Abstract
The P300 speller allows users to select letters just by thoughts. However, due to the low signal-to-noise ratio of the P300 response, signal averaging is often performed, which improves the spelling accuracy but degrades the spelling speed. The authors have proposed reliability-based automatic repeat request (RB-ARQ) to ease this problem. RB-ARQ could be enhanced when it is combined with the error correction based on the error-related potentials. This paper presents how to combine both methods and how to optimize parameters to maximize the performance of the P300 speller. The result shows that the performance was improved by 40 percent on average.
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References
Blankertz, B., Müller, K.R., Krusienski, D.J., et al.: The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans. Neural Syst. Rehab. Eng. 14(2), 153–159 (2006)
Dal Seno, B., Matteucci, M., Mainardi, L.: The utility metric: A novel method to assess the overall performance of discrete brain-computer interfaces. IEEE Trans. Neural Syst. Rehab. Eng. 18(1), 20–28 (2009)
Dal Seno, B., Matteucci, M., Mainardi, L.: On-line detection of p300 and error potentials in a BCI speller. Computational Intelligence and Neuroscience 2010, Article ID 307254, 5 pages (2010)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. New Technology Communications, 2nd edn. (2001)
Farwell, L.A., Donchin, E.: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70(6), 510–523 (1988)
Ferrez, P.W., Millan, J.d.R.: Error-related EEG potentials generated during simulated brain-computer interaction. IEEE Trans. Biomed. Eng. 55(3), 923–929 (2008)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, Heidelberg (2009)
Schalk, G., McFarland, D., Hinterberger, T., et al.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004)
Takahashi, H., Yoshikawa, T., Furuhashi, T.: Application of support vector machines to reliability-based automatic repeat request for brain-computer interfaces. In: Proc. 31st Annual Int. Conf. IEEE EMBS, pp. 6457–6460 (2009)
Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., et al.: Brain-computer interface technology: a review of the first international meeting. IEEE Trans. Rehab. Eng. 8(2), 164–173 (2000)
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Takahashi, H., Yoshikawa, T., Furuhashi, T. (2010). Reliability-Based Automatic Repeat reQuest with Error Potential-Based Error Correction for Improving P300 Speller Performance. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_7
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DOI: https://doi.org/10.1007/978-3-642-17534-3_7
Publisher Name: Springer, Berlin, Heidelberg
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