![](bg8.png)
ognition rates above 70% are achieved when classifying positive and negative emo-
tions using LOOCV estimates with a k-NN Classifier. For future work, classification
based on criterions such as the emotion valence and arousal will be used; other emo-
tion elicitation techniques such as pictures, sounds, games can also be inserted and
tested in the developed Web application; acquiring new electrophysiological data and
extend our current database is also a future goal.
Acknowledgements
This work was partially supported by the National Strategic Reference Framework
(NSRF-QREN) programme under contract no. 3475 (Affective Mouse), and partially
developed under the grant SFRH/BD/65248/2009 from Fundação para a Ciência e
Tecnologia (FCT), whose support the authors gratefully acknowledge.
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