Open AccessEmotion classification from EEG signals using wearable sensors: pilot test
Author(s) -
Alejandro Jarillo-Silva,
Víctor A. Gomez-Perez,
Eduardo Alejandro Escotto Córdova,
Omar A. Domínguez-Ramírez
Publication year - 2020
Publication title -
ecorfan journal bolivia
Language(s) - English
Resource type - Journals
ISSN - 2410-4191
DOI - 10.35429/ejb.2020.12.7.1.9
Subject(s) - linear discriminant analysis , support vector machine , electroencephalography , artificial intelligence , pattern recognition (psychology) , computer science , approximate entropy , wearable computer , classifier (uml) , speech recognition , entropy (arrow of time) , emotion classification , discriminant , quadratic classifier , psychology , physics , quantum mechanics , psychiatry , embedded system
The objective of this work is to present a procedure for the classification of basic emotions based on the analysis of EEG signals (electroencephalogram). For this case, 25 subjects were stimulated, of whom 17 were men and 9 women between 20 and 35 years of age. The stimulus to induce positive, negative and neutral emotions with a certain level of excitation (activation) was a set of video clips previously evaluated. The processed and analyzed signals belong to the gamma and beta frequency bands of the F3, F4, P7, P8, T7, T8, O1 and O2 electrodes. The characteristic variables with the best result are the entropy of each band of each electrode. The cross validation algorithms are applied and later the main component analysis algorithm. Finally, four classifier algorithms are used: classifier trees, Support- Vector-Machine (SVM), Linear-Discriminant-Analysis (LDA) and k-Nearest-Neighbors (KNN). The results confirm that by carrying out the proposed procedure, the EEG signals contain enough information to allow the recognition of basic emotions.