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Brain machine interfaces

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https://www.readbyqxmd.com/read/28630937/high-precision-neural-decoding-of-complex-movement-trajectories-using-recursive-bayesian-estimation-with-dynamic-movement-primitives
#1
Guy Hotson, Ryan J Smith, Adam G Rouse, Marc H Schieber, Nitish V Thakor, Brock A Wester
Brain-machine interfaces (BMIs) are a rapidly progressing technology with the potential to restore function to victims of severe paralysis via neural control of robotic systems. Great strides have been made in directly mapping a user's cortical activity to control of the individual degrees of freedom of robotic end-effectors. While BMIs have yet to achieve the level of reliability desired for widespread clinical use, environmental sensors (e.g. RGB-D cameras for object detection) and prior knowledge of common movement trajectories hold great potential for improving system performance...
July 2016: IEEE Robotics and Automation Letters
https://www.readbyqxmd.com/read/28625485/motor-cortical-visuomotor-feedback-activity-is-initially-isolated-from-downstream-targets-in-output-null-neural-state-space-dimensions
#2
Sergey D Stavisky, Jonathan C Kao, Stephen I Ryu, Krishna V Shenoy
Neural circuits must transform new inputs into outputs without prematurely affecting downstream circuits while still maintaining other ongoing communication with these targets. We investigated how this isolation is achieved in the motor cortex when macaques received visual feedback signaling a movement perturbation. To overcome limitations in estimating the mapping from cortex to arm movements, we also conducted brain-machine interface (BMI) experiments where we could definitively identify neural firing patterns as output-null or output-potent...
June 6, 2017: Neuron
https://www.readbyqxmd.com/read/28620273/state-dependent-decoding-algorithms-improve-the-performance-of-a-bidirectional-bmi-in-anesthetized-rats
#3
Vito De Feo, Fabio Boi, Houman Safaai, Arno Onken, Stefano Panzeri, Alessandro Vato
Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28615329/physiological-properties-of-brain-machine-interface-input-signals
#4
Marc W Slutzky, Robert D Flint
Brain machine interfaces (BMIs), also called brain computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically-viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably the first of these is the selection of brain signals used to control BMIs. Here, we summarize the physiological characteristics and performance-including movement-related information, longevity, and stability-of multiple types of input signals that have been used in invasive BMIs to date...
June 14, 2017: Journal of Neurophysiology
https://www.readbyqxmd.com/read/28613237/major-depression-detection-from-eeg-signals-using-kernel-eigen-filter-bank-common-spatial-patterns
#5
Shih-Cheng Liao, Chien-Te Wu, Hao-Chuan Huang, Wei-Teng Cheng, Yi-Hung Liu
Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i...
June 14, 2017: Sensors
https://www.readbyqxmd.com/read/28597847/mapping-the-fine-structure-of-cortical-activity-with-different-micro-ecog-electrode-array-geometries
#6
Xi Wang, Alexis Gkogkidis, Olga Iljina, Lukas Fiederer, Chiristian Henle, Irina Mader, Jan Kaminsky, Thomas Stieglitz, Mortimer Gierthmuehlen, Tonio Ball
OBJECTIVE: Innovations in micro-electrocorticography (µECoG) electrode array manufacturing now allow for intricate designs with smaller contact diameters and/or pitch (i.e., center-to-center contact distance) down to the sub-mm range. The aims of the present study were: (i) to investigate whether frequency ranges up to 400 Hz can be reproducibly observed in µECoG recordings and (ii) to examine how differences in topographical substructure between these frequency bands and electrode array geometries can be quantified...
June 9, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28597018/robotic-devices-and-brain-machine-interfaces-for-hand-rehabilitation-post-stroke
#7
Alistair C McConnell, Renan C Moioli, Fabricio L Brasil, Marta Vallejo, David W Corne, Patricia A Vargas, Adam A Stokes
OBJECTIVE: To review the state of the art of robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain-machine interfaces. Each patient has a unique clinical history and, in response to personalized treatment needs, research into individualized and at-home treatment options has expanded rapidly in recent years. This has resulted in the development of many devices and design strategies for use in stroke rehabilitation. METHODS: The development progression of robotic-aided hand physiotherapy devices and brain-machine interface systems is outlined, focussing on those with mechanisms and control strategies designed to improve recovery outcomes of the hand post-stroke...
June 8, 2017: Journal of Rehabilitation Medicine
https://www.readbyqxmd.com/read/28573983/neuromorphic-neural-interfaces-from-neurophysiological-inspiration-to-biohybrid-coupling-with-nervous-systems
#8
Frédéric D Broccard, Siddharth Joshi, Jun Wang, Gert Cauwenberghs
OBJECTIVE: Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks...
