研究者業績

中川 誠司

Nakagawa Seiji  (Seiji NAKAGAWA)

基本情報

所属
千葉大学 フロンティア医工学センター 教授
(兼任)大学院 工学研究院 教授
(兼任)大学院 融合理工学府 基幹工学専攻 医工学コース 教授,コース長
(兼任)工学部 総合工学科 医工学コース 教授,コース長
(兼任)医学部附属病院 教授
国立研究開発法人 産業技術総合研究所 客員研究員
東京大学 大学院医学系研究科 客員研究員
Univ. of Washington Visiting Scholar
国立研究開発法人 量子科学技術研究開発機構 客員研究員
学位
博士(工学)(1999年3月 東京大学)

連絡先
s.nakagawa99.alumni.u-tokyo.ac.jp
J-GLOBAL ID
200901063867675418
researchmap会員ID
5000005804

外部リンク

非侵襲的手法による神経生理計測(特に脳機能計測),心理計測,物理計測,さらにはコンピュータ・シミュレーションを駆使して,聴覚を中心とした知覚メカニズムや認知メカニズムの解明を進めています.また,知覚・認知メカニズム研究で得られた成果を利用することで,骨伝導補聴器や骨伝導スマートホンを初めとした福祉機器・医用機器の開発や,室内の視聴覚環境の最適化,騒音の快音化といった応用研究にも取り組んでいます.


論文

 321
  • Irwansyah, Sho Otsuka, Seiji Nakagawa
    HardwareX e00618-e00618 2024年12月  
  • Yuki Ishizaka, Sho Otsuka, Seiji Nakagawa
    Acoustical Science and Technology 45(5) 293-297 2024年9月  
    The medial olivocochlear reflex (MOCR) is reported to be modulated by the predictability of an upcoming sound occurrence. Here the relationship between MOCR and internal confidence in temporal anticipation evaluated by reaction time (RT) was examined. The timing predictability of the MOCR elicitor was manipulated by adding jitters to preceding sounds. MOCR strength/RT unchanged in a small (10%) jitter condition, and decrease/increase significantly in the largest (40%) jitter condition compared to the without-jitter condition. The similarity indicates that the MOCR strength reflects confidence in anticipation, and that the predictive control of MOCR and response execution share a common neural mechanism.
  • Seiji Nakagawa
    The Journal of the Acoustical Society of America 156(1) 610-622 2024年7月1日  
    Fluid-filled fractures involving kinks and branches result in complex interactions between Krauklis waves-highly dispersive and attenuating pressure waves within the fracture-and the body waves in the surrounding medium. For studying these interactions, we introduce an efficient 2D time-harmonic elastodynamic boundary element method. Instead of modeling the domain within a fracture as a finite-thickness fluid layer, this method employs zero-thickness, poroelastic Linear-Slip Interfaces to model the low-frequency, local fluid-solid interaction. Using this method, the scattering of Krauklis waves by a single kink along a straight fracture and the radiation of body waves generated by Krauklis waves within complex fracture systems are examined.
  • Hajime Yano, Ryoichi Takashima, Tetsuya Takiguchi, Seiji Nakagawa
    European Signal Processing Conference 1546-1550 2024年  
    Brain computer interfaces based on speech imagery have attracted attention in recent years as more flexible tools of machine control and communication. Classifiers of imagined speech are often trained for each individual due to individual differences in brain activity. However, the amount of brain activity data that can be measured from a single person is often limited, making it difficult to train a model with high classification accuracy. In this study, to improve the performance of the classifiers for each individual, we trained variational autoencoders (VAEs) using magnetoencephalographic (MEG) data from seven participants during speech imagery. The trained encoders of VAEs were transferred to EEGNet, which classified speech imagery MEG data from another participant. We also trained conditional VAEs to augment the training data for the classifiers. The results showed that the transfer learning improved the performance of the classifiers for some participants. Data augmentation also improved the performance of the classifiers for most participants. These results indicate that the use of VAE feature representations learned using MEG data from multiple individuals can improve the classification accuracy of imagined speech from a new individual even when a limited amount of MEG data is available from the new individual.

MISC

 1016

書籍等出版物

 8

担当経験のある科目(授業)

 28

共同研究・競争的資金等の研究課題

 27