Across-subject estimation of 3-back task performance using EEG signals

Jinsoo Kim, Min Ki Kim, Christian Wallraven, Sung Phil Kim

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

Abstract

This study was aimed at estimating subjects' 3-back working memory task error rate using electroencephalogram (EEG) signals. Firstly, spatio-temporal band power features were selected based on statistical significance of across-subject correlation with the task error rate. Method-wise, ensemble network model was adopted where multiple artificial neural networks were trained independently and produced separate estimates to be later on aggregated to form a single estimated value. The task error rate of all subjects were estimated in a leave-one-out cross-validation scheme. While a simple linear method underperformed, the proposed model successfully obtained highly accurate estimates despite being restrained by very small sample size.

Original languageEnglish
Title of host publicationIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIBCI 2014: 2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5-9
Number of pages5
ISBN (Print)9781479945443
DOIs
Publication statusPublished - 2015 Jan 12
Event2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces, CIBCI 2014 - Orlando, United States
Duration: 2014 Dec 92014 Dec 12

Other

Other2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces, CIBCI 2014
CountryUnited States
CityOrlando
Period14/12/914/12/12

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Keywords

  • Artificial neural network
  • Committee of machines
  • EEG
  • N-back task
  • Network ensemble

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Computer Science Applications

Cite this

Kim, J., Kim, M. K., Wallraven, C., & Kim, S. P. (2015). Across-subject estimation of 3-back task performance using EEG signals. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIBCI 2014: 2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces, Proceedings (pp. 5-9). [7007785] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIBCI.2014.7007785