Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG
  Signal Classification   In this article, we propose a sparse spectra graph convolutional network
(SSGCNet) for solving Epileptic EEG signal classification problems. The aim is
to achieve a lightweight deep learning model without losing model
classification accuracy. We propose a weighted neighborhood field graph (WNFG)
to represent EEG signals, which reduces the redundant edges between graph
nodes. WNFG has lower time complexity and memory usage than the conventional
solutions. Using the graph representation, the sequential graph convolutional
network is based on a combination of sparse weight pruning technique and the
alternating direction method of multipliers (ADMM). Our approach can reduce
computation complexity without effect on classification accuracy. We also
present convergence results for the proposed approach. The performance of the
approach is illustrated in public and clinical-real datasets. Compared with the
existing literature, our WNFG of EEG signals achieves up to 10 times of
redundant edge reduction, and our approach achieves up to 97 times of model
pruning without loss of classification accuracy.

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The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
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The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
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The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
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The categories of this paper are: cs
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
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The categories of this paper are: math
32
The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas