Lindig-León C, Yáñez-Suárez O

Language: English
References: 8
Page: 53-69
PDF size: 1918.90 Kb.

ABSTRACT

This paper presents an application developed on the BCI2000 platform which reduces the average spelling time per symbol on the Donchin speller. The motivation was to reduce the compromise between spelling rate and spelling accuracy due to the large amount of responses required in order to perform coherent average techniques. The methodology was made under a Bayesian approach which allows calculation of each target’s class posterior probability. This result indicates the probability of each response of belonging to the infrequent class. When there is enough evidence to make a decision the system stops the stimulation process and moves on with the next symbol, otherwise it continues stimulating the user until it finds the selected letter. The average spelling rate, after using the proposed methodology with 14 healthy users and a maximum number of 5 stimulation sequences, was of 6.1 ± 0.63 char/min, compared to a constant rate of 3.93 char/min with the standard system.

REFERENCES

1. Farwell L.A., Donchin E., “Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials”, Electroencephal Clin Neurophysiol, 70(6):510-23, 1988.

2. Lee T.W., Yu Y.W., Wu H.C., Chen T.J., “Do resting brain dynamics predict oddball evoked-potential?”, BMC Neurosci, 24:12:121, 2011.

3. Rompelman O, Ros HH., “Coherent averaging technique: a tutorial review. Part 1: Noise reduction and the equivalent filter”, J Biomed Eng, 1986.

4. Ledesma C., Bojorges E.R., Gentiletti G., Bougrain L., Saavedra C., Yanez O, “P300-Speller Public-Domain Database”, http://akimpech.izt.uam.mx/p300db

5. Schalk G., McFarland D., Hinterberger T., Birbaumer N., Wolpaw, J., “BCI2000: A General-Purpose Brain-computer Interface System”, IEEE Trans. Biomed. Eng., 51:1034-1043, 2004.

6. Bishop C.M., Pattern Recognition and Machine Learning, 1st ed, Springer, 2006.

7. Platt J., “Probabilistic outputs for support vector machines and comparison to regularized likelihood methods”, in Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, Eds. Cambridge, MA: MIT Press, 2000.

8. Lin H.T., Lin C.J., Weng R.C., “A Note on Platt’s Probabilistic Outputs for Support Vector Machines”, Machine Learning, Volume 68, No. 3, 2007.