Decoding hand movement velocity from electroencephalogram signals during a drawing task

<p>Abstract</p> <p>Background</p> <p>Decoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG). Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear.</p> <p>Methods</p> <p>Here we collected whole-scalp EEG data of five subjects during a sequential 4-directional drawing task, and employed spatial filtering algorithms to extract the amplitude and power features of EEG in multiple frequency bands. From these features, we reconstructed hand movement velocity by Kalman filtering and a smoothing algorithm.</p> <p>Results</p> <p>The average Pearson correlation coefficients between the measured and the decoded velocities are 0.37 for the horizontal dimension and 0.24 for the vertical dimension. The channels on motor, posterior parietal and occipital areas are most involved for the decoding of hand velocity. By comparing the decoding performance of the features from different frequency bands, we found that not only slow potentials in 0.1-4 Hz band but also oscillatory rhythms in 24-28 Hz band may carry the information of hand velocity.</p> <p>Conclusions</p> <p>These results provide another support to neural control of motor prosthesis based on EEG signals and proper decoding methods.</p

Voxel selection in fMRI data analysis based on sparse representation

Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. In each iteration of the algorithm, a linear programming problem is solved and a sparse weight vector is subsequently obtained. The final weight vector is the mean of those obtained in all iterations. The characteristics of our algorithm are as follows: 1) the weight vector (output) is sparse; 2) the magnitude of each entry of the weight vector represents the significance of its corresponding variable or feature in a classification or regression problem; and 3) due to the convergence of this algorithm, a stable weight vector is obtained. To demonstrate the validity of our algorithm and illustrate its application, we apply the algorithm to the Pittsburgh Brain Activity Interpretation Competition 2007 functional fMRI dataset for selecting the voxels, which are the most relevant to the tasks of the subjects. Based on this dataset, the aforementioned characteristics of our algorithm are analyzed, and a comparison between our method with the univariate general-linear-model-based statistical parametric mapping is performed. Using our method, a combination of voxels are selected based on the principle of effective/sparse representation of a task. Data analysis results in this paper show that this combination of voxels is suitable for decoding tasks and demonstrate the effectiveness of our method

A large-scale dataset for end-to-end table recognition in the wild

Table recognition (TR) is one of the research hotspots in pattern recognition, which aims to extract information from tables in an image. Common table recognition tasks include table detection (TD), table structure recognition (TSR) and table content recognition (TCR). TD is to locate tables in the image, TCR recognizes text content, and TSR recognizes spatial ogical structure. Currently, the end-to-end TR in real scenarios, accomplishing the three sub-tasks simultaneously, is yet an unexplored research area. One major factor that inhibits researchers is the lack of a benchmark dataset. To this end, we propose a new large-scale dataset named Table Recognition Set (TabRecSet) with diverse table forms sourcing from multiple scenarios in the wild, providing complete annotation dedicated to end-to-end TR research. It is the largest and first bi-lingual dataset for end-to-end TR, with 38.1K tables in which 20.4K are in English\, and 17.7K are in Chinese. The samples have diverse forms, such as the border-complete and -incomplete table, regular and irregular table (rotated, distorted, etc.). The scenarios are multiple in the wild, varying from scanned to camera-taken images, documents to Excel tables, educational test papers to financial invoices. The annotations are complete, consisting of the table body spatial annotation, cell spatial logical annotation and text content for TD, TSR and TCR, respectively. The spatial annotation utilizes the polygon instead of the bounding box or quadrilateral adopted by most datasets. The polygon spatial annotation is more suitable for irregular tables that are common in wild scenarios. Additionally, we propose a visualized and interactive annotation tool named TableMe to improve the efficiency and quality of table annotation

Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network

Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this paper a novel data-synthesized spatio-temporally convolutional encoder-decoder network method termed DST-CedNet is proposed for ESI. DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a convolutional encoder-decoder network (CedNet) to learn a robust mapping from the measured electroencephalography/magnetoencephalography (E/MEG) signals to the brain activity. In particular, by incorporating prior knowledge regarding dynamical brain activities, a novel data synthesis strategy is devised to generate large-scale samples for effectively training CedNet. This stands in contrast to traditional ESI methods where the prior information is often enforced via constraints primarily aimed for mathematical convenience. Extensive numerical experiments as well as analysis of a real MEG and Epilepsy EEG dataset demonstrate that DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.Comment: 15 pages, 14 figures, and journa

Identification of antibodies with non-overlapping neutralization sites that target coxsackievirus A16

手足口病（Hand, Foot and Mouth Disease，HFMD）是一种由人肠道病毒引起的全球性传染病，主要发生于5岁以下的婴幼儿。2月5日，我校夏宁邵教授团队在《细胞》子刊《细胞•宿主与微生物》（Cell Host & Microbe）上在线发表题为“Identification of antibodies with non-overlapping neutralization sites that target coxsackievirus A16”的研究论文。该研究首次揭示了手足口病主要病原体柯萨奇病毒A组16型（CVA16）三种衣壳颗粒形式与三种不同类型的治疗性中和抗体的全面相互作用细节和非重叠的中和表位结构信息，阐明了CVA16成熟颗粒是疫苗候选主要保护性免疫原的理论基础，建立了可指导疫苗研制的免疫原特异检测方法，为CVA16疫苗及抗病毒药物研究提供关键基础。我校夏宁邵教授、李少伟教授、程通副教授和美国加州大学洛杉矶分校纳米系统研究所Z. Hong Zhou（周正洪）教授为该论文的共同通讯作者。我校博士生何茂洲、徐龙发博士后、郑清炳高级工程师、博士生朱瑞和尹志超为该论文共同第一作者。【Abstract】Hand, foot, and mouth disease is a common childhood illness primarily caused by coxsackievirus A16 (CVA16), for which there are no current vaccines or treatments. We identify three CVA16-specific neutralizing monoclonal antibodies (nAbs) with therapeutic potential: 18A7, 14B10, and NA9D7. We present atomic structures of these nAbs bound to all three viral particle forms—the mature virion, A-particle, and empty particle—and show that each Fab can simultaneously occupy the mature virion. Additionally, 14B10 or NA9D7 provide 100% protection against lethal CVA16 infection in a neonatal mouse model. 18A7 binds to a non-conserved epitope present in all three particles, whereas 14B10 and NA9D7 recognize broad protective epitopes but only bind the mature virion. NA9D7 targets an immunodominant site, which may overlap the receptor-binding site. These findings indicate that CVA16 vaccines should be based on mature virions and that these antibodies could be used to discriminate optimal virion-based immunogens.This work was supported by grants from the Major Program of National Natural Science Foundation of China ( 81991490 ), the National Science and Technology Major Projects for Major New Drugs Innovation and Development ( 2018ZX09711003-005-003 ), the National Science and Technology Major Project of Infectious Diseases ( 2017ZX10304402-002-003 ), the National Natural Science Foundation of China ( 31670933 and 81801646 ), the China Postdoctoral Science Foundation ( 2018M640599 and 2019T120557 ), the Principal Foundation of Xiamen University ( 20720190117 ), and the National Institutes of Health ( R37-GM33050 , GM071940 , DE025567 , and AI094386 ). 该研究获得了国家自然科学基金、新药创制国家科技重大专项、传染病防治国家科技重大专项和美国国立卫生研究院基金的资助