論文

査読有り
2014年4月

Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information

NEUROIMAGE
  • Hiroshi Morioka
  • ,
  • Atsunori Kanemura
  • ,
  • Satoshi Morimoto
  • ,
  • Taku Yoshioka
  • ,
  • Shigeyuki Oba
  • ,
  • Motoaki Kawanabe
  • ,
  • Shin Ishii

90
開始ページ
128
終了ページ
139
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.neuroimage.2013.12.035
出版者・発行元
ACADEMIC PRESS INC ELSEVIER SCIENCE

For practical brain-machine interfaces (BMIs), electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are the only current methods that are non-invasive and available in non-laboratory environments. However, the use of EEG and NIRS involves certain inherent problems. EEG signals are generally a mixture of neural activity from broad areas, some of which may not be related to the task targeted by BMI, hence impairing BMI performance. NIRS has an inherent time delay as it measures blood flow, which therefore detracts from practical real-time BMI utility. To try to improve real environment EEG-NIRS-based BMIs, we propose here a novel methodology in which the subjects' mental states are decoded from cortical currents estimated from EEG, with the help of information from NIRS. Using a Variational Bayesian Multimodal EncephaloGraphy (VBMEG) methodology, we incorporated a novel form of NIRS-based prior to capture event related desynchronization from isolated current sources on the cortical surface. Then, we applied a Bayesian logistic regression technique to decode subjects' mental states from further sparsified current sources. Applying our methodology to a spatial attention task, we found our EEG-NIRS-based decoder exhibited significant performance improvement over decoding methods based on EEG sensor signals alone. The advancement of our methodology, decoding from current sources sparsely isolated on the cortex, was also supported by neuroscientific considerations; intraparietal sulcus, a region known to be involved in spatial attention, was a key responsible region in our task. These results suggest that our methodology is not only a practical option for EEG-NIRS-based BMI applications, but also a potential tool to investigate brain activity in non-laboratory and naturalistic environments. (C) 2013 Elsevier Inc. All rights reserved.

Web of Science ® 被引用回数 : 34

リンク情報
DOI
https://doi.org/10.1016/j.neuroimage.2013.12.035
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/24374077
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000338909500014&DestApp=WOS_CPL