nips nips2009 nips2009-237 nips2009-237-reference knowledge-graph by maker-knowledge-mining
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Author: Siamac Fazli, Cristian Grozea, Marton Danoczy, Benjamin Blankertz, Florin Popescu, Klaus-Robert Müller
Abstract: In the quest to make Brain Computer Interfacing (BCI) more usable, dry electrodes have emerged that get rid of the initial 30 minutes required for placing an electrode cap. Another time consuming step is the required individualized adaptation to the BCI user, which involves another 30 minutes calibration for assessing a subject’s brain signature. In this paper we aim to also remove this calibration proceedure from BCI setup time by means of machine learning. In particular, we harvest a large database of EEG BCI motor imagination recordings (83 subjects) for constructing a library of subject-specific spatio-temporal filters and derive a subject independent BCI classifier. Our offline results indicate that BCI-na¨ve ı users could start real-time BCI use with no prior calibration at only a very moderate performance loss.
[1] N. Birbaumer, N. Ghanayim, T. Hinterberger, I. Iversen, B. Kotchoubey, A. K¨ bler, J. Perelmouter, u E. Taub, and H. Flor. A spelling device for the paralysed. Nature, 398:297–298, 1999.
[2] B. Blankertz, G. Curio, and K.-R. M¨ ller. Classifying single trial EEG: Towards brain computer interu facing. In T. G. Diettrich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Inf. Proc. Systems (NIPS 01), volume 14, pages 157–164, 2002.
[3] B. Blankertz, G. Dornhege, M. Krauledat, K.-R. M¨ ller, V. Kunzmann, F. Losch, and G. Curio. The Berlin u Brain-Computer Interface: EEG-based communication without subject training. IEEE Trans Neural Syst Rehabil Eng, 14:147–152, 2006.
[4] B. Blankertz, G. Dornhege, S. Lemm, M. Krauledat, G. Curio, and K.-R. M¨ ller. The Berlin Brainu Computer Interface: Machine learning based detection of user specific brain states. Journal of Universal Computer Science, 12:2006, 2006.
[5] B. Blankertz, G. Dornhege, C. Sch¨ fer, R. Krepki, J. Kohlmorgen, K.-R. M¨ ller, V. Kunzmann, F. Losch, a u and G. Curio. Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis. IEEE Trans. Neural Sys. Rehab. Eng., 11(2):127–131, 2003.
[6] B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K.-R. M¨ ller. Optimizing spatial filters for robust u EEG single-trial analysis. IEEE Signal Proc Magazine, 25(1):41–56, 2008.
[7] Benjamin Blankertz and Carmen Vidaurre. Towards a cure for bci illiteracy: Machine-learning based co-adaptive learning. BMC Neuroscience, 10, 2009.
[8] R. Boostani and M. H. Moradi. A new approach in the BCI research based on fractal dimension as feature and adaboost as classifier. J. Neural Eng., 1:212–217, 2004.
[9] G. Dornhege, J.del R. Mill´ n, T. Hinterberger, D. McFarland, and K.-R. M¨ ller, editors. Toward Braina u Computer Interfacing. Cambridge, MA: MIT Press, 2007.
[10] T. Elbert, B. Rockstroh, W. Lutzenberger, and N. Birbaumer. Biofeedback of slow cortial potentials. I. Electroencephalogr. Clin. Neurophysiol., 48:293–301, 1980.
[11] M. Grant and S. Boyd. CVX: Matlab software for disciplined convex programming (web page and software). http://stanford.edu/ boyd/cvx, 2008.
[12] Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning, Second Edition: Data Mining, Inference, and Prediction (Springer Series in Statistics). Springer New York, 2 edition, 2001.
[13] Z.J. Koles and A. C. K. Soong. EEG source localization: implementing the spatio-temporal decomposition approach. Electroencephalogr. Clin Neurophysiol, 107:343–352, 1998.
[14] M. Krauledat, M. Schr¨ der, B. Blankertz, and K.-R. M¨ ller. Reducing calibration time for brain-computer o u interfaces: A clustering approach. In B. Sch¨ lkopf, J. Platt, and T. Hoffman, editors, Advances in Neural o Inf. Proc. Systems (NIPS 07), volume 19, pages 753–760, 2007.
[15] M. Krauledat, P. Shenoy, B. Blankertz, R.P.N. Rao, and K.-R. M¨ ller. Adaptation in csp-based BCI u systems. In Toward Brain-Computer Interfacing, pages 305–309. MIT Press, 2007.
[16] M. Krauledat, M. Tangermann, B. Blankertz, and K.-R. M¨ ller. Towards zero training for brain-computer u interfacing. PLoS ONE, 3:e2967, 2008.
[17] R. Polikar. Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3):21– 45, 2006.
[18] F. Popescu, S. Fazli, Y. Badower, B. Blankertz, and K.-R. M¨ ller. Single trial classification of motor u imagination using 6 dry EEG electrodes. PLoS ONE, 2:e637, 2007.
[19] H. Ramoser, J. M¨ ller-Gerkin, and G. Pfurtscheller. Optimal spatial filtering of single trial EEG during u imagined hand movement. IEEE Trans. Rehab. Eng, 8(4):441–446, 2000.
[20] P. Shenoy, M. Krauledat, B. Blankertz, R.P.N. Rao, and K.-R. M¨ ller. Towards adaptive classification for u BCI. Journal of Neural Engineering, 3(1):R13–R23, 2006.
[21] S. Wang, Z. Lin, and C. Zhang. Network boosting for BCI applications. Book Series Lecture Notes in Computer Science, 3735:386–388, 2005. 9