論文

査読有り
2015年5月

Learning a common dictionary for subject-transfer decoding with resting calibration

NEUROIMAGE
  • Hiroshi Morioka
  • ,
  • Atsunori Kanemura
  • ,
  • Jun-ichiro Hirayama
  • ,
  • Manabu Shikauchi
  • ,
  • Takeshi Ogawa
  • ,
  • Shigeyuki Ikeda
  • ,
  • Motoaki Kawanabe
  • ,
  • Shin Ishii

111
開始ページ
167
終了ページ
178
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.neuroimage.2015.02.015
出版者・発行元
ACADEMIC PRESS INC ELSEVIER SCIENCE

Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain-machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual-spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments. (C) 2015 Elsevier Inc. All rights reserved.

Web of Science ® 被引用回数 : 32

リンク情報
DOI
https://doi.org/10.1016/j.neuroimage.2015.02.015
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/25682943
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000352224100016&DestApp=WOS_CPL