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Predicting EEG Sample Size Required for Classification Calibration

  • Zijing Mao
  • Tzyy-Ping Jung
  • Chin-Teng Lin
  • Yufei HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9743)

Abstract

This study considers an important problem of predicting required calibration sample size for electroencephalogram (EEG)-based classification in brain computer interaction (BCI). We propose an adaptive algorithm based on learning curve fitting to learn the relationship between sample size and classification performance for each individual subject. The algorithm can always provide the predicted result in advance of reaching the baseline performance with an average error of 17.4 %. By comparing the learning curve of different classifiers, the algorithm can also recommend the best classifier for a BCI application. The algorithm also learns a sample size upper bound from the prior datasets and uses it to detect subject outliers that potentially need excessive amount of calibration data. The algorithm is applied to three EEG-based BCI datasets to demonstrate its utility and efficacy. A Matlab package with GUI is also developed and available for downloading at https://github.com/ZijingMao/LearningCurveFittingForSampleSizePrediction. Since few algorithms are yet available to predict performance for BCIs, our algorithm will be an important tool for real-life BCI applications.

Keywords

Sample size prediction Calibration Brain computer interface EEG Rapid serial visual presentation Driver’s fatigue 

Notes

Acknowledgments

Research was sponsored in part by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-10-2-0022. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implies, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for the Government purposes notwithstanding any copyright notation herein. This work received computational support from Computational System Biology Core at the University of Texas at San Antonio, funded by the National Institute on Minority Health and Health Disparities (G12MD007591) from the National Institutes of Health.

References

  1. 1.
    Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G., et al.: Brain-computer interface technology: a review of the first international meeting. IEEE Trans. Rehabil. Eng. 8, 164–173 (2000)CrossRefGoogle Scholar
  2. 2.
    Bigdely-Shamlo, N., Vankov, A., Ramirez, R.R., Makeig, S.: Brain activity-based image classification from rapid serial visual presentation. IEEE Trans. Neural Syst. Rehabil. Eng. 16, 432–441 (2008)CrossRefGoogle Scholar
  3. 3.
    Wu, D., Lance, B.J., Parsons, T.D.: Collaborative filtering for brain-computer interaction using transfer learning and active class selection. PLoS ONE 8, e56624 (2013)CrossRefGoogle Scholar
  4. 4.
    Sun, S., Zhou, J.: A review of adaptive feature extraction and classification methods for EEG-based brain-computer interfaces. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 1746–1753 (2014)Google Scholar
  5. 5.
    Panicker, R.C., Puthusserypady, S., Sun, Y.: Adaptation in P300 brain–computer interfaces: a two-classifier cotraining approach. IEEE Trans. Biomed. Eng. 57, 2927–2935 (2010)CrossRefGoogle Scholar
  6. 6.
    Eng, J.: Sample Size Estimation: How Many Individuals Should Be Studied? Radiology 227, 309–313 (2003)CrossRefGoogle Scholar
  7. 7.
    Suresh, K., Chandrashekara, S.: Sample size estimation and power analysis for clinical research studies. J. Hum. Reprod. Sci. 5, 7 (2012)CrossRefGoogle Scholar
  8. 8.
    Figueroa, R.L., Zeng-Treitler, Q., Kandula, S., Ngo, L.H.: Predicting sample size required for classification performance. BMC Med. Inform. Decis. Mak. 12, 8 (2012)CrossRefGoogle Scholar
  9. 9.
    Zodpey, S.P.: Sample size and power analysis in medical research. Indian J. Dermatol. Venereol. Leprology 70, 123 (2004)Google Scholar
  10. 10.
    Meek, C., Thiesson, B., Heckerman, D.: The learning-curve sampling method applied to model-based clustering. J. Mach. Learn. Res. 2, 397–418 (2002)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Cortes, C., Jackel, L.D., Solla, S.A., Vapnik, V., Denker, J.S.: Learning curves: asymptotic values and rate of convergence. Adv. Neural Inf. Process. Syst. 6, 327–334 (1994)Google Scholar
  12. 12.
    Meng, J., Meriño, L.M., Shamlo, N.B., Makeig, S., Robbins, K., Huang, Y.: Characterization and robust classification of EEG signal from image RSVP events with independent time-frequency features. PLoS ONE 7, e44464 (2012)CrossRefGoogle Scholar
  13. 13.
    U.S. Department of the Army. Use of volunteers as subjects of research. AR 70–25 Washington DC. Government Printing Office (1990)Google Scholar
  14. 14.
    U.S Department of Defense Office of the Secretary of Defense, Code of federal regulations, protection of human subjects. 32 CFR 219, vol. 32 CFR 219 (1999)Google Scholar
  15. 15.
    Chuang, S.-W., Ko, L.-W., Lin, Y.-P., Huang, R.-S., Jung, T.-P., Lin, C.-T.: Co-modulatory spectral changes in independent brain processes are correlated with task performance. Neuroimage 62, 1469–1477 (2012)CrossRefGoogle Scholar
  16. 16.
    Sajda, P., Pohlmeyer, E., Wang, J., Parra, L.C., Christoforou, C., Dmochowski, J., et al.: In a blink of an eye and a switch of a transistor: cortically coupled computer vision. Proc. IEEE 98, 462–478 (2010)CrossRefGoogle Scholar
  17. 17.
    Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38, 367–378 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    McLachlan, G.: Discriminant Analysis and Statistical Pattern Recognition, vol. 544. Wiley, New York (2004)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zijing Mao
    • 1
  • Tzyy-Ping Jung
    • 2
  • Chin-Teng Lin
    • 3
  • Yufei Huang
    • 1
    Email author
  1. 1.Department of Electrical and Computer EngineeringUniversity of TexasSan AntonioUSA
  2. 2.Institute for Neural ComputationUniversity of CaliforniaSan DiegoUSA
  3. 3.Brain Research CenterNational Chiao Tung UniversityHsinchuTaiwan

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