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

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.