Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ScalingNet: extracting features from raw EEG data for emotion
  recognition   Convolutional Neural Networks(CNNs) has achieved remarkable performance
breakthrough in a variety of tasks. Recently, CNNs based methods that are fed
with hand-extracted EEG features gradually produce a powerful performance on
the EEG data based emotion recognition task. In this paper, we propose a novel
convolutional layer allowing to adaptively extract effective data-driven
spectrogram-like features from raw EEG signals, which we reference as scaling
layer. Further, it leverages convolutional kernels scaled from one data-driven
pattern to exposed a frequency-like dimension to address the shortcomings of
prior methods requiring hand-extracted features or their approximations. The
proposed neural network architecture based on the scaling layer, references as
ScalingNet, has achieved the state-of-the-art result across the established
DEAP benchmark dataset.

9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics