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Realization of Artifact-free Systems Using Single-channel EEG in Real Environment

Research Project

Project/Area Number 17H07389
Research Category

Grant-in-Aid for Research Activity Start-up

Allocation TypeSingle-year Grants
Research Field Human interface and interaction
Research InstitutionNational Institute of Advanced Industrial Science and Technology

Principal Investigator

KANOGA Suguru  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 (40803903)

Project Period (FY) 2017-08-25 – 2019-03-31
Project Status Completed (Fiscal Year 2018)
Budget Amount *help
¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Keywords脳波 / 単極信号 / 信号分離 / アーチファクト除去 / ウェアラブルデバイス / ブレインコンピュータインタフェース
Outline of Final Research Achievements

This study developed an automatic ocular artifact reduction technique using only single-channel EEG data. Usually, we need a reference channel to alleviate effects of ocular artifacts; however, the proposed technique can remove the effects automatically without any reference channel information. Although we can realize ocular artifact-free single-channel EEG-based systems in real environment after integrating this technique into them, this artifact reduction technique does not ensure the discriminability of artifact-reduced EEG data. Thus, the expansion of proposed artifact reduction for remaining discriminability is my future work.

Academic Significance and Societal Importance of the Research Achievements

脳波は非侵襲で脳内部の活動を観測でき、脳情報ベースのシステムに用いられている。近年、実環境下計測の利便性を求め、1チャネル(単極)のみで脳波を観測する場面が増えている。一方、観測脳波新語は常に多様なノイズ(アーチファクト)に汚染される。このため、解析時に脳波成分を抽出する必要がある。しかしながら、1種類の信号から脳波成分を抽出することは困難である。本研究は1種類の信号から脳波成分を自動的に抽出する手法を提案・精度実証した。これは、ウェアラブル機器により実環境下で観測される脳波信号から脳波成分を抽出し、脳情報ベースのシステムをより簡便に使用できるようになる意義を持つ。

Report

(3 results)
  • 2018 Annual Research Report   Final Research Report ( PDF )
  • 2017 Annual Research Report
  • Research Products

    (12 results)

All 2019 2018 2017 Other

All Journal Article (1 results) (of which Peer Reviewed: 1 results,  Open Access: 1 results) Presentation (8 results) (of which Int'l Joint Research: 8 results) Remarks (3 results)

  • [Journal Article] Multi-scale Dictionary Learning for Ocular Artifact Reduction from Single-channel Electroencephalograms2019

    • Author(s)
      Suguru Kanoga, Atsunori Kanemura, Hideki Asoh
    • Journal Title

      Neurocomputing

      Volume: undecided

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed / Open Access
  • [Presentation] A Comparative Study of Features and Classifiers in Single-channel EEG-based Motor Imagery BCI2018

    • Author(s)
      Suguru Kanoga, Atsunori Kanemura, Hideki Asoh
    • Organizer
      IEEE Global Conference on Signal and Information Processing (Global SIP'18)
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Semi-simulation Experiments for Quantifying the Performance of SSVEP-based BCI after Reducing Artifacts from Trapezius Muscles2018

    • Author(s)
      Suguru Kanoga, Masaki Nakanishi, Akihiko Murai, Mitsunori Tada, and Atsunori Kanemura
    • Organizer
      40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18)
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Transfer Learning Over Time and Placementin Wearable Myoelectric Control Systems2018

    • Author(s)
      Suguru Kanoga, Masashi Matsuoka, and Atsunori Kanemura
    • Organizer
      40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18)
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Semi-simulation Experiments for Quantifying the Performance of SSVEP-based BCI after Reducing Artifacts from Trapezius Muscles2018

    • Author(s)
      Suguru Kanoga, Masaki Nakanishi, Akihiko Murai, and Mitsunori Tada
    • Organizer
      40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18)
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Transfer Learning Over Time and Placementin Wearable Myoelectric Control Systems2018

    • Author(s)
      Suguru Kanoga, Masashi Matsuoka, Atsunori Kanemura
    • Organizer
      40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18)
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Assessing the Effect of Transfer Learning on Myoelectric Control Systems with Three Electrode Positions2018

    • Author(s)
      Suguru Kanoga and Atsunori Kanemura
    • Organizer
      19th IEEE International Conference of Industrial Technology (ICIT'18)
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Exploring Optimal Myoelectric Feature Indices for Forearm Control Strategy Using Robust Principal Component Analysis2017

    • Author(s)
      Suguru Kanoga, Akihiko Murai, and Mitsunori Tada
    • Organizer
      39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18)
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research
  • [Presentation] 転移学習による筋電位ベース動作識別モデルの適応力向上2017

    • Author(s)
      叶賀卓,兼村厚範
    • Organizer
      電気学会C部門大会
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research
  • [Remarks] Suguru Kanoga

    • URL

      http://u4ag2kanosr1.blogspot.com/

    • Related Report
      2018 Annual Research Report
  • [Remarks] 人工知能研究センター 機械学習研究チーム

    • URL

      https://www.airc.aist.go.jp/mlrt/

    • Related Report
      2018 Annual Research Report
  • [Remarks] Suguru Kanoga

    • URL

      http://u4ag2kanosr1.blogspot.jp/

    • Related Report
      2017 Annual Research Report

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Published: 2017-08-25   Modified: 2020-03-30  

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