
Stephanie Martin, José Del R Millán, Robert T Knight, Brian N Pasley
Decoding speech from intracranial recordings serves two main purposes: understanding the neural correlates of speech processing and decoding speech features for targeting speech neuroprosthetic devices. Intracranial recordings have high spatial and temporal resolution, and thus offer a unique opportunity to investigate and decode the electrophysiological dynamics underlying speech processing. In this review article, we describe current approaches to decoding different features of speech perception and production - such as spectrotemporal, phonetic, phonotactic, semantic, and articulatory components - using intracranial recordings...
July 1, 2016: Brain and Language
Tobias Rader, Julia Döge, Youssef Adel, Tobias Weissgerber, Uwe Baumann
In normal hearing, the pitch of an acoustic tone can theoretically be encoded by either the place of stimulation in the cochlea or the corresponding rate of vibration. Thus spectral attributes and temporal fine structure of an acoustic signal are naturally correlated. Cochlear implants (CIs), neural prosthetic devices that restore hearing in the profoundly hearing impaired, currently disregard this mechanism; electrical stimulation is provided at fixed electrode positions with default place independent stimulation rate assignments...
September 2016: Hearing Research
Gabriel W Vattendahl Vidal, Mathew L Rynes, Zachary Kelliher, Shikha Jain Goodwin
The brain-machine interface (BMI) used in neural prosthetics involves recording signals from neuron populations, decoding those signals using mathematical modeling algorithms, and translating the intended action into physical limb movement. Recently, somatosensory feedback has become the focus of many research groups given its ability in increased neural control by the patient and to provide a more natural sensation for the prosthetics. This process involves recording data from force sensitive locations on the prosthetics and encoding these signals to be sent to the brain in the form of electrical stimulation...
2016: Scientifica
P Geethanjali
In this paper, a low-cost mechatronics platform for the design and development of robotic hands as well as a surface electromyogram (EMG) pattern recognition system is proposed. This paper also explores various EMG classification techniques using a low-cost electronics system in prosthetic hand applications. The proposed platform involves the development of a four channel EMG signal acquisition system; pattern recognition of acquired EMG signals; and development of a digital controller for a robotic hand. Four-channel surface EMG signals, acquired from ten healthy subjects for six different movements of the hand, were used to analyse pattern recognition in prosthetic hand control...
September 2016: Australasian Physical & Engineering Sciences in Medicine
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