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Digital Signal Processing and Machine Learning 327
Step 2 For the qth round in a new trial, feature vectors Fek,q (k = 1, ··· , 12,
q = 1, ··· , 10) are extracted. Applying the SVM to these feature vectors,
120 scores denoted as sk,q are obtained. These scores are the values of the
objective function of the SVM.
Step 3 P300 detection. Sum the scores for all the rows or columns k = 1, ··· ,12
across all the q rounds using ssk = �10 q=1 sk,q. Therow(k = 1, ··· , 6) and
column (k = 7, ··· , 12) with the respective maximum summed scores are
then identified. The decision is then made on the character at the intersection
of the identified row and column. This character forms the output of the BCI
speller.
Using this BCI speller, the 10 users were able to write the test phrase of 42
characters with an average accuracy of 99% [54].
8 Summary
This chapter discusses signal processing and machine learning methods that are
commonly used in BCIs. However, there are many methods that are not discussed.
Furthermore, signal processing often involves several stages and there is no best
method for each stage. Nevertheless, the most suitable method and parameters
depend on the brain patterns used in BCIs (such as P300, SSVEP, or ERD/ERS),
the quantity and quality of the training data acquired, the training time, and various
user factors.
Acknowledgments The authors are grateful to G. Townsend, B. Graimann, and G. Pfurtscheller
for their permission to use Figures 7 and 8 in this chapter. The authors are also grateful to anonymous
reviewers and the editors B. Graimann, G. Pfurtscheller, B. Allison of this book for their
contributions to this chapter. Yuanqing Li’s work was partially supported by National Natural
Science Foundation of China under Grant 60825306, Natural Science Foundation of Guangdong
Province, China under Grant 9251064101000012. Kai Keng Ang and Cuntai Guan’s work were
supported by the Science and Engineering Research Council of A ∗ STAR (Agency for Science,
Technology and Research), Singapore.
References
1. B. Rockstroh, T. Elbert, A. Canavan, W. Lutzenberger, and N. Birbaumer, Eds., Slow cortical
potentials and behavior, 2nd ed. Urban and Schwarzenberg, Baltimore, MD, (1989).
2. N. Birbaumer, Slow cortical potentials: their origin, meaning and clinical use. In G.J.M.
van Boxtel and K.B.E. Böcker, (Eds.) Brain and behavior:past, present, and future, Tilburg
University Press, Tilburg, pp. 25–39, (1997).
3. E. Donchin, ‘Surprise!...surprise?’ Psychophysiology, 18(5) 493–513, (1981).
4. L.A. Farwell and E. Donchin, Talking off the top of your head: Toward a mental prosthesis
utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol, 70(6) 510–
523, (1998).
5. T.F. Collura, Real-time filtering for the estimation of steady-state visual evoked brain
potentials. IEEE Trans Biomed Eng, 37(6), 650–652, (1990).