June 2, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28570813/polymer-composite-with-carbon-nanofibers-aligned-during-thermal-drawing-as-a-microelectrode-for-chronic-neural-interfaces
#9
Yuanyuan Guo, Shan Jiang, Benjamin J B Grena, Ian F Kimbrough, Emily G Thompson, Yoel Fink, Harald Sontheimer, Tatsuo Yoshinobu, Xiaoting Jia
Microelectrodes provide a direct pathway to investigate brain activities electrically from the external world, which has advanced our fundamental understanding of brain functions and has been utilized for rehabilitative applications as brain-machine interfaces. However, minimizing the tissue response and prolonging the functional durations of these devices remain challenging. Therefore, the development of next-generation microelectrodes as neural interfaces is actively progressing from traditional inorganic materials toward biocompatible and functional organic materials with a miniature footprint, good flexibility, and reasonable robustness...
June 13, 2017: ACS Nano
https://www.readbyqxmd.com/read/28559792/open-source-low-cost-free-behavior-monitoring-and-reward-system-for-neuroscience-research-in-non-human-primates
#10
Tyler Libey, Eberhard E Fetz
We describe a low-cost system designed to document bodily movement and neural activity and deliver rewards to monkeys behaving freely in their home cage. An important application is to studying brain-machine interface (BMI) systems during free behavior, since brain signals associated with natural movement can differ significantly from those associated with more commonly used constrained conditions. Our approach allows for short-latency (<500 ms) reward delivery and behavior monitoring using low-cost off-the-shelf components...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28558002/electroencephalogram-based-decoding-cognitive-states-using-convolutional-neural-network-and-likelihood-ratio-based-score-fusion
#11
Raheel Zafar, Sarat C Dass, Aamir Saeed Malik
Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity...
2017: PloS One
https://www.readbyqxmd.com/read/28553580/fast-and-efficient-four%C3%A2-class-motor-imagery-electroencephalography-signal-analysis-using-common-spatial-pattern-ridge-regression-algorithm-for-the-purpose-of-brain-computer-interface
#12
Sahar Seifzadeh, Mohammad Rezaei, Karim Faez, Mahmood Amiri
Brain-computer interfaces enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. One of the most challenging issues in this regard is the balance between the accuracy of brain signals from patients and the speed of interpreting them into machine language. The main objective of this paper is to analyze different approaches to achieve the balance more quickly and in a better way. To reduce the ocular artifacts, the symmetric prewhitening independent component analysis (ICA) algorithm has been evaluated, which has the lowest runtime and lowest signal-to-interference (SIR) index, without destroying the original signal...
April 2017: Journal of Medical Signals and Sensors
https://www.readbyqxmd.com/read/28541908/a-hardware-efficient-scalable-spike-sorting-neural-signal-processor-module-for-implantable-high-channel-count-brain-machine-interfaces
#13
Yuning Yang, Sam Boling, Andrew J Mason
Next-generation brain machine interfaces demand a high-channel-count neural recording system to wirelessly monitor activities of thousands of neurons. A hardware efficient neural signal processor (NSP) is greatly desirable to ease the data bandwidth bottleneck for a fully implantable wireless neural recording system. This paper demonstrates a complete multichannel spike sorting NSP module that incorporates all of the necessary spike detector, feature extractor, and spike classifier blocks. To meet high-channel-count and implantability demands, each block was designed to be highly hardware efficient and scalable while sharing resources efficiently among multiple channels...
May 24, 2017: IEEE Transactions on Biomedical Circuits and Systems
https://www.readbyqxmd.com/read/28538681/human-thalamic-somatosensory-nucleus-ventral-caudal-vc-as-a-locus-for-stimulation-by-inputs-from-tactile-noxious-and-thermal-sensors-on-an-active-prosthesis
#14
REVIEW
Jui Hong Chien, Anna Korzeniewska, Luana Colloca, Claudia Campbell, Patrick Dougherty, Frederick Lenz
The forebrain somatic sensory locus for input from sensors on the surface of an active prosthesis is an important component of the Brain Machine Interface. We now review the neuronal responses to controlled cutaneous stimuli and the sensations produced by Threshold Stimulation at Microampere current levels (TMIS) in such a locus, the human thalamic Ventral Caudal nucleus (Vc). The responses of these neurons to tactile stimuli mirror those for the corresponding class of tactile mechanoreceptor fiber in the peripheral nerve, and TMIS can evoke sensations like those produced by the stimuli that optimally activate each class...
