Statistical analysis and classification of EEG-based attention network task using optimized feature selection

Hua Chin Lee, Li-Wei Ko*, Hui Ling Huang, Jui Yun Wu, Ya Ting Chuang, Shinn-Ying Ho

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This research incorporates optimized feature selection using an inheritable bi-objective combinatorial genetic algorithm (IBCGA) and mathematic modeling for classification and analysis of electroencephalography (EEG) based attention network. It consists of two parts. 1) We first design the attention network experiments, record the EEG signals of subjects from NeuronScan instrument, and filter noise from the EEG data. We use alerting scores, orienting scores, and conflict scores to serve as the efficiency evaluation of the attention network. 2) Based on an intelligent evolutionary algorithm as the core technique, we analyze the large-scale EEG data, identify a set of important frequency-channel factors, and establish mathematical models for within-subject, across-subject and leave-one-subject-out evaluation using a global optimization approach. The results of using 10 subjects show that the average classification accuracy of independent test in the within-subject case is 86.51%, the accuracy of the across-subject case is 68.44%, and the accuracy of the leave-one-subject-out case is 54.33%

Original languageEnglish
Title of host publicationIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014
Subtitle of host publication2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages100-105
Number of pages6
ISBN (Electronic)9781479945504
DOIs
StatePublished - 23 Jan 2014
Event2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2014 - Orlando, United States
Duration: 9 Dec 201412 Dec 2014

Publication series

NameIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings

Conference

Conference2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2014
CountryUnited States
CityOrlando
Period9/12/1412/12/14

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    Lee, H. C., Ko, L-W., Huang, H. L., Wu, J. Y., Chuang, Y. T., & Ho, S-Y. (2014). Statistical analysis and classification of EEG-based attention network task using optimized feature selection. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings (pp. 100-105). [7020700] (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCMB.2014.7020700