Schreuder Martijn, Hohne Johannes, Treder Matthias, Blankertz Benjamin, Tangermann Michael
BBCI group of the Machine Learning Department, Berlin Institute of Technology, Berlin, Germany.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4580-3. doi: 10.1109/IEMBS.2011.6091134.
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.
基于事件相关电位的脑机接口面临系统速度和准确性之间的权衡，因为两者都取决于迭代次数。增加迭代次数会提高准确性，但会降低系统速度。这种权衡通常通过找到在校准数据上能产生良好结果的固定迭代次数来解决。我们在此表明，这种方法并非最优，并且仅在五个数据集中的一个数据集上显著提高了性能。文献中描述了几种替代方法，我们测试了其中四种方法的通用性。一种称为秩差的方法在所有数据集上均显著提高了性能。这些发现很重要，因为它们表明：1）在基于事后离线性能曲线报告脑机接口的潜在性能时应谨慎；2）有一些简单方法确实可以提高性能。
