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ردیف | عنوان | نوع |
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1 |
Graph Variational Auto-Encoder for Deriving EEG-based Graph Embedding
رمزگذار خودکار گرافیکی برای استخراج نمودار مبتنی بر EEG-2021 Graph embedding is an effective method for deriving low-dimensional representations of graph data. The power of graph deep learning methods to characterize electroencephalogram (EEG) graph embedding is still in question. We designed a novel graph variational auto-encoder (GVAE) method to extract nodal features of brain functional connections. A new decoder model for the GVAEs network is proposed, which considers the node neighborhood of the reconstructed adjacency matrix. The GVAE is applied and tested on 3 biometric databases which contain 64 to 9 channels’ EEG recordings. For all datasets, promising results with more than 95% accuracy and consider- ably low computational cost are achieved compared to state-of-the-art user identification methods. The proposed GVAE is robust to a limited number of nodes and stable to users’ task performance. Moreover, we developed a Abstract Graph embedding is an effective method for deriving low-dimensional representations of graph data. The power of graph deep learning methods to characterize electroencephalogram (EEG) graph embedding is still in ques- tion. We designed a novel graph variational auto-encoder (GVAE) method to extract nodal features of brain functional connections. A new decoder model for the GVAEs network is proposed, which considers the node neighborhood of the reconstructed adjacency matrix. The GVAE is applied and tested on 3 biometric databases which contain 64 to 9 channels’ EEG recordings. For all datasets, promising results with more than 95% accuracy and consider- ably low computational cost are achieved compared to state-of-the-art user identification methods. The proposed GVAE is robust to a limited number of nodes and stable to users’ task performance. Moreover, we developed atraditional variational auto-encoder to demonstrate that more accurate features can be obtained when observing EEG-based brain connectivity from a graph perspective. Email addresses: tina.behrouzi@mail.utoronto.ca (Tina Behrouzi),dimitris@comm.utoronto.ca (Dimitrios Hatzinakos)Preprint submitted to Pattern Recognition July 20, 2021 Keywords: Biometrics | Functional connectivity | Electroencephalogram (EEG) | Graph Variational Auto Encoder (GVAE) | Graph deep learning |
مقاله انگلیسی |
2 |
Graph-theoretical derivation of brain structural connectivity
استخراج نمودار نظری از اتصال ساختاری مغز-2020 Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense ef- forts in the field, no mathematical description has been obtained so far. Here, we present a mathematical framework based on a graph-theoretical approach that, starting from exper- imental data obtained from a few small subsets of neurons, can quantitatively explain and predict the corresponding full network properties. This model also changes the paradigm with which large-scale model networks can be built, from using probabilistic/empiric con- nections or limited data, to a process that can algorithmically generate neuronal networks connected as in the real system. Keywords: Connectome | Neuronal networks | Random graphs |
مقاله انگلیسی |
3 |
Chimera states in hybrid coupled neuron populations
حالت کیمرا در جمعیت های سلولهای عصبی جفت ترکیبی-2020 Here we study the emergence of chimera states, a recently reported phenomenon referring to the
coexistence of synchronized and unsynchronized dynamical units, in a population of Morris–Lecar
neurons which are coupled by both electrical and chemical synapses, constituting a hybrid synaptic
architecture, as in actual brain connectivity. This scheme consists of a nonlocal network where the
nearest neighbor neurons are coupled by electrical synapses, while the synapses from more distant
neurons are of the chemical type. We demonstrate that peculiar dynamical behaviors, including
chimera state and traveling wave, exist in such a hybrid coupled neural system, and analyze how the
relative abundance of chemical and electrical synapses affects the features of chimera and different
synchrony states (i.e. incoherent, traveling wave and coherent) and the regions in the space of relevant
parameters for their emergence. Additionally, we show that, when the relative population of chemical
synapses increases further, a new intriguing chaotic dynamical behavior appears above the region
for chimera states. This is characterized by the coexistence of two distinct synchronized states with
different amplitude, and an unsynchronized state, that we denote as a chaotic amplitude chimera. We
also discuss about the computational implications of such state. Keywords: Chimera state | Hybrid coupling | Chaotic population behavior |
مقاله انگلیسی |
4 |
Machine learning in resting-state fMRI analysis
یادگیری ماشین در تجزیه و تحلیل fMRI وضعیت استراحت-2019 Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic
Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine
learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning methods in restingstate
fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications
to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next,
we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subjectlevel
predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the
perspective of machine learning applications. Keywords: Machine learning | Resting-state | Functional MRI | Intrinsic networks | Brain connectivity |
مقاله انگلیسی |
5 |
High-Performance Correlation and Mapping Engine for rapid generating brain connectivity networks from big fMRI data
موتور همبستگی و نقشه برداری با سرعت بالا برای ایجاد سریع شبکه های ارتباطی مغز از داده های بزرگ fMRI-2018 Brain connectivity networks help physicians better understand the neurological effects of certain diseases
and make improved treatment options for patients. Seed-based Correlation Analysis (SCA) of Functional
Magnetic Resonance Imaging (fMRI) data has been used to create the individual brain connectivity net
works. However, an outstanding issue is the long processing time to generate full brain connectivity maps.
With close to a million individual voxels in a typical fMRI dataset, the number of calculations involved
in a voxel-by-voxel SCA becomes very high. With the emergence of the dynamic time-varying functional
connectivity analysis, the population-based studies, and the studies relying on real-time neurological
feedbacks, the need for rapid processing methods becomes even more critical. This work aims to develop
a new method which produces high-resolution brain connectivity maps rapidly. The new method accel
erates the correlation processing by using an architecture that includes clustered FPGAs and an efficient
memory pipeline, which is termed High-Performance Correlation and Mapping Engine (HPCME). The
method has been tested with datasets from the Human Connectome Project. The preliminary results
show that HPCME with four FPGAs can improve the SCA processing speed by a factor of 27 or more over
that of a PC workstation with a multicore CPU.
Keywords: Brain Functional Connectivity ، FMRI ، Seed-based Correlation Analysis ، FPGA-based Parallel Computing ، Human Connectome Project |
مقاله انگلیسی |