Reduction of Information Collection Cost for Inferring Brain Model Relations From Profile Information Using Machine Learning

Ryoichi Shinkuma, Satoshi Nishida, Naoya Maeda, Masataka Kado, Shinji Nishimoto

Research output: Contribution to journalArticlepeer-review


A content recommendation system based on human brain activity has become a reality. However, the cost of collecting the information from people is problematic. This article proposes a scheme that resolves the tradeoff between the inference performance from a profile model to a brain model and the cost of collecting profile information. In the proposed scheme, a machine learning model infers the brain model from the profile model and a feature selection method is applied to reduce the cost, i.e., the number of questionnaire items, of collecting profile information. Since only the top questionnaire items with the highest importance scores are used, we can maintain the inference performance as high as possible while limiting the number of questionnaire items. We demonstrate the effectiveness of the proposed scheme with a performance evaluation using an experimentally obtained brain model and a profile model created from real profile information. The results over different experimental parameters, video lengths, and feature selection methods demonstrate that the proposed scheme successfully identifies the top questionnaire items that contribute most significantly to the inference of brain models.

Original languageEnglish
Pages (from-to)4057-4068
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number7
Publication statusPublished - 2022 Jul 1
Externally publishedYes


  • Brain model
  • feature selection
  • machine learning (ML)
  • profile information

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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