PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Performance optimization of ERP-based BCIs using dynamic stopping
Martijn Schreuder, J. Höhne, M. Treder, Benjamin Blankertz and Michael Tangermann
Conf Proc IEEE Eng Med Biol Soc Volume 2011, pp. 4580-4583, 2011.

Abstract

Brain-computer interfaces based on event-related potentials face a trade-off between the speed and accuracy of the system, as both depend on the number of iterations. Increasing the number of iterations leads to a higher accuracy but reduces the speed of the system. This trade-off is generally dealt with by finding a fixed number of iterations that give a good result on the calibration data. We show here that this method is sub optimal and increases the performance significantly in only one out of five datasets. Several alternative methods have been described in literature, and we test the generalization of four of them. One method, called rank diff, significantly increased the performance over all datasets. These findings are important, as they show that 1) one should be cautious when reporting the potential performance of a BCI based on post-hoc offline performance curves and 2) simple methods are available that do boost performance.

EPrint Type:Article
Additional Information:This work is supported by the European ICT Programme Project TOBI FP7-224631.
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:9378
Deposited By:Martijn Schreuder
Deposited On:16 March 2012