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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.
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Received: 29 September 2015
Published: 12 April 2016
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