Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Convolutional Neural Network Applied to Electroencephalography: Raw
  Data vs Spectral Features   The success of deep learning in computer vision has inspired the scientific
community to explore new analysis methods. Within the field of neuroscience,
specifically in electrophysiological neuroimaging, researchers are starting to
explore leveraging deep learning to make predictions on their data without
extensive feature engineering. This paper compares deep learning using
minimally processed EEG raw data versus deep learning using EEG spectral
features using two different deep convolutional neural architectures. One of
them from Putten et al. (2018) is tailored to process raw data; the other was
derived from the VGG16 vision network (Simonyan and Zisserman, 2015) which is
designed to process EEG spectral features. We apply them to classify sex on
24-channel EEG from a large corpus of 1,574 participants. Not only do we
improve on state-of-the-art classification performance for this type of
classification problem, but we also show that in all cases, raw data
classification leads to superior performance as compared to spectral EEG
features. Interestingly we show that the neural network tailored to process EEG
spectral features has increased performance when applied to raw data
classification. Our approach suggests that the same convolutional networks used
to process EEG spectral features yield superior performance when applied to EEG
raw data.

19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio