title: Emotional states recognition, implementing a low computational complexity strategy.
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.
authors.affiliation: Instituto Tecnológico de Tijuana, Mexico.
authors.forename: Adrian Rodriguez
authors.initials: AR
authors.lastname: Aguiñaga
authors.affiliation: Instituto Tecnológico de Tijuana, Mexico.
authors.forename: Miguel Angel Lopez
authors.initials: MAL
authors.lastname: Ramirez
mesh_terms: D004644:Emotions / Q000145:classification*; D006801:Humans; D000069550:Machine Learning / Q000639:trends*; D016571:Neural Networks, Computer; D021641:Recognition, Psychology; D060388:Support Vector Machine / Q000592:standards
keywords: Brodmann regions; EEG; affective computing; arousal; emotions; neural networks; support vector machines; valence