Using a simple electroencephalograph for activity recognition of learners

Understanding the states or emotions of learners at a lecture is expected to be useful for improving lecture quality. In our work, we tried to recognize two activities of learners by using their brain wave data to estimate their states. While existing analyses of brain wave data for activity recognition used standard bands such as α and β as features, we used other bands with higher and lower frequencies to compensate for the coarseness of simple electroencephalographs. We conducted experiments on recognizing two activities performed by six subjects with brain wave data captured by a simple electroencephalograph. We applied a support vector machine to 8-dimensional vectors corresponding to eight bands of the brain wave data. The results show that using the eight bands yielded higher accuracy compared than that obtained with the standard features based on at most four bands.