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2013 Fiscal Year Final Research Report

A study on intention recognition based on analysis of human brain activity

Research Project

  • PDF
Project/Area Number 23650538
Research Category

Grant-in-Aid for Challenging Exploratory Research

Allocation TypeMulti-year Fund
Research Field Educational technology
Research InstitutionKobe University

Principal Investigator

TAKIGUCHI Tetsuya  神戸大学, 都市安全研究センター, 准教授 (40397815)

Project Period (FY) 2011 – 2013
Keywordsヒューマン・インターフェイス / コミュニケーション
Research Abstract

In this study, we focused on human verbal communication based on analysis of human brain activity data obtained by magnetoencephalography (MEG). In 2011, we proposed a new weighting method using a multiple kernel learning (MKL) algorithm to localize the brain area contributing to the accurate vowel discrimination. Our MKL simultaneously estimates both the classification boundary and the weight of each MEG sensor. The estimated weight indicates how the corresponding sensor is useful for classifying the MEG response patterns. But our proposed method using multiple kernel learning had a high computational cost. In 2012, we proposed a novel and fast weighting method using an AdaBoost algorithm to find the sensor area contributing to the accurate discrimination of vowels. Then, in 2013, we proposed a random projection for feature extraction of human activity data.

  • Research Products

    (2 results)

All 2012

All Journal Article (2 results) (of which Peer Reviewed: 2 results)

  • [Journal Article] An AdaBoost-Based Weighting Method for Localizing Human Brain Magnetic Activity2012

    • Author(s)
      T. Takiguchi, R. Takashima, Y. Ariki, T. Imada, J.-F. Lin, P.K. Kuhl, M. Kawakatsu, and M. Kotani
    • Journal Title

      APSIPA

      Pages: 1-4

    • URL

      http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6411803&abstractAccess=no&userType=inst

    • Peer Reviewed
  • [Journal Article] A New Multiple-Kernel-Learning Weighting Method for Localizing Human Brain Magnetic Activity2012

    • Author(s)
      T. Takiguchi, T. Imada, R. Takashima, Y. Ariki, J.-F. L. Lin, P.K. Kuhl, M. Kawakatsu, and M. Kotani
    • Journal Title

      IEEE ICASSP

      Pages: 761-764

    • DOI

      10.1109/ICASSP.2012.6287995

    • Peer Reviewed

URL: 

Published: 2015-06-25  

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