Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simultaneously exploring multi-scale and asymmetric EEG features for
  emotion recognition   In recent years, emotion recognition based on electroencephalography (EEG)
has received growing interest in the brain-computer interaction (BCI) field.
The neuroscience researches indicate that the left and right brain hemispheres
demonstrate differences under different emotional activities, which could be an
important principle for designing deep learning (DL) model for emotion
recognition. Besides, owing to the nonstationarity of EEG signals, using
convolution kernel with a single size may not sufficiently extract the useful
features. Based on these two angles, we proposed a model termed Multi-Scales
Bi-hemispheric Asymmetric Model (MSBAM) based on convolutional neural network
(CNN) structure. Evaluated on the public DEAP dataset, the MSBAM achieved over
96% accuracy of binary classification task in the four dimensions, i.e,
arousal, valence, dominance and liking. This study further demonstrated the
promising potential to design the DL model from the multi-scale characteristics
of the EEG data and the neural mechanisms from the emotion cognition.

2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat