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Brain machine interfaces

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https://www.readbyqxmd.com/read/29298691/volition-adaptive-control-for-gait-training-using-wearable-exoskeleton-preliminary-tests-with-incomplete-spinal-cord-injury-individuals
#1
Vijaykumar Rajasekaran, Eduardo López-Larraz, Fernando Trincado-Alonso, Joan Aranda, Luis Montesano, Antonio J Del-Ama, Jose L Pons
BACKGROUND: Gait training for individuals with neurological disorders is challenging in providing the suitable assistance and more adaptive behaviour towards user needs. The user specific adaptation can be defined based on the user interaction with the orthosis and by monitoring the user intentions. In this paper, an adaptive control model, commanded by the user intention, is evaluated using a lower limb exoskeleton with incomplete spinal cord injury individuals (SCI). METHODS: A user intention based adaptive control model has been developed and evaluated with 4 incomplete SCI individuals across 3 sessions of training per individual...
January 3, 2018: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/29297303/an-improved-discriminative-filter-bank-selection-approach-for-motor-imagery-eeg-signal-classification-using-mutual-information
#2
Shiu Kumar, Alok Sharma, Tatsuhiko Tsunoda
BACKGROUND: Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent. METHODS: In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks...
December 28, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29281985/deep-convolutional-neural-networks-for-pan-specific-peptide-mhc-class-i-binding-prediction
#3
Youngmahn Han, Dongsup Kim
BACKGROUND: Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, machine-learning-based methods have generated successful results by training large amounts of experimental data. However, many machine learning-based methods are generally less sensitive in recognizing locally-clustered interactions, which can synergistically stabilize peptide binding...
December 28, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29277720/extracting-information-from-the-shape-and-spatial-distribution-of-evoked-potentials
#4
Vítor Lopes-Dos-Santos, Hernan G Rey, Joaquin Navajas, Rodrigo Quian Quiroga
BACKGROUND: Over 90 years after its first recording, scalp electroencephalography (EEG) remains one of the most widely used techniques in human neuroscience research, in particular for the study of event-related potentials (ERPs). However, because of its low signal-to-noise ratio, extracting useful information from these signals continues to be a hard-technical challenge. Many studies focus on simple properties of the ERPs such as peaks, latencies, and slopes of signal deflections. NEW METHOD: To overcome these limitations, we developed the Wavelet-Information method which uses wavelet decomposition, information theory, and a quantification based on single-trial decoding performance to extract information from evoked responses...
December 22, 2017: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/29234269/application-of-the-stockwell-transform-to-electroencephalographic-signal-analysis-during-gait-cycle
#5
Mario Ortiz, Marisol Rodríguez-Ugarte, Eduardo Iáñez, José M Azorín
The analysis of electroencephalographic signals in frequency is usually not performed by transforms that can extract the instantaneous characteristics of the signal. However, the non-steady state nature of these low voltage electrical signals makes them suitable for this kind of analysis. In this paper a novel tool based on Stockwell transform is tested, and compared with techniques such as Hilbert-Huang transform and Fast Fourier Transform, for several healthy individuals and patients that suffer from lower limb disability...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/29224063/an-efficient-scheme-for-mental-task-classification-utilizing-reflection-coefficients-obtained-from-autocorrelation-function-of-eeg-signal
#6
M M Rahman, M A Chowdhury, S A Fattah
Classification of different mental tasks using electroencephalogram (EEG) signal plays an imperative part in various brain-computer interface (BCI) applications. In the design of BCI systems, features extracted from lower frequency bands of scalp-recorded EEG signals are generally considered to classify mental tasks and higher frequency bands are mostly ignored as noise. However, in this paper, it is demonstrated that high frequency components of EEG signal can provide accommodating data for enhancing the classification performance of the mental task-based BCI...
