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Enhancing the classification accuracy of Steady-State Visual Evoked Potential-based Brain-Computer Interface using Component Synchrony Measure

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3 Author(s)
Kian B. Ng ; The University of Queensland, Queensland Brain Institute, Brisbane, 4072, Australia ; Ross Cunnington ; Andrew P. Bradley

Steady-State Visual Evoked Potential-based (SSVEP) Brain-Computer Interface (BCI) shows great potential as a viable BCI due to its ease of implementation and speed. However, the majority of the SSVEP-BCI implementations use only features from the Power Spectral Density (PSD) despite the fact that upon transforming the signals to the Fourier domain, both the phase and amplitude components are available. In this study we extract the phase response and compute the phase variance as a measure of phase synchrony. This phase synchrony method is called Component Synchrony Measure (CSM). Our results indicate that by including the CSM as a feature, the SSVEP-BCI classification accuracy is significantly enhanced. This further establishes the use of both amplitude and phase information for obtaining good classification accuracy in SSVEP-BCI.

Published in:

The 2012 International Joint Conference on Neural Networks (IJCNN)

Date of Conference:

10-15 June 2012