A Recurrence-Based Approach for Feature Extraction in Brain-Computer Interface Systems
The feature extraction stage is one of the main tasks underlying pattern recognition, and, is particularly important for designing Brain-Computer Interfaces (BCIs), i.e. structures capable of mapping brain signals in commands for external devices. Within one of the most used BCIs paradigms, that based on Steady State Visual Evoked Potentials (SSVEP), such task is classically performed in the spectral domain, albeit it does not necessarily provide the best achievable performance. The aim of this work is to use recurrence-based measures in an attempt to improve the classification performance obtained with a classical spectral approaches for a five-command SSVEP-BCI system. For both recurrence and spectral spaces, features were selected using a cluster measure defined by the Davies-Bouldin index and the classification stage was based on linear discriminant analysis. As the main result, it was found that the threshold \(\varepsilon \) of the recurrence plot, chosen so as to yield a recurrence rate of 2.5 %, defined the key discriminant feature, typically providing a mean classification error of less than 2 % when information from 4 electrodes was used. Such classification performance was significantly better than that attained using spectral features, which strongly indicates that RQA is an efficient feature extraction technique for BCI.
KeywordsLinear Discriminant Analysis Classification Error Spectral Domain Recurrence Plot Recurrence Quantification Analysis
L.F.S. Uribe thanks CAPES for financial support. D.C. Soriano and R. Suyama thank UFABC and FAPESP (Grant number 2012/50799-2) for the financial support. F.I. Fazanaro thanks FAPESP (Grant number 2012/09624-4) for the financial support. R. Attux thanks CNPq for financial support. All authors are grateful to FINEP for funding the DesTine project (process number 01.10.0449.00).
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