Parvez, Mohammad ZavidRahman, Md AnisurTasika, Nadia JebinAlam, SalwaRimo, Mohsena BegumHaque, Mohtasim AlHaque, Mohammad Hasibul2020-03-082020-03-0820202020-01ID 15301106ID 15101071ID 15301009ID 13301082ID 13301047http://hdl.handle.net/10361/13835Cataloged from PDF version of thesis.Includes bibliographical references (pages 22-27).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2020.Mind Wandering (MW) is the recurrent occurrence in which our mind gets disengaged from the immediate task and focused on internal trains of thought. In terms of intelligent interfaces MW can both have good as well as detrimental e ects; hence it is crucial to measure MW. This interesting phenomenon and part of our daily life can be e ectively measured using electroencephalogram (EEG) Signals. There are several techniques that have been used to predict MW however; literature review shows that there are still chances of further improvement in this eld. Therefore, in this paper we proposed a framework based on data mining and machine learning to detect MW using EEG signals. In our framework, we extracted a number of features from 64 internal EEG channels. We evaluate the performance of our proposed framework using 2 subjects with total of 19 sessions. The prediction accuracy of the proposed framework is higher than the other researches under this field that indicates the superiority of our proposed framework and efficiency of the data.27 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Electroencephalogram (EEG)MindWandering (MW)Support Vector Machine (SVM)Brain-computer interfacesElectroencephalographyThesis