%0 Journal Article
%T Emotional states recognition, implementing a low computational complexity strategy
%A Adrian Rodriguez Agui£żaga
%A Miguel Angel Lopez Ramirez
%J Health Informatics Journal
%@ 1741-2811
%D 2018
%R 10.1177/1460458216661862
%X 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
%K EEG
%K affective computing
%K emotions
%K neural networks
%K support vector machines
%K Brodmann regions
%K arousal
%K valence
%U https://journals.sagepub.com/doi/full/10.1177/1460458216661862