**Affective Valence Detection from EEG Signals Using Wrapper Methods Affective Valence Detection from EEG Signals Using Wrapper Methods**

Antonio R. Hidalgo‐Muñoz, Míriam M. López, Isabel M. Santos, Manuel Vázquez‐Marrufo, Elmar W. Lang and Ana M. Tomé Antonio R. Hidalgo‐Muñoz, Míriam M. López, Isabel M. Santos, Manuel Vázquez‐Marrufo, Elmar W. Lang and Ana M. Tomé Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/66667

#### **Abstract**

[40] S. Yang and B. Bhanu, "Understanding discrete facial expressions in video using an emotion avatar image," *IEEE Trans. Syst. Man, Cybern. Part B Cybern.*, vol. 42, no. 4, pp.

[41] A. C. Cruz, B. Bhanu, and N. Thakoor, "Facial emotion recognition with expression

[42] E. Cambria, G.‐B. Huang, L. L. C. Kasun, H. Zhou, C. M. Vong, J. Lin, J. Yin, Z. Cai, Q. Liu, K. Li, V. C. M. Leung, L. Feng, Y.‐S. Ong, M.‐H. Lim, A. Akusok, A. Lendasse, F. Corona, R. Nian, Y. Miche, P. Gastaldo, R. Zunino, S. Decherchi, X. Yang, K. Mao, B.‐S. Oh, J. Jeon, K.‐A. Toh, A. B. J. Teoh, J. Kim, H. Yu, Y. Chen, and J. Liu, "Extreme learn‐ ing machines [trends & controversies]," *IEEE Intell. Syst.*, vol. 28, no. 6, pp. 30–59, 2013.

[43] C. Liu, J. Yuen, and A. Torralba, "Sift flow: Dense correspondence across scenes and its applications," *IEEE Trans. Pattern Anal. Mach. Intell.*, vol. 33, no. 5, pp. 15–49, 2015. [44] S. Baker and I. Matthews, "Lucas‐Kanade 20 years on: A unifying framework," *Int. J.* 

[45] C. Chow and C. Liu, "Discrete probability distributions with dependence trees," *IEEE* 

[46] A. C. Cruz, B. Bhanu, and N. S. Thakoor, "Background suppressing Gabor energy filter‐

[47] A. Cruz, B. Bhanu, and N. Thakoor, "Vision and attention theory based sampling for continuous facial emotion recognition," *IEEE Trans. Affect. Comput.*, vol. PP, no. 99,

[48] C.‐C. Chang and C.‐J. Lin, "LIBSVM," *ACM Trans. Intell. Syst. Technol.*, vol. 2, no. 3,

energy," *ACM Int'l. Conf. Multimodal Interact. Work.*, pp. 457–464, 2012.

*Comput. Vis.*, vol. 56, no. 3, pp. 221–255, 2004.

22 Emotion and Attention Recognition Based on Biological Signals and Images

*Trans. Inf. Theory*, vol. 14, no. 3, pp. 462–467, 1968.

ing," *Pattern Recognit. Lett.*, vol. 52, pp. 40–47, 2015.

980–992, 2012.

pp. 1–1, 2014.

pp. 1–27, 2011.

In this work, a novel valence recognition system applied to EEG signals is presented. It consists of a feature extraction block followed by a wrapper classification algorithm. The proposed feature extraction method is based on measures of relative energies computed in short‐time intervals and certain frequency bands of EEG signal segments time‐locked to the stimuli presentation. These measures represent event‐related desynchronization/ synchronization of underlying brain neural networks. The subsequent feature selection and classification steps comprise a wrapper technique based on two different classifica‐ tion approaches: an ensemble classifier, i.e., a random forest of classification trees and a support vector machine algorithm. Applying a proper importance measure from the clas‐ sifiers, the feature elimination has been used to identify the most relevant features of the decision making both for intrasubject and intersubject settings, using single trial signals and ensemble averaged signals, respectively. The proposed methodologies allowed us to identify a frontal region and a beta band as the most relevant characteristics, extracted from the electrical brain activity, in order to determine the affective valence elicited by visual stimuli.

**Keywords:** EEG, random forest, SVM, wrapper method
