Main Article Content
Emotions is one of the advantages given by God to human beings compared to other living creatures. Emotions have an important role in human life. Many studies have been conducted to recognize human emotions using physiological measurements, one of which is Electroencephalograph (EEG). However, the previous researches have not discussed the types of wavelet families that have the best performance and canals that are optimal in the introduction of human emotions. In this paper, the power features of several types of wavelet families, namely Daubechies, symlets, and coiflets with the Correlation Feature Selection (CFS) method to select the best features of alpha, beta, gamma and theta frequencies. According to the results, coiflet is a method of the wavelet family that has the best accuracy value in emotional recognition. The use of the CFS feature selection can improve the accuracy of the results from 81% to 93%, and the five most dominant channels in the power features of alpha and gamma band on T8, T7, C5, CP5, and TP7. Hence, it can be concluded that the temporal of the left brain is more dominant in recognition of human emotions.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
 Uskul, A. & Horn, A. (2015). Emotions and Health. International Encyclopedia of the Social & Behavioral Sciences. Elsevier. Hlm. 496–501.
 Graimann, B., Allison, B.Z. & Pfurtscheller, G. (2010). Brain-Computer Interfaces. The Frontiers Collection.
 Thejaswiini, S., Kumar Ravi., Rupali S. & Abijith, V. (2018). EEG Based Emotion Recognition Using Wavelets and Neural Networks Classifier. SpringerBriefs in Forensic and Medical Bioinformatics.
 Zheng, W.L. & Lu, B.L. (2016). Identifying Stable Patterns over Time for Emotion Recognition from EEG. IEEE Transactions on Affective Computing.
 Zheng, W.L. & Lu, B.L. (2015). Investigating Critical Frequency Bands and Channels for EEG-based Emotion Recognition with Deep Neural Networks. IEEE Transactions on Autonomous Mental Development.
 Jenke, R., Peer, A. & Buss, M. (2014). Feature Extraction and Selection for Emotion Recognition from EEG. IEEE Transactions on Affective computing.Vol. 5.
 Qiao, F. (2005). Introduction to Wavelet. Workshop 118 on WaveleteApplication in Transportation Engineering.
 Hall, M. (1999). Correlation-based Feature Selection for Machine Learning. Department of Computer Science The University of Waikato. Newzealand.
 Hardle, W. & Simar, L. (2015). Applied Multivariate Statistical Analysis. Springer Berlin Heidelberg. Berlin.
 Lin, C.T., Tsai, S.F., Lee, H.C., Huang, H.L., Ho, S.Y. & Ko, L.W. (2012). Motion sickness estimation system, Proc. Int. Jt. Conf. Neural Networks, pp. 10–15.
 Ko, L.W., Lee, H.C., Tsai, S.F. & Shih, T.C. (2013). EEG-based Motion Sickness Classification system with Genetic Feature Selection, pp. 158–164.
 Prasetyo, E. (2012). Data Mining: Konsep dan Aplikasi menggunakan Matlab, 1 ed. Yogyakarta: Andi Offset.
 Sokolova, M. & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. Vol. 45. No. 4. hlm. 427–437.
 Yohannes, R. & Huang, G.B. (2012). Discrete wavelet transform coefficients for emotion recognition from EEG signals. 34th Annual International Conference of the IEEE EMBS.
 Buck, R. (1980). Left and right hemisphere brain functions and symbolic vs spontaneous communication processes. Annual Meeting of the speech communication association
 Goghari, V., MacDonald, A. & Sponheim, S. (2010). Temporal Lobe Structures and Facial Emotion Recognition in Schizophrenia Patients and Nonpsychotic Relatives. Schizophrenia Bulletin, 37(6), 1281–1294.