Detection of control or idle state with a likelihood ratio test in asynchronous SSVEP-based brain-computer interface systems

We consider the detection of the control or idle state in an asynchronous Steady-state visually evoked potential (SSVEP)-based brain computer interface system. We propose a likelihood ratio test using Canonical Correlation Analysis (CCA) scores calculated from the EEG measurements. The test exploits...

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Bibliographic Details
Published in2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2016; pp. 1568 - 1571
Main Authors Merino, Lenis M., Nayak, Tapsya, Hall, Garrett, Pack, Daniel J., Yufei Huang
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.08.2016
Subjects
Algorithms
Brain-Computer Interfaces
Electroencephalography
Error analysis
Evoked Potentials
Evoked Potentials, Visual
Histograms
Humans
Light rail systems
Robustness
Support vector machines
Training
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Summary:We consider the detection of the control or idle state in an asynchronous Steady-state visually evoked potential (SSVEP)-based brain computer interface system. We propose a likelihood ratio test using Canonical Correlation Analysis (CCA) scores calculated from the EEG measurements. The test exploits the state-specific distributions of CCA scores. The algorithm was tested on offline measurements from 42 participants and the results should a significant improvement in detection error rate over the support vector machine classifier. The proposed test is also shown to be robust against training sample size.
ISSN:1557-170X
DOI:10.1109/EMBC.2016.7591011