keyword
MENU ▼
Read by QxMD icon Read
search

brain-machine interface

keyword
https://www.readbyqxmd.com/read/28198591/interpenetrating-conducting-hydrogel-materials-for-neural-interfacing-electrodes
#1
Josef Goding, Aaron Gilmour, Penny Martens, Laura Poole-Warren, Rylie Green
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...
February 15, 2017: Advanced Healthcare Materials
https://www.readbyqxmd.com/read/28198354/noise-robust-unsupervised-spike-sorting-based-on-discriminative-subspace-learning-with-outlier-handling
#2
Mohammad Reza Keshtkaran, Zhi Yang
OBJECTIVE: Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high...
February 15, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28192282/classifier-transfer-with-data-selection-strategies-for-online-support-vector-machine-classification-with-class-imbalance
#3
Mario Michael Krell, Nils Wilshusen, Anett Seeland, Su Kyoung Kim
OBJECTIVE: Classifier transfers usually come with dataset shifts. To overcome dataset shifts in practical applications, we consider the limitations in computational resources in this paper for the adaptation of batch learning algorithms, like the support vector machine (SVM). APPROACH: We focus on data selection strategies which limit the size of the stored training data by different inclusion, exclusion, and further dataset manipulation criteria like handling class imbalance with two new approaches...
February 13, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28190641/emergence-of-coordinated-neural-dynamics-underlies-neuroprosthetic-learning-and-skillful-control
#4
Vivek R Athalye, Karunesh Ganguly, Rui M Costa, Jose M Carmena
During motor learning, movements and underlying neural activity initially exhibit large trial-to-trial variability that decreases over learning. However, it is unclear how task-relevant neural populations coordinate to explore and consolidate activity patterns. Exploration and consolidation could happen for each neuron independently, across the population jointly, or both. We disambiguated among these possibilities by investigating how subjects learned de novo to control a brain-machine interface using neurons from motor cortex...
February 6, 2017: Neuron
https://www.readbyqxmd.com/read/28162776/the-future-of-psychiatry-brain-devices
#5
Jorge Alberto Costa E Silva, Ricardo Ewbank Steffen
Recent advances in deep brain stimulators and brain-machine interfaces have greatly expanded the possibilities of neuroprosthetics and neuromodulation. Together with advances in neuroengineering, nanotechnology, molecular biology and material sciences, it is now possible to address fundamental questions in neuroscience in new, more powerful ways. It is now possible to apply these new technologies in ways that range from augmenting and restoring function to neuromodulation modalities that treat neuropsychiatric disorders...
January 11, 2017: Metabolism: Clinical and Experimental
https://www.readbyqxmd.com/read/28143603/classification-of-upper-limb-center-out-reaching-tasks-by-means-of-eeg-based-continuous-decoding-techniques
#6
Andrés Úbeda, José M Azorín, Ricardo Chavarriaga, José Del R Millán
BACKGROUND: One of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low frequency EEG components. However, the validity of these results is still a matter of discussion. In this paper, we assess the feasibility of decoding upper limb kinematics from EEG signals in center-out reaching tasks during passive and active movements...
February 1, 2017: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/28141803/brain-computer-interface-based-communication-in-the-completely-locked-in-state
#7
Ujwal Chaudhary, Bin Xia, Stefano Silvoni, Leonardo G Cohen, Niels Birbaumer
Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS). Based on a motor learning theoretical context and on the failure of neuroelectric brain-computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure. Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)-two of them in permanent CLIS and two entering the CLIS without reliable means of communication-learned to answer personal questions with known answers and open questions all requiring a "yes" or "no" thought using frontocentral oxygenation changes measured with fNIRS...
