Title :
Performance optimization of ERP-based BCIs using dynamic stopping
Author :
Schreuder, Martijn ; Höhne, Johannes ; Treder, Matthias ; Blankertz, Benjamin ; Tangermann, Michael
Author_Institution :
Machine Learning Dept., Berlin Inst. of Technol., Berlin, Germany
fDate :
Aug. 30 2011-Sept. 3 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.
Keywords :
auditory evoked potentials; brain-computer interfaces; calibration; optimisation; visual evoked potentials; ERP-based BCI; brain-computer interface; calibration data; datasets; dynamic stopping; event-related potential; iteration; performance optimization; post-hoc offline performance curves; rank cliff method; Accuracy; Brain computer interfaces; Calibration; Electroencephalography; Neuroscience; Training; Visualization; Brain; Evoked Potentials; Humans; Man-Machine Systems;
Conference_Titel :
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-4121-1
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2011.6091134