Abstract
In this paper, we use the DEAP database to investigate emotion recognition problem. Firstly we use data clustering technique to determine four target classes of human emotional state. Then we compare two different feature extraction methods: one is wavelet transform and another is nonlinear dynamics. Furthermore, we examine the effect of feature reduction on classification performance. Finally, we compare the performance of four different classifiers, including k-nearest neighbor, naïve Bayesian, support vector machine, and random forest. The results show the effectiveness of Kernel Spectral Regression (KSR) and random forest based classifier for emotion recognition and analysis.
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Chen, P., Zhang, J. (2017). Performance Comparison of Machine Learning Algorithms for EEG-Signal-Based Emotion Recognition. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10613. Springer, Cham. https://doi.org/10.1007/978-3-319-68600-4_25
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DOI: https://doi.org/10.1007/978-3-319-68600-4_25
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