December 9, 2017: Brain Informatics
https://www.readbyqxmd.com/read/29218004/decoding-of-self-paced-lower-limb-movement-intention-a-case-study-on-the-influence-factors
#7
Dong Liu, Weihai Chen, Ricardo Chavarriaga, Zhongcai Pei, José Del R Millán
Brain-machine interfaces (BMIs) have been applied as new rehabilitation tools for motor disabled individuals. Active involvement of cerebral activity has been shown to enhance neuroplasticity and thus to restore mobility. Various studies have focused on the detection of upper-limb movement intention, while the fewer study has investigated the lower-limb movement intention decoding. This study presents a BMI to decode the self-paced lower-limb movement intention, with 10 healthy subjects participating in the experiment...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/29215082/the-myokinetic-control-interface-tracking-implanted-magnets-as-a-means-for-prosthetic-control
#8
S Tarantino, F Clemente, D Barone, M Controzzi, C Cipriani
Upper limb amputation deprives individuals of their innate ability to manipulate objects. Such disability can be restored with a robotic prosthesis linked to the brain by a human-machine interface (HMI) capable of decoding voluntary intentions, and sending motor commands to the prosthesis. Clinical or research HMIs rely on the interpretation of electrophysiological signals recorded from the muscles. However, the quest for an HMI that allows for arbitrary and physiologically appropriate control of dexterous prostheses, is far from being completed...
December 7, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29198041/ethical-considerations-on-novel-neuronal-interfaces
#9
Kadircan H Keskinbora, Kader Keskinbora
Wireless powered implants, each smaller than a grain of rice, have the potential to scan and stimulate brain cells. Further research may lead to next-generation brain-machine interfaces for controlling prosthetics, exoskeletons, and robots, as well as "electroceuticals" to treat disorders of the brain and body. In conditions that can be particularly alleviated with brain stimulation, the use of such mini devices may pose certain challenges. Health professionals are becoming increasingly more accountable in decision-making processes that have impacts on the life quality of individuals...
December 2, 2017: Neurological Sciences
https://www.readbyqxmd.com/read/29192609/superior-arm-movement-decoding-from-cortex-with-a-new-unsupervised-learning-algorithm
#10
Joseph Gerard Makin, Joseph O'Doherty, Mariana M B Cardoso, Philip N Sabes
OBJECTIVE: The aim of this work is to improve the state of the art for motor-control with a brain-machine interface (BMI). BMIs use neurological recording devices and a decoding algorithms to transform brain activity directly into real-time control of a machine, archetypically a robotic arm or a cursor. The standard procedure treats neural activity--vectors of spike counts in small temporal windows--as noisy observations of the kinematic state (position, velocity, acceleration) of the fingertip...
December 1, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29182152/decoding-of-finger-trajectory-from-ecog-using-deep-learning
#11
Ziqian Xie, Odelia Schwartz, Abhishek Prasad
Conventional decoding pipeline for brain machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance...
November 28, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29180616/changes-in-cortical-network-connectivity-with-long-term-brain-machine-interface-exposure-after-chronic-amputation
#12
Karthikeyan Balasubramanian, Mukta Vaidya, Joshua Southerland, Islam Badreldin, Ahmed Eleryan, Kazutaka Takahashi, Kai Qian, Marc W Slutzky, Andrew H Fagg, Karim Oweiss, Nicholas G Hatsopoulos
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...
November 27, 2017: Nature Communications
https://www.readbyqxmd.com/read/29170726/accuracy-to-detection-timing-for-assisting-repetitive-facilitation-exercise-system-using-mrcp-and-svm
#13
Satoshi Miura, Junichi Takazawa, Yo Kobayashi, Masakatsu G Fujie
This paper presents a feasibility study of a brain-machine interface system to assist repetitive facilitation exercise. Repetitive facilitation exercise is an effective rehabilitation method for patients with hemiplegia. In repetitive facilitation exercise, a therapist stimulates the paralyzed part of the patient while motor commands run along the nerve pathway. However, successful repetitive facilitation exercise is difficult to achieve and even a skilled practitioner cannot detect when a motor command occurs in patient's brain...
