Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A large-scale evaluation framework for EEG deep learning architectures   EEG is the most common signal source for noninvasive BCI applications. For
such applications, the EEG signal needs to be decoded and translated into
appropriate actions. A recently emerging EEG decoding approach is deep learning
with Convolutional or Recurrent Neural Networks (CNNs, RNNs) with many
different architectures already published. Here we present a novel framework
for the large-scale evaluation of different deep-learning architectures on
different EEG datasets. This framework comprises (i) a collection of EEG
datasets currently including 100 examples (recording sessions) from six
different classification problems, (ii) a collection of different EEG decoding
algorithms, and (iii) a wrapper linking the decoders to the data as well as
handling structured documentation of all settings and (hyper-) parameters and
statistics, designed to ensure transparency and reproducibility. As an
applications example we used our framework by comparing three publicly
available CNN architectures: the Braindecode Deep4 ConvNet, Braindecode Shallow
ConvNet, and two versions of EEGNet. We also show how our framework can be used
to study similarities and differences in the performance of different decoding
methods across tasks. We argue that the deep learning EEG framework as
described here could help to tap the full potential of deep learning for BCI
applications.

28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci