Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Emotion recognition from EEG using higher order crossings
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
IEEE Transactions on Affective Computing
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
IEEE Transactions on Affective Computing
Performance surfaces of a single-layer perceptron
IEEE Transactions on Neural Networks
IEEE Transactions on Information Technology in Biomedicine
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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.