Mind Wandering (MW) is the recurrent occurrence in which our mind gets disengaged from the immediate task and focused on internal trains of thought. MW can have both good as well as detrimental effects. Hence, it is crucial to measure MW. This interesting phenomenon and part of our daily life can be effectively measured using EEG signals. Several techniques that have been used to predict MW. However, literature shows that there are still chances of further improvement in this field. 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 EEG channels. We evaluate the performance of our proposed framework using 19 sessions of two subjects. The accuracy of the proposed framework is higher than the other researches under this field that indicates the superiority of our proposed framework.
|Title of host publication||IEEE Region 10 Symposium (TENSYMP) 2020|
|Publication status||Accepted/In press - 28 Apr 2020|
Tasika, N. J., Haque, M. H., Rimo, M. B., Haque, M. A., Alam, S., Tamanna, T., Rahman, M. A., & Parvez, M. Z. (Accepted/In press). A Framework for Mind Wandering Detection Using EEG Signals. In IEEE Region 10 Symposium (TENSYMP) 2020 IEEE Xplore.