January 2017: PLoS Biology
https://www.readbyqxmd.com/read/28141525/e-o-a-brain-controlled-lower-limb-exoskeleton-for-rhesus-macaques
#8
Tristan Vouga, Katie Zhuang, Jeremy Olivier, Mikhail Lebedev, Miguel Nicolelis, Mohamed Bouri, Hannes Bleuler
Recent advances in the field of brain-machine interfaces (BMIs) have demonstrated enormous potential to shape the future of rehabilitation and prosthetic devices. Here, a lower-limb exoskeleton controlled by the intracortical activity of an awake, behaving rhesus macaque is presented as a proof-of-concept for a locomotor BMI. A detailed description of the mechanical device, including its innovative features and first experimental results, is provided. During operation, BMI-decoded position and velocity are directly mapped onto the bipedal exoskeleton's motions, which then move the monkey's legs as the monkey remains physically passive...
January 26, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28113942/decoding-local-field-potentials-for-neural-interfaces
#9
Andrew Jackson, Thomas M Hall
The stability and frequency content of the local field potentials (LFP) 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...
November 14, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28113512/generation-of-stimulus-triggering-from-intracortical-spike-activity-for-brain-machine-body-interfaces-bmbis
#10
Shahab Shahdoost, Randolph Nudo, Pedram Mohseni
Brain-machine-body interfaces (BMBIs) aim to create an artificial connection in the nervous system by converting neural activity recorded from one cortical region to electrical stimuli delivered to another cortical region, spinal cord, or muscles in real-time. In particular, conditioning-mode BMBIs utilize such activity-dependent stimulation strategies to induce functional re-organization in the nervous system and promote functional recovery after injury by exploiting mechanisms underlying neuroplasticity. This paper reports on reconfigurable, field-programmable gate array (FPGA)-based implementation of a translation algorithm to extract multichannel stimulus trigger signals from intracortical neural spike activity...
October 5, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28113323/brain-machine-interface-control-algorithms
#11
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
https://www.readbyqxmd.com/read/28106034/corrigendum-making-brain-machine-interfaces-robust-to-future-neural-variability
#12
David Sussillo, Sergey D Stavisky, Jonathan C Kao, Stephen I Ryu, Krishna V Shenoy
No abstract text is available yet for this article.
January 20, 2017: Nature Communications
https://www.readbyqxmd.com/read/28102825/versatile-modular-three-dimensional-microelectrode-arrays-for-neuronal-ensemble-recordings-from-design-to-fabrication-assembly-and-functional-validation-in-non-human-primates
#13
Falk Barz, Alessandro Livi, Marco Lanzilotto, Monica Maranesi, Luca Bonini, Oliver Paul, Patrick Ruther
<i>Objective. </i>Application-specific designs of electrode arrays offer an improved effectiveness for providing access to targeted brain regions in neuroscientific research and brain machine interfaces. The simultaneous and stable recording of neuronal ensembles is the main goal in the design of advanced neural interfaces. Here, we describe the development and assembly of highly customizable three-dimensional microelectrode arrays and demonstrate their recording performance in chronic applications in non-human primates...
January 19, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28087767/trial-by-trial-motor-cortical-correlates-of-a-rapidly-adapting-visuomotor-internal-model
#14
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
https://www.readbyqxmd.com/read/28068293/a-hybrid-bmi-based-exoskeleton-for-paresis-emg-control-for-assisting-arm-movements
#15
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
https://www.readbyqxmd.com/read/28065938/efficient-implementation-of-a-real-time-estimation-system-for-thalamocortical-hidden-parkinsonian-properties
#16
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
https://www.readbyqxmd.com/read/28059065/rapid-control-and-feedback-rates-enhance-neuroprosthetic-control
#17
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
https://www.readbyqxmd.com/read/28055887/eeg-based-strategies-to-detect-motor-imagery-for-control-and-rehabilitation
#18
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
https://www.readbyqxmd.com/read/28029630/design-of-a-closed-loop-bidirectional-brain-machine-interface-system-with-energy-efficient-neural-feature-extraction-and-pid-control
#19
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
https://www.readbyqxmd.com/read/28026782/a-hybrid-cmos-memristor-neuromorphic-synapse
#20
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
keyword
keyword
14889
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"