3,532 research outputs found
Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis
Motor imagery (MI) classification is one of the most widely-concern research
topics in Electroencephalography (EEG)-based brain-computer interfaces (BCIs)
with extensive industry value. The MI-EEG classifiers' tendency has changed
fundamentally over the past twenty years, while classifiers' performance is
gradually increasing. In particular, owing to the need for characterizing
signals' non-Euclidean inherence, the first geometric deep learning (GDL)
framework, Tensor-CSPNet, has recently emerged in the BCI study. In essence,
Tensor-CSPNet is a deep learning-based classifier on the second-order
statistics of EEGs. In contrast to the first-order statistics, using these
second-order statistics is the classical treatment of EEG signals, and the
discriminative information contained in these second-order statistics is
adequate for MI-EEG classification. In this study, we present another GDL
classifier for MI-EEG classification called Graph-CSPNet, using graph-based
techniques to simultaneously characterize the EEG signals in both the time and
frequency domains. It is realized from the perspective of the time-frequency
analysis that profoundly influences signal processing and BCI studies. Contrary
to Tensor-CSPNet, the architecture of Graph-CSPNet is further simplified with
more flexibility to cope with variable time-frequency resolution for signal
segmentation to capture the localized fluctuations. In the experiments,
Graph-CSPNet is evaluated on subject-specific scenarios from two well-used
MI-EEG datasets and produces near-optimal classification accuracies.Comment: 16 pages, 5 figures, 9 Tables; This work has been submitted to the
IEEE for possible publication. Copyright may be transferred without notice,
after which this version may no longer be accessibl
A structure-preserving integrator for incompressible finite elastodynamics based on a grad-div stabilized mixed formulation with particular emphasis on stretch-based material models
We present a structure-preserving scheme based on a recently-proposed mixed
formulation for incompressible hyperelasticity formulated in principal
stretches. Although there exist Hamiltonians introduced for
quasi-incompressible elastodynamics based on different variational
formulations, the one in the fully incompressible regime has yet been
identified in the literature. The adopted mixed formulation naturally provides
a new Hamiltonian for fully incompressible elastodynamics. Invoking the
discrete gradient formula, we are able to design fully-discrete schemes that
preserve the Hamiltonian and momenta. The scaled mid-point formula, another
popular option for constructing algorithmic stresses, is analyzed and
demonstrated to be non-robust numerically. The generalized Taylor-Hood element
based on the spline technology conveniently provides a higher-order, robust,
and inf-sup stable spatial discretization option for finite strain analysis. To
enhance the element performance in volume conservation, the grad-div
stabilization, a technique initially developed in computational fluid dynamics,
is introduced here for elastodynamics. It is shown that the stabilization term
does not impose additional restrictions for the algorithmic stress to respect
the invariants, leading to an energy-decaying and momentum-conserving fully
discrete scheme. A set of numerical examples is provided to justify the claimed
properties. The grad-div stabilization is found to enhance the discrete mass
conservation effectively. Furthermore, in contrast to conventional algorithms
based on Cardano's formula and perturbation techniques, the spectral
decomposition algorithm developed by Scherzinger and Dohrmann is robust and
accurate to ensure the discrete conservation laws and is thus recommended for
stretch-based material modeling
Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance
The efficacy of Electroencephalogram (EEG) classifiers can be augmented by
increasing the quantity of available data. In the case of geometric deep
learning classifiers, the input consists of spatial covariance matrices derived
from EEGs. In order to synthesize these spatial covariance matrices and
facilitate future improvements of geometric deep learning classifiers, we
propose a generative modeling technique based on state-of-the-art score-based
models. The quality of generated samples is evaluated through visual and
quantitative assessments using a left/right-hand-movement motor imagery
dataset. The exceptional pixel-level resolution of these generative samples
highlights the formidable capacity of score-based generative modeling.
Additionally, the center (Frechet mean) of the generated samples aligns with
neurophysiological evidence that event-related desynchronization and
synchronization occur on electrodes C3 and C4 within the Mu and Beta frequency
bands during motor imagery processing. The quantitative evaluation revealed
that 84.3% of the generated samples could be accurately predicted by a
pre-trained classifier and an improvement of up to 8.7% in the average accuracy
over ten runs for a specific test subject in a holdout experiment.Comment: 7 pages, 4 figures; This work has been accepted by the 2023 45th
Annual International Conference of the IEEE Engineering in Medicine & Biology
Conference (IEEE EMBC 2023'). Copyright will be transferred without notice,
after which this version may no longer be accessibl
Characterization of the GXXXG motif in the first transmembrane segment of Japanese encephalitis virus precursor membrane (prM) protein
The interaction between prM and E proteins in flavivirus-infected cells is a major driving force for the assembly of flavivirus particles. We used site-directed mutagenesis to study the potential role of the transmembrane domains of the prM proteins of Japanese encephalitis virus (JEV) in prM-E heterodimerization as well as subviral particle formation. Alanine insertion scanning mutagenesis within the GXXXG motif in the first transmembrane segment of JEV prM protein affected the prM-E heterodimerization; its specificity was confirmed by replacing the two glycines of the GXXXG motif with alanine, leucine and valine. The GXXXG motif was found to be conserved in the JEV serocomplex viruses but not other flavivirus groups. These mutants with alanine inserted in the two prM transmembrane segments all impaired subviral particle formation in cell cultures. The prM transmembrane domains of JEV may play importation roles in prM-E heterodimerization and viral particle assembly
GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection
With the rapid development of deep generative models (such as Generative
Adversarial Networks and Diffusion models), AI-synthesized images are now of
such high quality that humans can hardly distinguish them from pristine ones.
Although existing detection methods have shown high performance in specific
evaluation settings, e.g., on images from seen models or on images without
real-world post-processing, they tend to suffer serious performance degradation
in real-world scenarios where testing images can be generated by more powerful
generation models or combined with various post-processing operations. To
address this issue, we propose a Global and Local Feature Fusion (GLFF)
framework to learn rich and discriminative representations by combining
multi-scale global features from the whole image with refined local features
from informative patches for AI synthesized image detection. GLFF fuses
information from two branches: the global branch to extract multi-scale
semantic features and the local branch to select informative patches for
detailed local artifacts extraction. Due to the lack of a synthesized image
dataset simulating real-world applications for evaluation, we further create a
challenging fake image dataset, named DeepFakeFaceForensics (DF 3 ), which
contains 6 state-of-the-art generation models and a variety of post-processing
techniques to approach the real-world scenarios. Experimental results
demonstrate the superiority of our method to the state-of-the-art methods on
the proposed DF 3 dataset and three other open-source datasets.Comment: 13 pages, 6 figures, 8 table
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