Comput. Biol. Med. - EEG-based emotion estimation using Bayesian weighted-log-posterior function and perceptron convergence algorithm.

Tópicos

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Resumo

This paper addresses the emotion recognition problem from electroencephalogram signals, in which emotions are represented on the valence and arousal dimensions. Fast Fourier transform analysis is used to extract features and the feature selection based on Pearson correlation coefficient is applied. This paper proposes a probabilistic classifier based on Bayes' theorem and a supervised learning using a perceptron convergence algorithm. To verify the proposed methodology, we use an open database. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the average accuracy of the valence and arousal estimation is 70.9% and 70.1%, respectively. For the three-level class case, the average accuracy is 55.4% and 55.2%, respectively.

Resumo Limpo

paper address emot recognit problem electroencephalogram signal emot repres valenc arous dimens fast fourier transform analysi use extract featur featur select base pearson correl coeffici appli paper propos probabilist classifi base bay theorem supervis learn use perceptron converg algorithm verifi propos methodolog use open databas emot defin twolevel class threelevel class valenc arous dimens twolevel class case averag accuraci valenc arous estim respect threelevel class case averag accuraci respect

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