Fast BCI Calibration - Comparing Methods to Adapt BCI Systems for New Subjects

Jean Thorey, Parvaneh Adibpour, Yohei Tomita, Antoine Gaume, Hovagim Bakardjian, Gérard Dreyfus, François-B. Vialatte

2012

Abstract

A Brain Computer Interface (BCI) is a system where a direct connection is established between the brain and a computer, providing a subject with a new communication channel. Unfortunately, BCI have many drawbacks: signal recording is problematic, brain signatures are non reproducible from individual to individual, etc. A dependent-BCI prototype, the BrainPC project, was developed in the SIGMA laboratory. Electroencephalographic (EEG) signals collected by a BrainAmp amplifier in responses to flickering light stimuli (Steady State Visual Evoked Potentials) are converted into machine-readable commands. This system is coupled with a human-machine interface. We propose a solution for fast calibration of the automatic detection of SSVEP between experimental subjects. We tested different calibration methods; harmonic and electrode selections were shown to be the most efficient methods.

References

  1. Bakardjian, H., Tanaka, T., Cichocki, A. 2010, Optimization of SSVEP brain responses with application to eight-command Brain-Computer Interface. Neuroscience Letters, 469(1):34-38.
  2. Bin, G., Gao, X., Yan, Z., Hong, B., Gao, S., 2009. An online multi-channel SSVEP based brain-computer interface using a canonical correlation analysis method. J. Neural Eng. 6 (4) 6: 046002
  3. Friman, O., Volosyak, I., Graser, A., 2007. Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces. IEEE Trans. Biomed. Eng. 54 (4), 742-750.
  4. Kaplan A. Y., Fingelkurts A. A., Fingelkurts A. A., Borisov S. V., Darkhovsky B. S., 2005, Nonstationary nature of the brain activity as revealed by EEG/MEG: Methodological, practical and conceptual challenges. Signal Processing, 85(11):2190-2212.
  5. Krauledat, M., Tangermann, M., Blankertz, B., Muller, K. R., 2008. Towards zero training for brain-computer interfacing. PLoS ONE 3(8), e2967.
  6. Lin, Z., Zhang, C., Wu, W., Gao, X., 2006. Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs. IEEE Trans. Biomed Eng. 532610-4.
  7. Lotte, F., 2011. Generating Artificial EEG Signals To Reduce BCI Calibration Time, 5th International Brain-Computer Interface Workshop
  8. Nicolelis M., 2011, Beyond Boundaries: The New Neuroscience of Connecting Brains With MachinesAnd How It Will Change Our Lives. Times books.
  9. Parini, S., Maggi, L., Turconi, A. C., and Andreoni, G., 2009. A Robust and SelfPaced BCI System Based on a Four Class SSVEP Paradigm: Algorithms and Protocols for a High-Transfer-Rate Direct Brain Communication. Comput.Intelli. Neurosci ID 864564.
  10. Silberstein, R. B., Schier, M. A., Pipingas, A., Ciorciari, J., Wood, S. R., Simpson, D. G., 1990. Steady-state Visually evoked potential topography associated with a visual vigilance task. Brain Topogr. 3, 337-347.
  11. Shishkin S. L., Nikolaev A. A., Nuzhdin Y. O., Zhigalov A. Y., Ganin I. P., Kaplan A., Y. 2011. Calibration of the P300 BCI with the single-stimulus protocol. Proc. of the Fifth International BCI Conference Graz University of Technology, Austria. 256-259
  12. Tomita, Y., Gaume, A., Bakardijian, H., Maurice, M., Cichocki, A., Yamaguchi, Y., Drefus, G., Vialatte, F., 2011. Concatenation Method for high temporal resolution SSVEP-BCI. International Conference on Neural Computation Theory and Applications.
  13. Vialatte, F. B., Maurice, M., Dauwels, J., Cichocki, A., 2010. Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives, Progress in Neurobiology 90 418-438
  14. Volosyak, I., Malechka, T., Valbuenaand, D., Graser, A., 2010. A Novel Calibration Method for SSVEPBased Brain Computer Interfaces. 18th European Signal Processing Conference, 939-943.
  15. Wang, Y., Wang, R., Gao, X., Hong, B., Gao, S., 2006. A practical VEP-based brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 14 (2), 234-239.
  16. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., Vaughan, T. M., 2002. Braincomputer interfaces for communication and control. Clin. Neurophysiol. 113 (6), 767-791.
  17. Zhang, Y., Jin, J., Qing, X., Wang, B., Wang, X., 2012. LASSO based stimulus frequency recognition model for SSVEP BCIs, Biomedical Signal Processing and Control (7)104- 111
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Paper Citation


in Harvard Style

Thorey J., Adibpour P., Tomita Y., Gaume A., Bakardjian H., Dreyfus G. and Vialatte F. (2012). Fast BCI Calibration - Comparing Methods to Adapt BCI Systems for New Subjects . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: SSCN, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 663-669. DOI: 10.5220/0004180806630669


in Bibtex Style

@conference{sscn12,
author={Jean Thorey and Parvaneh Adibpour and Yohei Tomita and Antoine Gaume and Hovagim Bakardjian and Gérard Dreyfus and François-B. Vialatte},
title={Fast BCI Calibration - Comparing Methods to Adapt BCI Systems for New Subjects},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: SSCN, (IJCCI 2012)},
year={2012},
pages={663-669},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004180806630669},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: SSCN, (IJCCI 2012)
TI - Fast BCI Calibration - Comparing Methods to Adapt BCI Systems for New Subjects
SN - 978-989-8565-33-4
AU - Thorey J.
AU - Adibpour P.
AU - Tomita Y.
AU - Gaume A.
AU - Bakardjian H.
AU - Dreyfus G.
AU - Vialatte F.
PY - 2012
SP - 663
EP - 669
DO - 10.5220/0004180806630669