Curriculum Vitaes

Yairi Ikuko

  (矢入 郁子)

Profile Information

Affiliation
Professor, Faculty of Science and Technology, Department of Information and Communication Sciences, Sophia University
Degree
Doctor of Philosophy in Engineering(Mar, 1999, The University of Tokyo)

Other name(s) (e.g. nickname)
Ikuko Eguchi Yairi
Researcher number
10358880
ORCID ID
 https://orcid.org/0000-0001-7522-0663
J-GLOBAL ID
200901082419968115
researchmap Member ID
6000011105

External link

Research Field: Informatics, Media and Communication Science and Technology
Main theme:
Applied research:(1)Barrier-free ubiquitous mobility support system, (2)Geographic information system for disabled pedestrian navigation, (3)Universal-designed interactive map contents and interface, and so on.
Basic research: (1)Spatial and graphic information representation method with sound and touch without vision, (2)Interactive interface design for the aged, the disabled and children, (3)Community support for offering spatial information, and so on.

(Subject of research)
Clinical research on technical acceptance and human-centered design of socially vulnerable people such as the elderly and the impaired


Papers

 134
  • Hiroshi Yamakawa, Ayako Fukawa, Ikuko Eguchi Yairi, Yutaka Matsuo
    Frontiers in Systems Neuroscience, 18, Aug 20, 2024  Peer-reviewed
    Background Imagination represents a pivotal capability of human intelligence. To develop human-like artificial intelligence, uncovering the computational architecture pertinent to imaginative capabilities through reverse engineering the brain's computational functions is essential. The existing Structure-Constrained Interface Decomposition (SCID) method, leverages the anatomical structure of the brain to extract computational architecture. However, its efficacy is limited to narrow brain regions, making it unsuitable for realizing the function of imagination, which involves diverse brain areas such as the neocortex, basal ganglia, thalamus, and hippocampus. Objective In this study, we proposed the Function-Oriented SCID method, an advancement over the existing SCID method, comprising four steps designed for reverse engineering broader brain areas. This method was applied to the brain's imaginative capabilities to design a hypothetical computational architecture. The implementation began with defining the human imaginative ability that we aspire to simulate. Subsequently, six critical requirements necessary for actualizing the defined imagination were identified. Constraints were established considering the unique representational capacity and the singularity of the neocortex's modes, a distributed memory structure responsible for executing imaginative functions. In line with these constraints, we developed five distinct functions to fulfill the requirements. We allocated specific components for each function, followed by an architectural proposal aligning each component with a corresponding brain organ. Results In the proposed architecture, the distributed memory component, associated with the neocortex, realizes the representation and execution function; the imaginary zone maker component, associated with the claustrum, accomplishes the dynamic-zone partitioning function; the routing conductor component, linked with the complex of thalamus and basal ganglia, performs the manipulation function; the mode memory component, related to the specific agranular neocortical area executes the mode maintenance function; and the recorder component, affiliated with the hippocampal formation, handles the history management function. Thus, we have provided a fundamental cognitive architecture of the brain that comprehensively covers the brain's imaginative capacities.
  • Tianchen Zhou, Yutaka Yakuwa, Natsuki Okamura, Takayuki Kuroda, Ikuko Eguchi Yairi
    IEICE Communications Express, 12(11) 575-578, Nov, 2023  Peer-reviewedLast authorCorresponding author
  • Nagisa Masuda, Ikuko Eguchi Yairi
    Frontiers in Psychology, 14, Jun 1, 2023  Peer-reviewedLast authorCorresponding author
    Objective and accurate classification of fear levels is a socially important task that contributes to developing treatments for Anxiety Disorder, Obsessive–compulsive Disorder, Post-Traumatic Stress Disorder (PTSD), and Phobia. This study examines a deep learning model to automatically estimate human fear levels with high accuracy using multichannel EEG signals and multimodal peripheral physiological signals in the DEAP dataset. The Multi-Input CNN-LSTM classification model combining Convolutional Neural Network (CNN) and Long Sort-Term Memory (LSTM) estimated four fear levels with an accuracy of 98.79% and an F1 score of 99.01% in a 10-fold cross-validation. This study contributes to the following; (1) to present the possibility of recognizing fear emotion with high accuracy using a deep learning model from physiological signals without arbitrary feature extraction or feature selection, (2) to investigate effective deep learning model structures for high-accuracy fear recognition and to propose Multi-Input CNN-LSTM, and (3) to examine the model’s tolerance to individual differences in physiological signals and the possibility of improving accuracy through additional learning.
  • Natsuki Okamura, Yutaka Yakuwa, Takayuki Kuroda, Ikuko E. Yairi
    IEICE Communications Express, 11(10) 667-672, Oct 1, 2022  Peer-reviewedLast authorCorresponding author
  • Eichi Takaya, Masaki Haraoka, Hiroki Takahashi, Ikuko Eguchi Yairi, Yasuyuki Kobayashi
    Journal of St. Marianna University, 13(2) 95-100, 2022  Peer-reviewed

Misc.

 121

Research Projects

 15

Academic Activities

 1

Social Activities

 22