Performance optimization of ERP-based BCIs using dynamic stopping.

BBCI group of the Machine Learning Department, Berlin Institute of Technology, Berlin, Germany.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:4580-3. DOI: 10.1109/IEMBS.2011.6091134
Source: PubMed

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.

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    ABSTRACT: Increasing the freedom of communication using conventional row/column (RC) P300 paradigm by naive way (increasing matrix size) may deteriorate inherent distraction effect and interaction speed. In this paper, we propose a two-level predictive (TLP) paradigm by integrating a 33 two-level matrix paradigm with a statistical language model. The TLP paradigm is evaluated using offline and online data from 10 healthy subjects. Significantly larger event-related potentials (ERPs) are evoked by the TLP paradigm compared with the classical 6 6 RC. During an online task (correctly spell an English sentence with 57 characters), accuracy and information transfer rate for the TLP are increased by 14.45% and 29.29%, respectively, when compared with the 6 6 RC. Time to complete the task is also decreased by 24.61% using TLP. In sharp contrast, an 88 RC (naive extension of the 66 RC) consumed 19:18% more time than the classical 66 RC. Furthermore, the statistical language model is also exploited to improve classification accuracy in a Bayesian approach. The proposed Bayesian fusion method is tested offline on data from the online spelling tasks. The results show its potential improvement on single-trial ERP classification.
    IEEE transactions on bio-medical engineering 05/2013; · 2.15 Impact Factor

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