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Computational Intelligence and Neuroscience
Volume 2018, Article ID 3505371, 13 pages
Research Article

Decoding Pigeon Behavior Outcomes Using Functional Connections among Local Field Potentials

1School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
2School of Information Engineering, Huanghuai University, Zhumadian, Henan, China
3Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China

Correspondence should be addressed to Hong Wan; nc.ude.uzz@gnohnaw

Received 24 October 2017; Revised 9 January 2018; Accepted 14 January 2018; Published 15 February 2018

Academic Editor: Saeid Sanei

Copyright © 2018 Yan Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recent studies indicate that the local field potential (LFP) carries information about an animal’s behavior, but issues regarding whether there are any relationships between the LFP functional networks and behavior tasks as well as whether it is possible to employ LFP network features to decode the behavioral outcome in a single trial remain unresolved. In this study, we developed a network-based method to decode the behavioral outcomes in pigeons by using the functional connectivity strength values among LFPs recorded from the nidopallium caudolaterale (NCL). In our method, the functional connectivity strengths were first computed based on the synchronization likelihood. Second, the strength values were unwrapped into row vectors and their dimensions were then reduced by principal component analysis. Finally, the behavioral outcomes in single trials were decoded using leave-one-out combined with the -nearest neighbor method. The results showed that the LFP functional network based on the gamma-band was related to the goal-directed behavior of pigeons. Moreover, the accuracy of the network features (74 8%) was significantly higher than that of the power features (61 12%). The proposed method provides a powerful tool for decoding animal behavior outcomes using a neural functional network.