Flexible multidimensional signal processing using signal structure by sparse dictionary learning
Project/Area Number |
26730130
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Research Category |
Grant-in-Aid for Young Scientists (B)
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Allocation Type | Multi-year Fund |
Research Field |
Soft computing
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Research Institution | National Institute of Advanced Industrial Science and Technology |
Principal Investigator |
Kanemura Atsunori 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 研究員 (50580297)
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Project Period (FY) |
2014-04-01 – 2018-03-31
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Project Status |
Completed (Fiscal Year 2017)
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Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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Keywords | 辞書学習 / スパース性 / グループ正則化 / ベイズ情報量規準 / 脳波 / Brain computer interface |
Outline of Final Research Achievements |
We have proposed many signal processing methods that take into account the nature of domain problems and the characteristics of domain signals. Our main achievements include dictionary learning that introduced individual- and session-wise linear transformation for EEG signals, bases extraction from human mobility traces, bases extraction from response patterns of human skill tests, and sparse dictionary learning of time series and it skip representations.
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Report
(5 results)
Research Products
(45 results)
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[Journal Article] Learning a common dictionary for subject-transfer decoding with resting calibration2015
Author(s)
Morioka, H., Kanemura, A., Hirayama, J., Shikauchi, M., Ogawa, T., Ikeda, S., Kawanabe, M., and Ishii, S.
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Journal Title
NeuroImage
Volume: 111
Pages: 167-178
DOI
Related Report
Peer Reviewed / Acknowledgement Compliant
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[Presentation] 脳波データへの深層学習の適用2016
Author(s)
野沢健人,星野貴行,福田拓也,兼村厚範
Organizer
情報論的学習理論ワークショップ(IBIS)
Place of Presentation
百周年時計台記念館 (京都府京都市)
Year and Date
2016-11-17
Related Report
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