Analyzing brain waves for activity recognition of learners

Hiromichi Abe; Kazuya Kinoshita; Kensuke Baba; Shigeru Takano; Kazuaki Murakami

doi:10.1007/978-3-319-24315-3_7

Analyzing brain waves for activity recognition of learners

Hiromichi Abe, Kazuya Kinoshita, Kensuke Baba, Shigeru Takano, Kazuaki Murakami

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Information and Communication Technology - 3rd IFIP TC 5/8 International Conference, ICT-EurAsia 2015 and 9th IFIP WG 8.9 Working Conference, CONFENIS 2015 Held as Part of WCC 2015, Proceedings
Editors	Ilsun You, Li Da Xu, Erich Neuhold, A. Min Tjoa, Ismail Khalil
Publisher	Springer Verlag
Pages	64-73
Number of pages	10
ISBN (Print)	9783319243146
DOIs	https://doi.org/10.1007/978-3-319-24315-3_7
Publication status	Published - 2015
Event	3rd IFIP TC 5/8 International Conference on Information and Communication Technology, ICT-EurAsia 2015 and 9th IFIP WG 8.9 Working Conference on Research and Practical Issues of Enterprise Information Systems, CONFENIS 2015 - Daejeon, Korea, Republic of Duration: Oct 4 2015 → Oct 7 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9357
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd IFIP TC 5/8 International Conference on Information and Communication Technology, ICT-EurAsia 2015 and 9th IFIP WG 8.9 Working Conference on Research and Practical Issues of Enterprise Information Systems, CONFENIS 2015
Country/Territory	Korea, Republic of
City	Daejeon
Period	10/4/15 → 10/7/15

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-24315-3_7

Cite this

Abe, H., Kinoshita, K., Baba, K., Takano, S., & Murakami, K. (2015). Analyzing brain waves for activity recognition of learners. In I. You, L. D. Xu, E. Neuhold, A. M. Tjoa, & I. Khalil (Eds.), Information and Communication Technology - 3rd IFIP TC 5/8 International Conference, ICT-EurAsia 2015 and 9th IFIP WG 8.9 Working Conference, CONFENIS 2015 Held as Part of WCC 2015, Proceedings (pp. 64-73). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9357). Springer Verlag. https://doi.org/10.1007/978-3-319-24315-3_7

Analyzing brain waves for activity recognition of learners. / Abe, Hiromichi; Kinoshita, Kazuya; Baba, Kensuke et al.
Information and Communication Technology - 3rd IFIP TC 5/8 International Conference, ICT-EurAsia 2015 and 9th IFIP WG 8.9 Working Conference, CONFENIS 2015 Held as Part of WCC 2015, Proceedings. ed. / Ilsun You; Li Da Xu; Erich Neuhold; A. Min Tjoa; Ismail Khalil. Springer Verlag, 2015. p. 64-73 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9357).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abe, H, Kinoshita, K, Baba, K, Takano, S & Murakami, K 2015, Analyzing brain waves for activity recognition of learners. in I You, LD Xu, E Neuhold, AM Tjoa & I Khalil (eds), Information and Communication Technology - 3rd IFIP TC 5/8 International Conference, ICT-EurAsia 2015 and 9th IFIP WG 8.9 Working Conference, CONFENIS 2015 Held as Part of WCC 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9357, Springer Verlag, pp. 64-73, 3rd IFIP TC 5/8 International Conference on Information and Communication Technology, ICT-EurAsia 2015 and 9th IFIP WG 8.9 Working Conference on Research and Practical Issues of Enterprise Information Systems, CONFENIS 2015, Daejeon, Korea, Republic of, 10/4/15. https://doi.org/10.1007/978-3-319-24315-3_7

Abe H, Kinoshita K, Baba K, Takano S, Murakami K. Analyzing brain waves for activity recognition of learners. In You I, Xu LD, Neuhold E, Tjoa AM, Khalil I, editors, Information and Communication Technology - 3rd IFIP TC 5/8 International Conference, ICT-EurAsia 2015 and 9th IFIP WG 8.9 Working Conference, CONFENIS 2015 Held as Part of WCC 2015, Proceedings. Springer Verlag. 2015. p. 64-73. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-24315-3_7

Abe, Hiromichi ; Kinoshita, Kazuya ; Baba, Kensuke et al. / Analyzing brain waves for activity recognition of learners. Information and Communication Technology - 3rd IFIP TC 5/8 International Conference, ICT-EurAsia 2015 and 9th IFIP WG 8.9 Working Conference, CONFENIS 2015 Held as Part of WCC 2015, Proceedings. editor / Ilsun You ; Li Da Xu ; Erich Neuhold ; A. Min Tjoa ; Ismail Khalil. Springer Verlag, 2015. pp. 64-73 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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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AU - Abe, Hiromichi

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AB - 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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ER -

Analyzing brain waves for activity recognition of learners

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