• Top page
  • Timetable
  • Per session
  • Per presentation
  • How to
  • Meeting Planner




開催日 2014/9/13
時間 17:10 - 18:10
会場 Room G(303)
Chairperson(s) 宮脇 陽一 / Yoichi Miyawaki (電気通信大学 先端領域教育研究センター / Center for Frontier Science and Engineering The University of Electro-Communications, Japan)
美馬 達哉 / Tatsuya Mima (京都大学大学院医学研究科附属脳機能総合研究センター / Human Brain Research Center, Kyoto University, Graduate School of Medicine, Japan)

Decoding daily-life behavioral signatures in the real environment: portable NIRS signal using behavior labels

  • O3-G-1-2
  • 小川 剛史 / Takeshi Ogawa:1 グプタ パンカジ / Pankaj Gupta:1 矢野 憲 / Ken Yano:1 アブドゥルラヒム ジャミラ / Jamilah Abdur-Rahim:1 森岡 博史 / Hiroshi Morioka:1,2,3 平山 淳一郎 / Jun-ichiro Hirayama:1 山口 俊平 / Shumpei Yamaguchi:4 石川 亮宏 / Akihiro Ishikawa:4 井上 芳浩 / Yoshihiro Inoue:4 川鍋 一晃 / Motoaki Kawanabe:1 石井 信 / Shin Ishii:1,2 
  • 1:ATR脳総研認知機構研動的脳イメージング研 / Dept Dynamic Brain Imaging, CMC, ATR, Japan 2:京都大院情報システム科学 / Dept System Science, Graduate School of Informatics, Kyoto Univ, Kyoto, Japan 3:日本学術振興会 / JSPS, Tokyo, Japan 4:島津製作所 / Shimadzu Corp, Kyoto, Japan 

In neuroimaging study, a challenging issue is elucidation of natural brain processing in a less-controlled environment. Thanks to the advancement in technologies to miniaturize the sensor device (EEG, NIRS, etc.) and wireless communication, portable/wearable devices are now available, allowing participants to be released from tight experimental conditions. To examine "natural behavior" signatures based on such portable devices, however, there are two main problems, i) how to analyze recorded signals with external/internal artifacts in the real environment, ii) how to detect behavior events from the signals without cue stimulus. To this end, we developed a wearable multi-channel fNIRS-EEG system (kNIRS-EEG). In the unique experimental environment that mimics a living house, called BMI house, we developed a system to record NIRS-EEG and environmental data simultaneously. We examined NIRS-EEG recordings from both single-participant and dual-participants simultaneous sessions. The participants were instructed to perform sequential daily-life and free moving behaviors. In off-line analysis, we labeled each behavior a tag and registered it to a "Brain-log database". After each segment of 8ch NIRS signals was associated with the behavioral tag that signifies the class of daily-life behaviors (i.e. operating TV/AC, reading etc.), we attempted to decode each behavior from the NIRS signals. We applied SVM for the decoding and evaluated timing of the decodable period. Our preliminary analysis found that the classification accuracy rose after the onset of the behavior segment. This result suggested that we could extract some behavior-related information from the recorded signal even if it contains motion artifacts. It is plausible to decode a participant's action and/or intention based on database that associates the brain signals with behavior labels determined from environmental sensors.

Copyright © Neuroscience2014. All Right Reserved.