2017: Robotics and Biomimetics
https://www.readbyqxmd.com/read/29163123/closed-loop-hybrid-gaze-brain-machine-interface-based-robotic-arm-control-with-augmented-reality-feedback
#14
Hong Zeng, Yanxin Wang, Changcheng Wu, Aiguo Song, Jia Liu, Peng Ji, Baoguo Xu, Lifeng Zhu, Huijun Li, Pengcheng Wen
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...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/29163122/selectivity-and-longevity-of-peripheral-nerve-and-machine-interfaces-a-review
#15
REVIEW
Usman Ghafoor, Sohee Kim, Keum-Shik Hong
For those individuals with upper-extremity amputation, a daily normal living activity is no longer possible or it requires additional effort and time. With the aim of restoring their sensory and motor functions, theoretical and technological investigations have been carried out in the field of neuroprosthetic systems. For transmission of sensory feedback, several interfacing modalities including indirect (non-invasive), direct-to-peripheral-nerve (invasive), and cortical stimulation have been applied. Peripheral nerve interfaces demonstrate an edge over the cortical interfaces due to the sensitivity in attaining cortical brain signals...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/29152523/workshops-of-the-sixth-international-brain-computer-interface-meeting-brain-computer-interfaces-past-present-and-future
#16
Jane E Huggins, Christoph Guger, Mounia Ziat, Thorsten O Zander, Denise Taylor, Michael Tangermann, Aureli Soria-Frisch, John Simeral, Reinhold Scherer, Rüdiger Rupp, Giulio Ruffini, Douglas K R Robinson, Nick F Ramsey, Anton Nijholt, Gernot Müller-Putz, Dennis J McFarland, Donatella Mattia, Brent J Lance, Pieter-Jan Kindermans, Iñaki Iturrate, Christian Herff, Disha Gupta, An H Do, Jennifer L Collinger, Ricardo Chavarriaga, Steven M Chase, Martin G Bleichner, Aaron Batista, Charles W Anderson, Erik J Aarnoutse
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...
2017: Brain Computer Interfaces
https://www.readbyqxmd.com/read/29130452/augmenting-intracortical-brain-machine-interface-with-neurally-driven-error-detectors
#17
Nir Even-Chen, Sergey D Stavisky, Jonathan C Kao, Stephen I Ryu, Krishna V Shenoy
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...
November 13, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29118386/code-modulated-visual-evoked-potentials-using-fast-stimulus-presentation-and-spatiotemporal-beamformer-decoding
#18
Benjamin Wittevrongel, Elia Van Wolputte, Marc M Van Hulle
When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer's occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase lags of which can be tied to these targets. The cVEP paradigm has enjoyed interest in the brain-computer interfacing (BCI) community for the reported high information transfer rates (ITR, in bits/min). In this study, we introduce a novel decoding algorithm based on spatiotemporal beamforming, and show that this algorithm is able to accurately identify the gazed target...
November 8, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29109247/highly-scalable-multichannel-mesh-electronics-for-stable-chronic-brain-electrophysiology
#19
Tian-Ming Fu, Guosong Hong, Robert D Viveros, Tao Zhou, Charles M Lieber
Implantable electrical probes have led to advances in neuroscience, brain-machine interfaces, and treatment of neurological diseases, yet they remain limited in several key aspects. Ideally, an electrical probe should be capable of recording from large numbers of neurons across multiple local circuits and, importantly, allow stable tracking of the evolution of these neurons over the entire course of study. Silicon probes based on microfabrication can yield large-scale, high-density recording but face challenges of chronic gliosis and instability due to mechanical and structural mismatch with the brain...
November 21, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29104374/gesture-decoding-using-ecog-signals-from-human-sensorimotor-cortex-a-pilot-study
#20
Yue Li, Shaomin Zhang, Yile Jin, Bangyu Cai, Marco Controzzi, Junming Zhu, Jianmin Zhang, Xiaoxiang Zheng
Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system...
2017: Behavioural Neurology
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