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 language | English |
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Title of host publication | IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIBCI 2014 |
Subtitle of host publication | 2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces, Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 5-9 |
Number of pages | 5 |
ISBN (Electronic) | 9781479945443 |
DOIs | |
Publication status | Published - 2015 Jan 12 |
Event | 2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces, CIBCI 2014 - Orlando, United States Duration: 2014 Dec 9 → 2014 Dec 12 |
Publication series
Name | IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIBCI 2014: 2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces, Proceedings |
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Other
Other | 2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces, CIBCI 2014 |
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Country/Territory | United States |
City | Orlando |
Period | 14/12/9 → 14/12/12 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
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