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ISSN: 2333-9721
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-  2018 

Emotional states recognition, implementing a low computational complexity strategy

DOI: 10.1177/1460458216661862

Keywords: EEG,affective computing,emotions,neural networks,support vector machines,Brodmann regions,arousal,valence

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Abstract:

This article describes a methodology to recognize emotional states through an electroencephalography signals analysis, developed with the premise of reducing the computational burden that is associated with it, implementing a strategy that reduces the amount of data that must be processed by establishing a relationship between electrodes and Brodmann regions, so as to discard electrodes that do not provide relevant information to the identification process. Also some design suggestions to carry out a pattern recognition process by low computational complexity neural networks and support vector machines are presented, which obtain up to a 90.2% mean recognition rate

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