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New Technology of Library and Information Service  2016, Vol. 32 Issue (3): 33-40    DOI: 10.11925/infotech.1003-3513.2016.03.05
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Identifying Research Trends Based on Patent Bibliographic Coupling: Case Study of Brain-Computer Interface
Gao Nan(),Fu Junying,Zhao Yunhua
Institute of Scientific & Technical Information of China, Beijing 100038, China
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Abstract  

[Objective] This study aims to identify the research trends (RT) based on patent bibliographic coupling method with the help of similarity algorithms. [Methods] We first established two types of patent similarity matrixes with two similarity algorithms - observed value (OV-BCA) and cosine distance (CD-BCA). We then used social network analysis to get the RT of Brain-Computer Interface (BCI) Patents. [Results] Six BCI research trend clusters were retrieved by OV-BCA algorithm, while CD-BCA algorithm got nine RT clusters. The two algorithms’ family ID coincidence rates were 43%. [Limitations] We focused on the comparison of results, including number, content and coincidence degree. More research is needed to study the characteristics of these algorithms. [Conclusions] RT can be retrieved by bibliographic coupling method with the help of the proposed algorithms. Specifically, the cosine distance algorithm can find more detailed research trends than the observed value algorithm.

Key wordsResearch trends      Patent      Bibliographic coupling analysis      Brian computer interface      Similarity algorithm     
Received: 29 September 2015      Published: 12 April 2016

Cite this article:

Gao Nan,Fu Junying,Zhao Yunhua. Identifying Research Trends Based on Patent Bibliographic Coupling: Case Study of Brain-Computer Interface. New Technology of Library and Information Service, 2016, 32(3): 33-40.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2016.03.05     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2016/V32/I3/33

[1] De Solla Price D J. Networks of Scientific Papers[J]. Science, 1965, 149(3683): 510-515.
[2] Small H G, Griffith B C.The Structure of Scientific Literatures I: Identifying and Graphing Specialties[J]. Science Studies, 1974, 4(1): 17-40.
[3] Persson O.The Intellectual Base and Research Fronts of JASIS 1986-1990[J]. Journal of the American Society for Information Science, 1994, 45(1): 31-38.
[4] Glanzel W, Czerwon H J.A New Methodological Approach to Bibliographic Coupling and Its Application to the National, Regional and Institutional Level[J]. Scientometrics, 1996, 37(2): 195-221.
[5] Morris S A, Yen G, Wu Z, et al.Time Line Visualization of Research Fronts[J]. Journal of the American Society for Information Science and Technology, 2003, 54(5): 413-422.
[6] Yang L, Morris S A, Barden E M.Mapping Institutions and Their Weak Ties in a Research Specialty: A Case Study of Cystic Fibrosis Body Composition Research[J]. Scientometrics, 2009, 79(2): 421-434.
[7] 张婷. 时间线和地形式可视化图谱: 科学传播研究前沿演进趋势分析[J]. 情报学报, 2009, 28(6): 923-928.
[7] (Zhang Ting.Timeline and Landscape: A Case Study of Visualizing the Evolution of Science Communication Research Front[J]. Journal of the China Society for Scientific & Technical Information, 2009, 28(6): 923-928.)
[8] Jarneving B.The Outbreak of SARS Mirrored by Bibliometric Mapping: Combining Bibliographic Coupling with the Complete Link Cluster Method[J]. Library and Information Science Research, 2007, 11(11): 11-36.
[9] Chen H, Huang M H, Chen D Z, et al.Detecting the Temporal Gaps of Technology Fronts: A Case Study of Smart Grid Field[J]. Technological Forecasting & Social Change, 2012, 79(9): 1705-1719.
[10] Hicks D.Limitations of Co-Citation Analysis as a Tool for Science Policy[J]. Social Studies of Science, 1987, 17(2): 295-316.
[11] 张嘉彬. 以书目耦合及共被引探讨不同引用区间之研究前沿: 以OLED领域为例[D]. 台北: 台湾大学, 2011.
[11] (Zhang Jiabin.The Study of Different Citation Windows of Research Fronts in OLED—Using Bibliographic Coupling and Co-Citation Methods [D]. Taipei: Taiwan University, 2011.)
[12] 张强. 基于专利计量的专利实施许可实证研究[D]. 重庆: 西南政法大学, 2012.
[12] (Zhang Qiang.Empirical Research of Patent Licensing Based on Patentometrics [D]. Chongqing: Southwest University of Political Science and Law, 2012.)
[13] 吴琳, 魏星, 霍翠婷. 基于Web的专利双语语料自动获取研究及实现——以esp@cenet数据库为例[J]. 现代图书情报技术, 2009(9): 57-63.
[13] (Wu Lin, Wei Xing, Huo Cuiting.Research and Implement of Automatic Patent Bilingual Corpus Extraction from Web——Taking esp@cenet as an Example[J]. New Technology of Library and Information Service, 2009(9): 57-63.)
[14] 经济合作与发展组织编著. 专利统计手册[M]. 高昌林等译. 北京: 科学技术文献出版社, 2013: 3.
[14] (Organization for Economic Co-operation and Development. Patent Statistics Manua [M]. Translated by Gao Changlin, et al. Beijing: Scientific and Technical Documention Press, 2013: 3.)
[15] 刘妍. 基于Lucene的余弦距离检测文档相似度方法的研究[J]. 信息系统工程, 2014(4): 129-130, 142.
[15] (Liu Yan.The Research of Document Similarity Detection Based on Lucene Cosine Distance Method[J]. China CIO News, 2014(4): 129-130, 142.)
[16] Wang B, Wong C M, Wan F, et al.Comparison of Different Classification Methods for EEG-based Brain Computer Interfaces: A Case Study [C]. In: Proceedings of the 2009 IEEE International Conference on Information and Automation. 2009.
[17] 杨建, 刘进, 吴明曦, 等. 脑机技术发展及其对军事领域的影响[J]. 国防科技, 2013, 34(6): 19-23.
[17] (Yang Jian, Liu Ming, Wu Mingxi, et al.Brain-Computer Technology Development and Its Impact on the Military[J]. National Defense Science & Technology, 2013, 34(6): 19-23.)
[18] 尧德中, 刘铁军, 雷旭, 等. 基于脑电的脑-机接口: 关键技术和应用前景[J]. 电子科技大学学报, 2009, 38(5): 550-554.
[18] (Yao Dezhong, Liu Tiejun, Lei Xu, et al.Electroencephalogram Based Brain-Computer Interface: Key Techniques and Application Prospect[J]. Journal of University of Electronic Science and Technology of China, 2009, 38(5): 550-554.)
[19] Mason S G, Bashshati A, Fatourechi M, et a1. A Comprehensive Survey of Brain Interface Technology Designs[J]. Annals of Biomedical Engineering, 2007, 35(2): 137-169.
[20] Huggins J E, Guger C, Allison B, et al.Workshops of the Fifth International Crain-Computer Interface Meeting: Defining the Future[J]. Brain-Computer Interfaces, Taylor & Francis, 2014, 1(1): 27-49.
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