Volume 14, Issue 2 (9-2017)                   JSDP 2017, 14(2): 131-140 | Back to browse issues page


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Mikaeili M, Najafi F. Performance Analysis of a Persian text input brain–computer interface (BCI) P300 Speller system with row/column paradigm (RCP). JSDP. 2017; 14 (2) :131-140
URL: http://jsdp.rcisp.ac.ir/article-1-448-en.html
Shahed University
Abstract:   (1227 Views)

As a Brain computer interface system, BCI P300 Speller tries to help disabled people and patients to regain some of their lost ability with allowing communication via typing. The ability of personalization is one of the most important features in a BCI system, so the typing language as a personalization factor is an important feature in a BCI speller. Most prior researches on P300 Speller has focused on displaying English alphabet and there were only few studies made on other languages such as Chinese. In this research, we present a P300 Speller system, based on RCP, for Persian (Farsi) character input.
RCP (Row or Column Paradigm) was introduced by Farwell and Donchin at 1988, and since then it has been considered as a benchmark in P300 BCI speller research. As a result, in this study also, Row or column paradigm was selected as the base stimulation pattern in P300 speller system.
In order to evaluate the Persian row or column paradigm performance, we recorded EEG signals from volunteered subjects while the stimulation pattern was being displayed. It should be noted that the test was explained to each subject before testing, and for more experience and in order to reduce the error, each subject participated in an experiment test before attending the main test. These EEG signals were recorded from 8 channels based on ‘‘Fz’’, ‘‘Cz’’, ‘‘P3’’, ‘‘Pz’’, ‘‘P4’’, ‘‘O1’’, ‘‘Oz’’ and ‘‘O2’’ site in accordance to the International 10–20 system electrode placement system and by using Science Beam co.’s EEG recording device. The sample rate was 1 KHz which was down sampled to 250Hz. After recording, the EEG signals were filtered using a band passed filter And for classification, Linear discriminate analysis was used in combination with K-fold validation method for classifier training.
As performance determination, we calculated accuracy and bit rate for the mentioned system based on recorded data from volunteers and reached the average accuracy of 88.21% and bit rate of 6.74 (bits/minute) (we use Linear LDA classifier for classification and the total trial number was set to 15). Furthermore, in this research performance was measured for different trial number and final results demonstrated that this system can achieve high average accuracy of 80.06% and average bit rate of 42.43 (bits/minute) by using only 2 repetitions.
 

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Type of Study: Research | Subject: Paper
Received: 2015/10/27 | Accepted: 2017/03/5 | Published: 2017/10/21 | ePublished: 2017/10/21

References
1. [1] Z. Seyyedsalehi, AM. Nasrabadi, and V. Abootalebi. " Quadratic B-Spline Wavelet and Committee Machine for the P300 Detection in Brain Computer Interface.", Signal and Data Processing., vol. 2, no. 10, pp. 57-70, 2009.
2. [2] B. Allison, J. Pineda, "Effects of SOA and flash pattern manipulations on ERPs performance and preference: Implications for a BCI system", Int. J. Psychophysiol., vol. 59, no. 2, pp. 127-140, Feb. 2006. [DOI:10.1016/j.ijpsycho.2005.02.007] [PMID]
3. [3] C.M. Bishop, Pattern Recognition and Machine Learning., Aug. 2006.
4. [4] M. Chang, T.M. Rutkowski, "Two-Step Input Spatial Auditory BCI for Japanese Kana Characters.", Advances in Cognitive Neurodynamics (V), pp. 383-389, 2016.
5. [5] D. J. Krusienski, E. W. Sellers, F. Cabestaing, S. Bayoudh, D. J. McFarland, T. M. Vaughan, J. R. Wolpaw, "A comparison of classification techniques for the P300 Speller", J. Neural Eng., vol. 3, pp. 299-305, Dec. 2006. [DOI:10.1088/1741-2560/3/4/007] [PMID]
6. [6] J. Jin, E. W. Sellers, X. Wang, "Targeting an Efficient Target-to-Target Interval for P300 Speller Brain-Computer Interfaces", Medical & Biological Engineering & Computing, vol. 50, no. 3, pp. 289-296, Feb. 2012. [DOI:10.1007/s11517-012-0868-x] [PMID] [PMCID]
7. [7] F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, B. Arnaldi, "A Review of Classification Algorithms for EEG-Based Brain-Computer Interfaces", J. Neural Eng., vol. 4, pp. R1-R13, 2007. [DOI:10.1088/1741-2560/4/2/R01] [PMID]
8. [8] J. Jin, B. Allison, C. Brunner, B. Wang, X. Wang, J. Zhang, C. Neuper, G. Pfurtscheller, "P300 Chinese input system based on Bayesian LDA", Biomed. Tech., vol. 55, no. 1, pp. 5-18, 2010. [DOI:10.1515/bmt.2010.003] [PMID]
9. [9] D. J. McFarland, W. A. Sarnacki, G. Townsend, T. Vaughan, J. R. Wolpaw, "The P300-based brain–computer interface (BCI): Effects of stimulus rate", Clin. Neurophysiol., vol. 122, pp. 731-737, 2011. [DOI:10.1016/j.clinph.2010.10.029] [PMID] [PMCID]
10. [10] J. W. Minett, H.-Y. Zheng, M. C.-M. Fong, L. Zhou, G. Peng, and W. S.-Y. Wang, "A Chinese Text Input Brain-Computer Interface Based on the P300 Speller," International Journal of Human-Computer Interaction, vol. 28, pp. 472-483, 2012. [DOI:10.1080/10447318.2011.622970]
11. [11] R. Ortner, R. Prueckl, V. Putz, J. Scharinger, M. Bruckner, A. Schnuerer, and C. Guger, "Accuracy of a P300 Speller for Different Conditions: A Comparison," Proc. of the 5th Int. Brain-Computer Interface Conference, 2011, Graz, Austria, p. 196.
12. [12] A. E. Selim, M. A. Wahed and Y. M. Kadah., "MacHine learning methodologies in P300 speller Brain-Computer Interface systems, " in Radio Science Conference, pp. 1-9, 2009.
13. [13] E.W. Sellers, "A P300 Event-Related Potential Brain-Computer Interface (BCI): The Effects of Matrix Size and Inter Stimulus Interval on Performance", Biological Psychology, vol. 73, no. 3, pp. 242-252, 2006. [DOI:10.1016/j.biopsycho.2006.04.007] [PMID]
14. [14] A. Rakotomamonjy, V. Guigue, "BCI competition III: Dataset II-ensemble of SVMs for BCI P300 speller", IEEE Trans. Biomed. Eng., vol. 55, no. 3, pp. 1147-1154, Mar. 2008. [DOI:10.1109/TBME.2008.915728] [PMID]
15. [15] D. E. Thompson, S. Blain-Moraes, J. E. Huggins, "Performance assessment in brain-computer interface-based augmentative and alternative communication", Biomed. Eng. Online, vol. 12, pp. 43, Jan. 2013. [DOI:10.1186/1475-925X-12-43] [PMID] [PMCID]

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