May 24, 2017: Sensors
https://www.readbyqxmd.com/read/28504971/robust-tactile-sensory-responses-in-finger-area-of-primate-motor-cortex-relevant-to-prosthetic-control
#15
Karen E Schroeder, Zachary T Irwin, Autumn J Bullard, David E Thompson, J Nicole Bentley, William C Stacey, Parag G Patil, Cynthia A Chestek
OBJECTIVE: Challenges in improving the performance of dexterous upper-limb brain-machine interfaces (BMIs) have prompted renewed interest in quantifying the amount and type of sensory information naturally encoded in the primary motor cortex (M1). Previous single unit studies in monkeys showed M1 is responsive to tactile stimulation, as well as passive and active movement of the limbs. However, recent work in this area has focused primarily on proprioception. Here we examined instead how tactile somatosensation of the hand and fingers is represented in M1...
May 15, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28499043/bioethics-and-transhumanism
#16
Allen Porter
Transhumanism is a "technoprogressive" socio-political and intellectual movement that advocates for the use of technology in order to transform the human organism radically, with the ultimate goal of becoming "posthuman." To this end, transhumanists focus on and encourage the use of new and emerging technologies, such as genetic engineering and brain-machine interfaces. In support of their vision for humanity, and as a way of reassuring those "bioconservatives" who may balk at the radical nature of that vision, transhumanists claim common ground with a number of esteemed thinkers and traditions, from the ancient philosophy of Plato and Aristotle to the postmodern philosophy of Nietzsche...
June 1, 2017: Journal of Medicine and Philosophy
https://www.readbyqxmd.com/read/28436837/passive-bci-in-operational-environments-insights-recent-advances-and-future-trends
#17
Pietro Arico, Gianluca Borghini, Gianluca Di Flumeri, Nicolina Sciaraffa, Alfredo Colosimo, Fabio Babiloni
OBJECTIVE: this mini-review aims to highlight recent important aspects to consider and evaluate when passive Brain-Computer Interface (pBCI) systems would be developed and used in operational environments, and remarks future directions of their applications. METHODS: Electroencephalography (EEG)-based pBCI has become an important tool for real-time analysis of brain activity, since it could potentially provide, covertly - without distracting the user from the main task - and objectively - not affected by the subjective judgement of an observer or the user itself - information about the operator cognitive state...
April 17, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28420954/multiple-kernel-based-region-importance-learning-for-neural-classification-of-gait-states-from-eeg-signals
#18
Yuhang Zhang, Saurabh Prasad, Atilla Kilicarslan, Jose L Contreras-Vidal
With the development of Brain Machine Interface (BMI) systems, people with motor disabilities are able to control external devices to help them restore movement abilities. Longitudinal validation of these systems is critical not only to assess long-term performance reliability but also to investigate adaptations in electrocortical patterns due to learning to use the BMI system. In this paper, we decode the patterns of user's intended gait states (e.g., stop, walk, turn left, and turn right) from scalp electroencephalography (EEG) signals and simultaneously learn the relative importance of different brain areas by using the multiple kernel learning (MKL) algorithm...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28420129/hand-motion-detection-in-fnirs-neuroimaging-data
#19
Mohammadreza Abtahi, Amir Mohammad Amiri, Dennis Byrd, Kunal Mankodiya
As the number of people diagnosed with movement disorders is increasing, it becomes vital to design techniques that allow the better understanding of human brain in naturalistic settings. There are many brain imaging methods such as fMRI, SPECT, and MEG that provide the functional information of the brain. However, these techniques have some limitations including immobility, cost, and motion artifacts. One of the most emerging portable brain scanners available today is functional near-infrared spectroscopy (fNIRS)...
April 15, 2017: Healthcare (Basel, Switzerland)
https://www.readbyqxmd.com/read/28411726/a-wellness-platform-for-stereoscopic-3d-video-systems-using-eeg-based-visual-discomfort-evaluation-technology
#20
Min-Koo Kang, Hohyun Cho, Han-Mu Park, Sung Chan Jun, Kuk-Jin Yoon
Recent advances in three-dimensional (3D) video technology have extended the range of our experience while providing various 3D applications to our everyday life. Nevertheless, the so-called visual discomfort (VD) problem inevitably degrades the quality of experience in stereoscopic 3D (S3D) displays. Meanwhile, electroencephalography (EEG) has been regarded as one of the most promising brain imaging modalities in the field of cognitive neuroscience. In an effort to facilitate comfort with S3D displays, we propose a new wellness platform using EEG...
July 2017: Applied Ergonomics
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