Selection of learning algorithm for musical tone stimulated wavelet de-noised EEG signal classification

• Main Authors: Navea, Roy Francis R., Dadios, Elmer Jose P.
• Format: text
• Published: Animo Repository 2017
• Subjects: Electroencephalography; Machine learning; Hymn tunes; Electrical and Computer Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2706
• Institution: De La Salle University

The task of classifying EEG signals pose a challenge in the selection of which learning algorithm is best to provide higher classification accuracy. In this study, five well-known learning algorithms used in data mining were utilized. The task is to classify musical tone stimulated wavelet de-noised EEG signals. Classification tasks include whether the EEG signal is tone stimulated or not, and whether the EEG signal is stimulated by either the C, F or G tone. Results show higher correct classification instances (CCI) percentages and accuracies in the first classification task using the J48 decision tree as the learning algorithm. For the second classification task, the k-nn learning algorithm outruns the other classifiers but gave low accuracy and low correct classification percentage. The possibility of increasing the performance was explored by increasing the k (number of neighbors). With the increment, its produced directly proportionate in accuracy and correct classification percentage within a certain value of k. A larger k value will reduce the accuracy and the correct classification percentages.