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Brain machine interfaces

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https://www.readbyqxmd.com/read/29783119/in-vivo-imaging-of-neuronal-calcium-during-electrode-implantation-spatial-and-temporal-mapping-of-damage-and-recovery
#1
James R Eles, Alberto L Vazquez, Takashi D Y Kozai, X Tracy Cui
Implantable electrode devices enable long-term electrophysiological recordings for brain-machine interfaces and basic neuroscience research. Implantation of these devices, however, leads to neuronal damage and progressive neural degeneration that can lead to device failure. The present study uses in vivo two-photon microscopy to study the calcium activity and morphology of neurons before, during, and one month after electrode implantation to determine how implantation trauma injures neurons. We show that implantation leads to prolonged, elevated calcium levels in neurons within 150 μm of the electrode interface...
May 7, 2018: Biomaterials
https://www.readbyqxmd.com/read/29772957/brain-machine-interfaces-powerful-tools-for-clinical-treatment-and-neuroscientific-investigations
#2
Marc W Slutzky
Brain-machine interfaces (BMIs) have exploded in popularity in the past decade. BMIs, also called brain-computer interfaces, provide a direct link between the brain and a computer, usually to control an external device. BMIs have a wide array of potential clinical applications, ranging from restoring communication to people unable to speak due to amyotrophic lateral sclerosis or a stroke, to restoring movement to people with paralysis from spinal cord injury or motor neuron disease, to restoring memory to people with cognitive impairment...
May 1, 2018: Neuroscientist: a Review Journal Bringing Neurobiology, Neurology and Psychiatry
https://www.readbyqxmd.com/read/29771663/applications-of-deep-learning-and-reinforcement-learning-to-biological-data
#3
Mufti Mahmud, Mohammed Shamim Kaiser, Amir Hussain, Stefano Vassanelli
Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence...
June 2018: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/29752486/effects-of-speed-and-direction-of-perturbation-on-electroencephalographic-and-balance-responses
#4
Rahul Goel, Recep A Ozdemir, Sho Nakagome, Jose L Contreras-Vidal, William H Paloski, Pranav J Parikh
The modulation of perturbation-evoked potential (PEP) N1 as a function of different biomechanical characteristics of perturbation has been investigated before. However, it remains unknown whether the PEP N1 modulation contributes to the shaping of the functional postural response. To improve this understanding, we examined the modulation of functional postural response in relation to the PEP N1 response in ten healthy young subjects during unpredictable perturbations to their upright stance-translations of the support surface in a forward or backward direction at two different amplitudes of constant speed...
May 11, 2018: Experimental Brain Research. Experimentelle Hirnforschung. Expérimentation Cérébrale
https://www.readbyqxmd.com/read/29746465/the-cybathlon-bci-race-successful-longitudinal-mutual-learning-with-two-tetraplegic-users
#5
Serafeim Perdikis, Luca Tonin, Sareh Saeedi, Christoph Schneider, José Del R Millán
This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain-computer interface (BCI) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event. We hypothesized that, contrary to the popular trend of focusing mostly on the machine learning aspects of MI BCI training, a comprehensive mutual learning methodology that reinstates the three learning pillars (at the machine, subject, and application level) as equally significant could lead to a BCI-user symbiotic system able to succeed in real-world scenarios such as the Cybathlon event...
May 2018: PLoS Biology
https://www.readbyqxmd.com/read/29740302/improved-volitional-recall-of-motor-imagery-related-brain-activation-patterns-using-real-time-functional-mri-based-neurofeedback
#6
Epifanio Bagarinao, Akihiro Yoshida, Mika Ueno, Kazunori Terabe, Shohei Kato, Haruo Isoda, Toshiharu Nakai
Motor imagery (MI), a covert cognitive process where an action is mentally simulated but not actually performed, could be used as an effective neurorehabilitation tool for motor function improvement or recovery. Recent approaches employing brain-computer/brain-machine interfaces to provide online feedback of the MI during rehabilitation training have promising rehabilitation outcomes. In this study, we examined whether participants could volitionally recall MI-related brain activation patterns when guided using neurofeedback (NF) during training...
2018: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/29737970/multiband-tangent-space-mapping-and-feature-selection-for-classification-of-eeg-during-motor-imagery
#7
Md Rabiul Islam, Toshihisa Tanaka, Md Khademul Islam Molla
Abstract
 Objective. When designing multiclass motor imagery-based brain computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operational frequency bands related brain activities associated with MI tasks.
 
 Approach. A multichannel EEG signal is decomposed into multiple subbands, and tangent features are then estimated on each subband...
May 8, 2018: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29733940/a-fresh-look-at-functional-link-neural-network-for-motor-imagery-based-brain-computer-interface
#8
Imali T Hettiarachchi, Toktam Babaei, Thanh Nguyen, Chee P Lim, Saeid Nahavandi
BACKGROUND: Artificial neural networks (ANN) is one of the widely used classifiers in the brain computer interface (BCI) systems-based on noninvasive electroencephalography (EEG) signals. Among the different ANN architectures, the most commonly applied for BCI classifiers is the multilayer perceptron (MLP). When appropriately designed with optimal number of neuron layers and number of neurons per layer, the ANN can act as a universal approximator. However, due to the low signal-to-noise ratio of EEG signal data, overtraining problem may become an inherent issue, causing these universal approximators to fail in real-time applications...
May 4, 2018: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/29710754/structural-analysis-of-a-rehabilitative-training-system-based-on-a-ceiling-rail-for-safety-of-hemiplegia-patients
#9
Kyong Kim, Won Kyung Song, Woo Suk Chong, Chang Ho Yu
The body-weight support (BWS) function, which helps to decrease load stresses on a user, is an effective tool for gait and balance rehabilitation training for elderly people with weakened lower-extremity muscular strength, hemiplegic patients, etc. This study conducts structural analysis to secure user safety in order to develop a rail-type gait and balance rehabilitation training system (RRTS). The RRTS comprises a rail, trolley, and brain-machine interface. The rail (platform) is connected to the ceiling structure, bearing the loads of the RRTS and of the user and allowing locomobility...
April 17, 2018: Technology and Health Care: Official Journal of the European Society for Engineering and Medicine
https://www.readbyqxmd.com/read/29698736/enhancement-of-motor-imagery-ability-via-combined-action-observation-and-motor-imagery-training-with-proprioceptive-neurofeedback
#10
Yumie Ono, Kenya Wada, Masaya Kurata, Naoto Seki
Varied individual ability to control the sensory-motor rhythms may limit the potential use of motor-imagery (MI) in neurorehabilitation and neuroprosthetics. We employed neurofeedback training of MI under action observation (AO: AOMI) with proprioceptive feedback and examined whether it could enhance MI-induced event-related desynchronization (ERD). Twenty-eight healthy young adults participated in the neurofeedback training. They performed MI while watching a video of hand-squeezing motion from a first-person perspective...
April 23, 2018: Neuropsychologia
https://www.readbyqxmd.com/read/29682000/an-adaptive-calibration-framework-for-mvep-based-brain-computer-interface
#11
Teng Ma, Fali Li, Peiyang Li, Dezhong Yao, Yangsong Zhang, Peng Xu
Electroencephalogram signals and the states of subjects are nonstationary. To track changing states effectively, an adaptive calibration framework is proposed for the brain-computer interface (BCI) with the motion-onset visual evoked potential (mVEP) as the control signal. The core of this framework is to update the training set adaptively for classifier training. The updating procedure consists of two operations, that is, adding new samples to the training set and removing old samples from the training set...
2018: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/29674949/effect-of-different-movement-speed-modes-on-human-action-observation-an-eeg-study
#12
Tian-Jian Luo, Jitu Lv, Fei Chao, Changle Zhou
Action observation (AO) generates event-related desynchronization (ERD) suppressions in the human brain by activating partial regions of the human mirror neuron system (hMNS). The activation of the hMNS response to AO remains controversial for several reasons. Therefore, this study investigated the activation of the hMNS response to a speed factor of AO by controlling the movement speed modes of a humanoid robot's arm movements. Since hMNS activation is reflected by ERD suppressions, electroencephalography (EEG) with BCI analysis methods for ERD suppressions were used as the recording and analysis modalities...
2018: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/29666460/cortical-classification-with-rhythm-entropy-for-error-processing-in-cocktail-party-environment-based-on-scalp-eeg-recording
#13
Yin Tian, Wei Xu, Li Yang
Using single-trial cortical signals calculated by weighted minimum norm solution estimation (WMNE), the present study explored a feature extraction method based on rhythm entropy to classify the scalp electroencephalography (EEG) signals of error response from that of correct response during performing auditory-track tasks in cocktail party environment. The classification rate achieved 89.7% with single-trial (≈700 ms) when using support vector machine(SVM) with the leave-one-out-cross-validation (LOOCV)...
April 17, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29660675/improvements-in-event-related-desynchronization-and-classification-performance-of-motor-imagery-using-instructive-dynamic-guidance-and-complex-tasks
#14
Yan Bian, Hongzhi Qi, Li Zhao, Dong Ming, Tong Guo, Xing Fu
BACKGROUND AND OBJECTIVE: The motor-imagery based brain-computer interface supplies a potential approach for motor-impaired patients, not only to control rehabilitation facilities but also to promote recovery from motor dysfunctions. To improve event-related desynchronization during motor imagery and obtain improved brain-computer interface classification accuracy, we introduce dynamic video guidance and complex motor tasks to the motor imagery paradigm. METHODS: Eleven participants were included in the experiment; 64-channel electroencephalographic data were collected and analyzed during four motor imagery tasks with different guidance...
March 30, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29623905/compact-standalone-platform-for-neural-recording-with-real-time-spike-sorting-and-data-logging
#15
Song Luan, Ian Williams, Michal Maslik, Yan Liu, Felipe De Carvalho, Andrew Jackson, Rodrigo Quian Quiroga, Timothy Constandinou
OBJECTIVE: Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective Brain Machine Interfaces (BMIs). These recordings generate enormous amounts of data for transmission & storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: 1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions); 2) producing real-time, low-latency, spike sorted data; and 3) long term untethered operation...
April 6, 2018: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29623902/a-continuous-time-resolved-measure-decoded-from-eeg-oscillatory-activity-predicts-working-memory-task-performance
#16
Elaine Astrand
OBJECTIVE: Working memory (WM), crucial for successful behavioral performance in most of our everyday activities, holds a central role in goal-directed behavior. As task demands increase, inducing higher WM load, maintaining successful behavioral performance requires the brain to work at the higher end of its capacity. Because it is depending on both external and internal factors, individual WM load likely varies in a continuous fashion. The feasibility to extract such a continuous measure in time that correlates to behavioral performance during a working memory task remains unsolved...
April 6, 2018: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29616982/a-fast-intracortical-brain-machine-interface-with-patterned-optogenetic-feedback
#17
Aamir Abbasi, Dorian Goueytes, Daniel E Shulz, Valerie Ego-Stengel, Luc Estebanez
OBJECTIVE: The development of brain-machine interfaces (BMIs) brings a new perspective to patients with a loss of autonomy. By combining online recordings of brain activity with a decoding algorithm, patients can learn to control a robotic arm in order to perform simple actions. However, in contrast to the vast amounts of somatosensory information channeled by limbs to the brain, current BMIs are devoid of touch and force sensors. Patients must therefore rely solely on vision and audition, which are maladapted to the control of a prosthesis...
April 4, 2018: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29596978/fast-and-robust-block-sparse-bayesian-learning-for-eeg-source-imaging
#18
Alejandro Ojeda, Kenneth Kreutz-Delgado, Tim Mullen
We propose a new Sparse Bayesian Learning (SBL) algorithm that can deliver fast, block-sparse, and robust solutions to the EEG source imaging (ESI) problem in the presence of noisy measurements. Current implementations of the SBL framework are computationally expensive and typically handle fluctuations in the measurement noise using different heuristics that are unsuitable for real-time imaging applications. We address these shortcomings by decoupling the estimation of the sensor noise covariance and the sparsity profile of the sources, thereby yielding an efficient two-stage algorithm...
March 26, 2018: NeuroImage
https://www.readbyqxmd.com/read/29563891/how-our-cognition-shapes-and-is-shaped-by-technology-a-common-framework-for-understanding-human-tool-use-interactions-in-the-past-present-and-future
#19
REVIEW
François Osiurak, Jordan Navarro, Emanuelle Reynaud
Over the evolution, humans have constantly developed and improved their technologies. This evolution began with the use of physical tools, those tools that increase our sensorimotor abilities (e.g., first stone tools, modern knives, hammers, pencils). Although we still use some of these tools, we also employ in daily life more sophisticated tools for which we do not systematically understand the underlying physical principles (e.g., computers, cars). Current research is also turned toward the development of brain-computer interfaces directly linking our brain activity to machines (i...
2018: Frontiers in Psychology
https://www.readbyqxmd.com/read/29557196/the-future-of-the-provision-process-for-mobility-assistive-technology-a-survey-of-providers
#20
Brad E Dicianno, James Joseph, Stacy Eckstein, Christina K Zigler, Eleanor J Quinby, Mark R Schmeler, Richard M Schein, Jon Pearlman, Rory A Cooper
PURPOSE: The purpose of this study was to evaluate the opinions of providers of mobility assistive technologies to help inform a research agenda and set priorities. MATERIALS AND METHODS: This survey study was anonymous and gathered opinions of individuals who participate in the process to provide wheelchairs and other assistive technologies to clients. Participants were asked to rank the importance of developing various technologies and rank items against each other in terms of order of importance...
March 20, 2018: Disability and Rehabilitation. Assistive Technology
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