A low computational complexity strategy using EEG signals and machine learning can achieve up to 90.2% accuracy in recognizing emotional states.
Should not yet alter clinical practice; leaves open prospective validation of EEG-ML emotion recognition.
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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Adrián Rodríguez Aguiñaga (2016) studied this question.
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