Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems
Joint Authors
Xu, Baolei
Fu, Yunfa
Shi, Gang
Yin, Xuxian
Wang, Zhidong
Li, Hongyi
Jiang, Changhao
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-17
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
We introduce a new motor parameter imagery paradigm using clench speed and clench force motor imagery.
The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using “MIFS” feature selection criterion, scaled feature using “MIFS” feature selection criterion, and scaled feature using “mRMR” feature selection criterion.
Support vector machines (SVMs) and extreme learning machines (ELMs) are compared for classification between clench speed and clench force motor imagery using the optimized feature.
Our results show that no significant difference in the classification rate between SVMs and ELMs is found.
The scaled feature combinations can get higher classification accuracy than the no-scaled feature combinations at significant level of 0.01, and the “mRMR” feature selection criterion can get higher classification rate than the “MIFS” feature selection criterion at significant level of 0.01.
The time-frequency-phase feature can improve the classification rate by about 20% more than the time-frequency feature, and the best classification rate between clench speed motor imagery and clench force motor imagery is 92%.
In conclusion, the motor parameter imagery paradigm has the potential to increase the direct control commands for BCI control and the time-frequency-phase feature has the ability to improve BCI classification accuracy.
American Psychological Association (APA)
Xu, Baolei& Fu, Yunfa& Shi, Gang& Yin, Xuxian& Wang, Zhidong& Li, Hongyi…[et al.]. 2014. Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1049567
Modern Language Association (MLA)
Xu, Baolei…[et al.]. Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems. The Scientific World Journal No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1049567
American Medical Association (AMA)
Xu, Baolei& Fu, Yunfa& Shi, Gang& Yin, Xuxian& Wang, Zhidong& Li, Hongyi…[et al.]. Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1049567
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049567