A novel bioelectronic nose based on brain-machine interface using implanted electrode recording in vivo in olfactory bulb

被引:23
|
作者
Dong, Qi [1 ,2 ]
Du, Liping [1 ]
Zhuang, Liujing [1 ]
Li, Rong [1 ]
Liu, Qingjun [1 ]
Wang, Ping [1 ,2 ]
机构
[1] Zhejiang Univ, Dept Biomed Engn, Educ Minist, Key Lab Biomed Engn,Biosensor Natl Special Lab, Hangzhou 310027, Zhejiang, Peoples R China
[2] Chinese Acad Sci, State Key Lab Transducer Technol, Shanghai 200050, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
Bioelectronic nose; Brain-machine interface; Odor discrimination; In vivo measurement; ODOR; EPITHELIUM; BIOSENSOR; DYNAMICS; NEURONS; SYSTEM; RATS;
D O I
10.1016/j.bios.2013.05.035
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
The mammalian olfactory system has merits of higher sensitivity, selectivity and faster response than current electronic nose system based on chemical sensor array. It is advanced and feasible to detect and discriminate odors by mammalian olfactory system. The purpose of this study is to develop a novel bioelectronic nose based on the brain machine interface (BMI) technology for odor detection by in vivo electrophysiological measurements of olfactory bulb. In this work, extracellular potentials of mitral/tufted (M/T) cells in olfactory bulb (OB) were recorded by implanted 16-channel microwire electrode arrays. The odor-evoked response signals were analyzed. We found that neural activities of different neurons showed visible different firing patterns both in temporal features and rate features when stimulated by different small molecular odorants. The detection low limit is below 1 ppm for some specific odors. Odors were classified by an algorithm based on population vector similarity and support vector machine (SVM). The results suggested that the novel bioelectonic nose was sensitive to odorant stimuli. The best classifying accuracy was up to 95%. With the development of the BMI and olfactory decoding methods, we believe that this system will represent emerging and promising platforms for wide applications in medical diagnosis and security fields. (C) 2013 Published by Elsevier B.V.
引用
收藏
页码:263 / 269
页数:7
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