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Methods Inf Med 2007; 46(02): 155-159
DOI: 10.1055/s-0038-1625398
Original Article
Schattauer GmbH

Enhancement of Classification Accuracy of a Time-frequency Approach for an EEG-based Brain-computer Interface

N. Yamawaki
1   Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, USA
,
C. Wilke
1   Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, USA
,
L. Hue
2   Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
,
Z. Liu
1   Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, USA
,
B. He
1   Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
11 January 2018 (online)

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Summary

Objectives : The aim of this paper is to develop a new algorithm to enhance the performance of EEG-based brain-computer interface (BCI).

Methods : We improved our time-frequency approach of classification of motor imagery (MI) tasks for BCI applications. The approach consists of Laplacian filtering, band-pass filtering and classification by correlation of time-frequency-spatial patterns.

Results and Conclusions : Through off-line analysis of data collected during a “cursor control" experiment, we evaluated the capability of our new method to reveal major features of the EEG control for enhancement of MI classification accuracy. The pilot results in a human subject are promising, with an accuracy rate of 96.1%.

Keywords

Time-frequency analysis - EEG - brain-computer interface - motor imagery
 

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