Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Contextual Minimum-Norm Estimates (CMNE): A Deep Learning Method for
  Source Estimation in Neuronal Networks   Magnetoencephalography (MEG) and Electroencephalography (EEG) source
estimates have thus far mostly been derived sample by sample, i.e., independent
of each other in time. However, neuronal assemblies are heavily interconnected,
constraining the temporal evolution of neural activity in space as detected by
MEG and EEG. The observed neural currents are thus highly context dependent.
Here, a new method is presented which integrates predictive deep learning
networks with the Minimum-Norm Estimates (MNE) approach. Specifically, we
employ Long Short-Term Memory (LSTM) networks, a type of recurrent neural
network, for predicting brain activity. Because we use past activity (context)
in the estimation, we call our method Contextual MNE (CMNE). We demonstrate
that these contextual algorithms can be used for predicting activity based on
previous brain states and when used in conjunction with MNE, they lead to more
accurate source estimation. To evaluate the performance of CMNE, it was tested
on simulated and experimental data from human auditory evoked response
experiments.

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The categories of this paper are: eess
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The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: physics
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The categories of this paper are: acc-phys