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Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control

Journal of Physics: Conference Series 2021年第1期1828卷

作者： Michael Pritchard Abraham Itzhak Weinberg John A R Williams Felipe Campelo Harry Goldingay Diego R Faria Aston Robotics Vision and Intelligent Systems Laboratory (ARVIS Lab) College of Engineering and Physical Sciences Aston University Birmingham B4 7ET UK Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham B4 7ET UK Aston Institute of Photonic Technologies Aston University Birmingham B4 7ET UK

We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as part of a brain-computer interface (BCI) the cost of EEG equipment commonly prevents this approach from being adopted outside the lab. This study demonstrates as a proof-of-concept that multimodal classification can be achieved by using low-cost EMG and EEG devices in tandem, with statistical decision-level fusion, to a high degree of accuracy. We present multiple fusion methods, including those based on Jensen-Shannon divergence which had not previously been applied to this problem. We report accuracies of up to 99% when merging both signal modalities, improving on the best-case single-mode classification. We hence demonstrate the strengths of combining EMG and EEG in a multimodal classification system that could in future be leveraged as an alternative control mechanism for robotic prostheses.

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Small worlds and clustering in spatial networks

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Physical Review Research 2020年第2期2卷 023040-023040页

作者： Marián Boguñá Dmitri Krioukov Pedro Almagro M. Ángeles Serrano Departament de Física de la Matèria Condensada Universitat de Barcelona Martí i Franquès 1 E-08028 Barcelona Spain Universitat de Barcelona Institute of Complex Systems (UBICS) Universitat de Barcelona Barcelona Spain Network Science Institute Northeastern University 177 Huntington avenue Boston Massachusetts 022115 USA Department of Physics Department of Mathematics Department of Electrical & Computer Engineering Northeastern University 110 Forsyth Street 111 Dana Research Center Boston Massachusetts 02115 USA Institució Catalana de Recerca i Estudis Avançats (ICREA) Passeig Lluís Companys 23 E-08010 Barcelona Spain

Networks with underlying metric spaces attract increasing research attention in network science, statistical physics, applied mathematics, computer science, sociology, and other fields. This attention is further amplified by the current surge of activity in graph embedding. In the vast realm of spatial network models, only a few reproduce even the most basic properties of real-world networks. Here, we focus on three such properties—sparsity, small worldness, and clustering—and identify the general subclass of spatial homogeneous and heterogeneous network models that are sparse small worlds and that have nonzero clustering in the thermodynamic limit. We rely on the maximum entropy approach in which network links correspond to noninteracting fermions whose energy depends on spatial distances between nodes.

关键词： Clustering Degree correlations Degree distributions Network formation & growth

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Cortical Localization of the Auditory Temporal Response Function from MEG via Non-convex Optimization 52

Cortical Localization of the Auditory Temporal Response Func...

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52nd Asilomar Conference on Signals, systems and computers, ACSSC 2018

作者： Das, Proloy Brodbeck, Christian Simon, Jonathan Z. Babadi, Behtash Department of Electrical and Computer Engineering University of Maryland College ParkMD United States Institute for Systems Research University of Maryland College ParkMD United States Department of Biology University of Maryland College ParkMD United States

ISBN: (纸本)9781538692189

The magnetoencephalography (MEG) response to continuous auditory stimuli, such as speech, is commonly described using a linear filter, the auditory temporal response function (TRF). Though components of the sensor level TRFs have been well characterized, the underlying neural sources responsible for these components are not well understood. In this work, we provide a unified framework for determining the TRFs of neural sources directly from the MEG data, by integrating the TRF and distributed forward source models into one, and casting the joint estimation task as a Bayesian optimization problem. Though the resulting problem emerges as non-convex, we propose efficient solutions that leverage recent advances in evidence maximization. We demonstrate the effectiveness of the resulting algorithm in both simulated and experimentally recorded MEG data from humans. © 2018 IEEE.

关键词： Magnetoencephalography

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On the sum of fisher-snedecor F variates and its application to maximal-ratio combining

arXiv

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arXiv 2019年

作者： Badarneh, Osamah S. da Costa, Daniel B. Sofotasios, Paschalis C. Muhaidat, Sami Cotton, Simon L. University of Tabuk Electrical Engineering Department Tabuk Saudi Arabia Department of Computer Engineering Federal University of Ceará Sobral CE Brazil Department of Electrical and Computer Engineering Khalifa University of Science and Technology Abu Dhabi127788 United Arab Emirates Department of Electronics and Communications Engineering Tampere University of Technology Tampere33101 Finland Institute for Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom Centre for Wireless Innovation ECIT Institute Queen's University Belfast BelfastBT3 9DT United Kingdom

Capitalizing on the recently proposed Fisher-Snedecor F composite fading model, in this letter, we investigate the sum of independent but not identically distributed (i.n.i.d.) Fisher-Snedecor F variates. First, a novel closed-form expression is derived for the moment generating function of the instantaneous signal-to-noise ratio. Based on this, the corresponding probability density function and cumulative distribution function of the sum of i.n.i.d. Fisher-Snedecor F variates are derived, which are subsequently employed in the analysis of multiple branch maximal-ratio combining (MRC). Specifically, we investigate the impact of multipath and shadowed fading on the outage probability and outage capacity of MRC based receivers. In addition, we derive exact closed-form expressions for the average bit error rate of coherent binary modulation schemes followed by an asymptotic analysis which provides further insights into the effect of the system parameters on the overall performance. Importantly, it is shown that the effect of multipath fading on the system performance is more pronounced than that of shadowing. Copyright © 2019, The Authors. All rights reserved.

关键词： Probability density function

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Bio-Syncretic Light-gated Field-Effect Transistor: Fabrication and Characterization

Bio-Syncretic Light-gated Field-Effect Transistor: Fabricati...

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IEEE Conference on Nanotechnology

作者： Jia Yang Wenxue Wang Gongxin Li Guangyong Li Ning Xi Lianqing Liu State Key Laboratory of Robotics Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China State Key Laboratory of Robotics Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States Emerging Technologies Institute University of Hong Kong Pokfulam Hong Kong

Biological visual system has many intrinsic functional superiorities, such as small size, high sensitivity and no heat-sink cooling, which are hard to be completely replicated by electromechanical systems. The bio-syncretic design of sensing device with living materials may provide some functional superiorities to those conventional sensing devices, and will have great potential benefits in both biomedical and engineering applications. In this study, we developed a novel bio-syncretic light-gated field-effect transistor (FET), in which optogenetically engineered cells were used as the gate substitute for conventional gate electrode, and the exposure of the cells to light irradiation results in the variation of the transistor's channel current. The bio-syncretic transistor was fabricated by virtue of graphene film and optogenetically engineered cells, and its basic photo-responsive performance was then tested and characterized. This reference-less bio-syncretic transistor has a wide range of potential applications in photodetection and biosensing with good biocompatibility and stability.

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Toward Cloud Computing QoS Architecture:Analysis of Cloud systems and Cloud Services

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IEEE/CAA Journal of Automatica Sinica 2017年第1期4卷 6-18页

作者： Mohammad Hossein Ghahramani MengChu Zhou Chi Tin Hon IEEE Institute of Systems Engineering Macao University of Science and Technology Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology School of Management Fudan University

Cloud can be defined as a new computing paradigm that provides scalable, on-demand, and virtualized resources for users. In this style of computing, users can access a shared pool of computing resources which are provisioned with minimal management efforts of users. Yet there are some obstacles and concerns about the use of clouds. Guaranteeing quality of service QoS by service providers can be regarded as one of the main concerns for companies tending to use it. Service provisioning in clouds is based on service level agreements representing a contract negotiated between users and providers. According to this contract, if a provider cannot satisfy its agreed application requirements, it should pay penalties as compensation. In this paper, we intend to carry out a comprehensive survey on the models proposed in literature with respect to the implementation principles to address the QoS guarantee issue. © 2014 Chinese Association of Automation.

关键词： Cloud computing Distributed computer systems System of systems systems engineering Telecommunication services Web services

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Linear convergence of first- And zeroth-order primal-dual algorithms for distributed nonconvex optimization

arXiv

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arXiv 2019年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Chai, Tianyou Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Digital Futures Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers the distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of local cost functions by using local information *** first propose a distributed first-order primal-dual algorithm. We show that it converges sublinearly to a stationary point if each local cost function is smooth and linearly to a global optimum under an additional condition that the global cost function satisfies the Polyak-Lojasiewicz condition. This condition is weaker than strong convexity, which is a standard condition for proving linear convergence of distributed optimization algorithms, and the global minimizer is not necessarily unique. Motivated by the situations where the gradients are unavailable, we then propose a distributed zeroth-order algorithm, derived from the proposed distributed first-order algorithm by using a deterministic gradient estimator, and show that it has the same convergence properties as the proposed first-order algorithm under the same conditions. The theoretical results are illustrated by numerical simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Cost functions

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Learning by neural reassociation (vol 21, pg 607, 2018)

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NATURE NEUROSCIENCE 2018年第8期21卷 1138-1138页

作者： Golub, Matthew D. Sadtler, Patrick T. Oby, Emily R. Quick, Kristin M. Ryu, Stephen I. Tyler-Kabara, Elizabeth C. Batista, Aaron P. Chase, Steven M. Yu, Byron M. Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh PA USA Department of Electrical Engineering Stanford University Stanford CA USA Department of Bioengineering University of Pittsburgh Pittsburgh PA USA Systems Neuroscience Institute University of Pittsburgh Pittsburgh PA USA Department of Neurosurgery Palo Alto Medical Foundation Palo Alto CA USA Department of Physical Medicine and Rehabilitation University of Pittsburgh Pittsburgh PA USA Department of Neurological Surgery University of Pittsburgh Pittsburgh PA USA Department of Biomedical Engineering Carnegie Mellon University Pittsburgh PA USA

Behavior is driven by coordinated activity across a population of neurons. Learning requires the brain to change the neural population activity produced to achieve a given behavioral goal. How does population activity reorganize during learning? We studied intracortical population activity in the primary motor cortex of rhesus macaques during short-term learning in a brain-computer interface (BCI) task. In a BCI, the mapping between neural activity and behavior is exactly known, enabling us to rigorously define hypotheses about neural reorganization during learning. We found that changes in population activity followed a suboptimal neural strategy of reassociation: animals relied on a fixed repertoire of activity patterns and associated those patterns with different movements after learning. These results indicate that the activity patterns that a neural population can generate are even more constrained than previously thought and might explain why it is often difficult to quickly learn to a high level of proficiency.

关键词： Learning and memory Sensorimotor processing

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Publisher Correction: Twisted moiré conductive thermal metasurface

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Nature communications 2024年第1期15卷 2876页

作者： Huagen Li Dong Wang Guoqiang Xu Kaipeng Liu Tan Zhang Jiaxin Li Guangming Tao Shuihua Yang Yanghua Lu Run Hu Shisheng Lin Ying Li Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore. State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China. International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining 314400 China. Wuhan National Laboratory for Optoelectronics and State Key Laboratory of Material Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China. Smart Materials for Architecture Research Lab Innovation Center of Yangtze River Delta Zhejiang University Jiaxing 314100 China. State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan 430074 China. Chongqing 2D Materials Institute Chongqing 400015 China. State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China. eleying@***. International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining 314400 China. eleying@***. Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore. chengwei.qiu@nus.edu.sg.

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Neural signatures of motor skill in the resting brain

arXiv

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arXiv 2019年

作者： Özdenizci, Ozan Meyer, Timm Wichmann, Felix Peters, Jan Schölkopf, Bernhard Çetin, Müjdat Grosse-Wentrup, Moritz Department of Electrical and Computer Engineering Northeastern University BostonMA United States Signal Processing and Information Systems Laboratory Faculty of Engineering and Natural Sciences Sabancı University Istanbul Turkey Max Planck Institute for Intelligent Systems Tübingen Germany Faculty of Science University of Tübingen Bernstein Center for Computational Neuroscience Tübingen Tübingen Germany FG Intelligente Autonome Systeme Technische Universität Darmstadt Darmstadt Germany Department of Electrical and Computer Engineering University of Rochester RochesterNY United States Research Group Neuroinformatics Faculty of Computer Science University of Vienna Vienna Austria

Stroke-induced disturbances of large-scale cortical networks are known to be associated with the extent of motor deficits. We argue that identifying brain networks representative of motor behavior in the resting brain would provide significant insights for current neurorehabilitation approaches. Particularly, we aim to investigate the global configuration of brain rhythms and their relation to motor skill, instead of learning performance as broadly studied. We empirically approach this problem by conducting a three-dimensional physical space visuomotor learning experiment during electroencephalographic (EEG) data recordings with thirty-seven healthy participants. We demonstrate that across-subjects variations in average movement smoothness as the quantified measure of subjects' motor skills can be predicted from the global configuration of resting-state EEG alpha-rhythms (8-14 Hz) recorded prior to the experiment. Importantly, this neural signature of motor skill was found to be orthogonal to (independent of) task- as well as to learning-related changes in alpha-rhythms, which we interpret as an organizing principle of the brain. We argue that disturbances of such configurations in the brain may contribute to motor deficits in stroke, and that reconfiguring stroke patients' brain rhythms by neurofeedback may enhance post-stroke neurorehabilitation. Copyright © 2019, The Authors. All rights reserved.

关键词： Electroencephalography

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