Towards activity recognition of learners by simple electroencephalographs

Understanding the states of learners at a lecture is expected to be useful for improving the quality of the lecture. This paper investigates the possibility of use of a simple electroencephalograph MindTune for activity recognition of a learner. The authors considered three kinds of activities for detecting states of a learner, and collected electroencephalography data with the activities by MindTune. Then, they applied K-nearest neighbor algorithm to the collected data, and the accuracy of the activity recognition was 58.2%. The result indicates a possibility of using MindTune for the activity recognition of learners.