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Brain machine interfaces

Sergey D Stavisky, Jonathan C Kao, Stephen I Ryu, Krishna V Shenoy
: Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and peri-movement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared...
January 13, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Toshihiro Kawase, Takeshi Sakurada, Yasuharu Koike, Kenji Kansaku
OBJECTIVE: Brain-machine interface (BMI) technologies have succeeded in controlling robotic exoskeletons, enabling some paralyzed people to control their own arms and hands. We have developed an exoskeleton asynchronously controlled by EEG signals. In this study, to enable real-time control of the exoskeleton for paresis, we developed a hybrid system with EEG and EMG signals, and the EMG signals were used to estimate its joint angles. APPROACH: Eleven able-bodied subjects and two patients with upper cervical spinal cord injuries (SCIs) performed hand and arm movements, and the angles of the metacarpophalangeal (MP) joint of the index finger, wrist, and elbow were estimated from EMG signals using a formula that we derived to calculate joint angles from EMG signals, based on a musculoskeletal model...
January 9, 2017: Journal of Neural Engineering
Shuangming Yang, Bin Deng, Jiang Wang, Huiyan Li, Chen Liu, Chris Fietkiewicz, Kenneth A Loparo
Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization...
January 9, 2017: Scientific Reports
Maryam M Shanechi, Amy L Orsborn, Helene G Moorman, Suraj Gowda, Siddharth Dangi, Jose M Carmena
Brain-machine interfaces (BMI) create novel sensorimotor pathways for action. Much as the sensorimotor apparatus shapes natural motor control, the BMI pathway characteristics may also influence neuroprosthetic control. Here, we explore the influence of control and feedback rates, where control rate indicates how often motor commands are sent from the brain to the prosthetic, and feedback rate indicates how often visual feedback of the prosthetic is provided to the subject. We developed a new BMI that allows arbitrarily fast control and feedback rates, and used it to dissociate the effects of each rate in two monkeys...
January 6, 2017: Nature Communications
Kai Keng Ang, Cuntai Guan
Advances in Brain-Computer Interface (BCI) technology have facilitated the detection of Motor Imagery (MI) from electroencephalography (EEG). First, we present three strategies of using BCI to detect MI from EEG: operant conditioning that employed a fixed model, machine learning that employed a subject-specific model computed from calibration, and adaptive strategy that continuously compute the subjectspecific model. Second, we review prevailing works that employed the operant conditioning and machine learning strategies...
December 30, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Xilin Liu, Milin Zhang, Andrew G Richardson, Timothy H Lucas, Jan Van der Spiegel
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...
December 16, 2016: IEEE Transactions on Biomedical Circuits and Systems
Mostafa Rahimi Azghadi, Bernabe Linares-Barranco, Derek Abbott, Philip H W Leong
Although data processing technology continues to advance at an astonishing rate, computers with brain-like processing capabilities still elude us. It is envisioned that such computers may be achieved by the fusion of neuroscience and nano-electronics to realize a brain-inspired platform. This paper proposes a high-performance nano-scale Complementary Metal Oxide Semiconductor (CMOS)-memristive circuit, which mimics a number of essential learning properties of biological synapses. The proposed synaptic circuit that is composed of memristors and CMOS transistors, alters its memristance in response to timing differences among its pre- and post-synaptic action potentials, giving rise to a family of Spike Timing Dependent Plasticity (STDP)...
December 22, 2016: IEEE Transactions on Biomedical Circuits and Systems
Fabio Boi, Timoleon Moraitis, Vito De Feo, Francesco Diotalevi, Chiara Bartolozzi, Giacomo Indiveri, Alessandro Vato
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...
2016: Frontiers in Neuroscience
Ranganatha Sitaram, Tomas Ros, Luke Stoeckel, Sven Haller, Frank Scharnowski, Jarrod Lewis-Peacock, Nikolaus Weiskopf, Maria Laura Blefari, Mohit Rana, Ethan Oblak, Niels Birbaumer, James Sulzer
Neurofeedback is a psychophysiological procedure in which online feedback of neural activation is provided to the participant for the purpose of self-regulation. Learning control over specific neural substrates has been shown to change specific behaviours. As a progenitor of brain-machine interfaces, neurofeedback has provided a novel way to investigate brain function and neuroplasticity. In this Review, we examine the mechanisms underlying neurofeedback, which have started to be uncovered. We also discuss how neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints...
