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Investigation of the trade-off between time window length, classifier update rate and classification accuracy for restorative brain-computer interfaces | IEEE Conference Publication | IEEE Xplore

Investigation of the trade-off between time window length, classifier update rate and classification accuracy for restorative brain-computer interfaces


Abstract:

Recently, the application of restorative brain-computer interfaces (BCIs) has received significant interest in many BCI labs. However, there are a number of challenges, t...Show More

Abstract:

Recently, the application of restorative brain-computer interfaces (BCIs) has received significant interest in many BCI labs. However, there are a number of challenges, that need to be tackled to achieve efficient performance of such systems. For instance, any restorative BCI needs an optimum trade-off between time window length, classification accuracy and classifier update rate. In this study, we have investigated possible solutions to these problems by using a dataset provided by the University of Graz, Austria. We have used a continuous wavelet transform and the Student t-test for feature extraction and a support vector machine (SVM) for classification. We find that improved results, for restorative BCIs for rehabilitation, may be achieved by using a 750 milliseconds time window with an average classification accuracy of 67% that updates every 32 milliseconds.
Date of Conference: 03-07 July 2013
Date Added to IEEE Xplore: 26 September 2013
Electronic ISBN:978-1-4577-0216-7

ISSN Information:

PubMed ID: 24110000
Conference Location: Osaka, Japan

References

References is not available for this document.