Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Emotion Detection Using Noninvasive Low Cost Sensors   Emotion recognition from biometrics is relevant to a wide range of
application domains, including healthcare. Existing approaches usually adopt
multi-electrodes sensors that could be expensive or uncomfortable to be used in
real-life situations. In this study, we investigate whether we can reliably
recognize high vs. low emotional valence and arousal by relying on noninvasive
low cost EEG, EMG, and GSR sensors. We report the results of an empirical study
involving 19 subjects. We achieve state-of-the- art classification performance
for both valence and arousal even in a cross-subject classification setting,
which eliminates the need for individual training and tuning of classification
models.

7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics