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|Title:||Investigation of the trade-off between time window length, classifier update rate and classification accuracy for restorative brain-computer interfaces|
|Citation:||Proceedings of the 35th Annual International Conference of the IEEE EMBS, 2013: pp.1567-1570|
|Series/Report no.:||IEEE Engineering in Medicine and Biology Society Conference Proceedings|
|Conference Name:||Annual International Conference of the IEEE Engineering in Medicine and Biology Society (35th : 2013 : Osaka, Japan)|
|Sam Darvishi, Michael C. Ridding, Derek Abbott, Mathias Baumert|
|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.|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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