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Analyzing Brain Waves for Activity Recognition of Learners

Abstract : Understanding the states of learners at a lecture is expected to be useful for improving the quality of the lecture. This paper is trying to recognize the activities of learners by their brain wave data for estimating the states. In analyses on brain wave data, generally, some particular bands such as α and β are considered as the features. The authors considered other bands of higher and lower frequencies to compensate for the coarseness of simple electroencephalographs. They conducted an experiment of recognizing two activities of five subjects with the brain wave data captured by a simple electroencephalograph. They applied support vector machine to 8-dimensional vectors which correspond to eight bands on the brain wave data. The results show that considering multiple bands yielded high accuracy compared with the usual features.
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Hiromichi Abe, Kazuya Kinoshita, Kensuke Baba, Shigeru Takano, Kazuaki Murakami. Analyzing Brain Waves for Activity Recognition of Learners. 3rd International Conference on Information and Communication Technology-EurAsia (ICT-EURASIA) and 9th International Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS), Oct 2015, Daejon, South Korea. pp.64-73, ⟨10.1007/978-3-319-24315-3_7⟩. ⟨hal-01466252⟩

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