December 22, 2016: Nature Reviews. Neuroscience
Giovanni Mirabella, Mikhail A Lebedev
It has been long known that neural activity, recorded with electrophysiological methods, contains rich information about a subject's motor intentions, sensory experiences, allocation of attention, action planning, and even abstract thoughts. All these functions have been the subject of neurophysiological investigations, with the goal of understanding how neuronal activity represents behavioral parameters, sensory inputs, and cognitive functions. The field of brain-machine interfaces (BMIs) strives for a somewhat different goal: it endeavors to extract information from neural modulations to create a communication link between the brain and external devices...
December 21, 2016: Journal of Neurophysiology
Yu-Yi Chien, Fang-Cheng Lin, John Zao, Ching-Chi Chou, Yi-Pai Huang, Heng-Yuan Kuo, Yijun Wang, Tzyy-Ping Jung, Han-Ping D Shieh
OBJECTIVE: Interactive displays armed with natural user interfaces (NUIs) will likely lead the next breakthrough in consumer electronics, and brain-computer interfaces (BCIs) are often regarded as the ultimate NUI-enabling machines to respond to human emotions and mental states. Steady-state visual evoked potentials (SSVEPs) are a commonly used BCI modality due to the ease of detection and high information transfer rates. However, the presence of flickering stimuli may cause user discomfort and can even induce migraines and seizures...
December 21, 2016: Journal of Neural Engineering
Ismael Seáñez-González, Camilla Pierella, Ali Farshchiansadegh, Elias B Thorp, Xue Wang, Todd Parrish, Ferdinando A Mussa-Ivaldi
The purpose of this study was to identify rehabilitative effects and changes in white matter microstructure in people with high-level spinal cord injury following bilateral upper-extremity motor skill training. Five subjects with high-level (C5-C6) spinal cord injury (SCI) performed five visuo-spatial motor training tasks over 12 sessions (2-3 sessions per week). Subjects controlled a two-dimensional cursor with bilateral simultaneous movements of the shoulders using a non-invasive inertial measurement unit-based body-machine interface...
December 19, 2016: Brain Sciences
Maryam M Shanechi
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...
December 14, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Assema Lalzad, Flora Wong, Michal Schneider
Ultrasound can lead to thermal and mechanical effects in interrogated tissues. We reviewed the literature to explore the evidence on ultrasound heating on fetal and neonatal neural tissue. The results of animal studies have suggested that ultrasound exposure of the fetal or neonatal brain may lead to a significant temperature elevation at the bone-brain interface above current recommended safety thresholds. Temperature increases between 4.3 and 5.6°C have been recorded. Such temperature elevations can potentially affect neuronal structure and function and may also affect behavioral and cognitive function, such as memory and learning...
December 12, 2016: Ultrasound in Medicine & Biology
David Sussillo, Sergey D Stavisky, Jonathan C Kao, Stephen I Ryu, Krishna V Shenoy
A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to-kinematic mappings and became more robust with larger training data sets...
December 13, 2016: Nature Communications
Zhichuan Tang, Shouqian Sun, Sanyuan Zhang, Yumiao Chen, Chao Li, Shi Chen
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)...
December 2, 2016: Sensors
Sergei L Shishkin, Yuri O Nuzhdin, Evgeny P Svirin, Alexander G Trofimov, Anastasia A Fedorova, Bogdan L Kozyrskiy, Boris M Velichkovsky
We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG) marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs) were collected during game's control-on and control-off conditions...
2016: Frontiers in Neuroscience
Robert D Flint, Joshua M Rosenow, Matthew C Tate, Marc W Slutzky
OBJECTIVE: Restoring or replacing function in paralyzed individuals will one day be achieved through the use of brain-machine interfaces. Regaining hand function is a major goal for paralyzed patients. Two competing prerequisites for the widespread adoption of any hand neuroprosthesis are accurate control over the fine details of movement, and minimized invasiveness. Here, we explore the interplay between these two goals by comparing our ability to decode hand movements with subdural and epidural field potentials (EFPs)...
November 30, 2016: Journal of Neural Engineering
Chaohua Wu, Ke Lin, Wei Wu, Xiaorong Gao
Recent years have witnessed brain-computer interface (BCI) as a promising technology for integrating human intelligence and machine intelligence. Currently, event-related potential (ERP)-based BCI is an important branch of noninvasive electroencephalogram (EEG)-based BCIs. Extracting ERPs from a limited number of trials remains challenging due to their low signal-to-noise ratio (SNR) and low spatial resolution caused by volume conduction. In this paper, we propose a probabilistic model for trial-by-trial concatenated EEG, in which the concatenated ERPs are expressed as a linear combination of a set of discrete sine and cosine bases...
November 17, 2016: IEEE Transactions on Neural Networks and Learning Systems
Jingyi Liu, Muhammad Abd-El-Barr, John H Chi
No abstract text is available yet for this article.
December 2016: Neurosurgery
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