Authors:
Juan Andrés Martínez-Escobar
;
Silvia B. González-Brambila
and
Josué Figueroa-González
Affiliation:
Universidad Autonoma Metropolitana, Mexico
Keyword(s):
Violence Images, Data Mining, EEG, Gender.
Abstract:
We collected information using the Electroencephalograph (EEG) EmotivEpoc, and the software complement of the Eye Tracking system SMI RED250mobile. As a first step, it was stored in text files, the readings of each EEG sensor during the time the presentation of 5 violent images and 5 non-violent images were observed. The database was collected with 50 volunteers, consisting of 25 men and 25 women. The database was later loaded into R, for the execution of the algorithms of data mining, K-means, K-medoids, Hierarchical Clustering, Naive Bayes, Support Vector Machines, Adaboost and Decision trees. In the clustering methods, a random clustering was presented and with little information, with the Naive Bayes, SVM and Adaboost models, a classification with a high percentage of error was obtained using the Decision Trees method, we obtained one of the worst results, with the highest error rates in the classification performed with the test data of selected method. Based on the results obta
ined, no significant difference was found in the individual's gender, which affected his reaction when viewing images with violent and non-violent content.
(More)