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Computational and Mathematical Methods in Medicine Volume 2018 ,2018-10-02
Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI
Research Article
Zhihua Huang 1 Minghong Li 2 Yuanye Ma 3
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DOI:10.1155/2018/4089021
Received 2018-05-17, accepted for publication 2018-09-04, Published 2018-09-04
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摘要

This work is intended to increase the classification accuracy of single EEG epoch, reduce the number of repeated stimuli, and improve the information transfer rate (ITR) of P300 Speller. Target EEG epochs and nontarget EEG ones are both mapped to a space by Wavelet. In this space, Fisher Criterion is used to measure the difference between target and nontarget ones. Only a few Daubechies wavelet bases corresponding to big differences are selected to construct a matrix, by which EEG epochs are transformed to feature vectors. To ensure the online experiments, the computation tasks are distributed to several computers that are managed and integrated by Storm so that they could be parallelly carried out. The proposed feature extraction was compared with the typical methods by testing its performance of classifying single EEG epoch and detecting characters. Our method achieved higher accuracies of classification and detection. The ITRs also reflected the superiority of our method. The parallel computing scheme of our method was deployed on a small scale Storm cluster containing three desktop computers. The average feedback time for one round of EEG epochs was 1.57 ms. The proposed method can improve the performance of P300 Speller BCI. Its parallel computing scheme is able to support fast feedback required by online experiments. The number of repeated stimuli can be significantly reduced by our method. The parallel computing scheme not only supports our wavelet feature extraction but also provides a framework for other algorithms developed for P300 Speller.

授权许可

Copyright © 2018 Zhihua Huang et al. 2018
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

通讯作者

Zhihua Huang.College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China, fzu.edu.cn.hzh@fzu.edu.cn

推荐引用方式

Zhihua Huang,Minghong Li,Yuanye Ma. Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI. Computational and Mathematical Methods in Medicine ,Vol.2018(2018)

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参考文献
[1] D. B. Percival, A. T. Walden. (2006). Wavelet Methods for Time Series Analysis. DOI: 10.1016/0013-4694(88)90149-6.
[2] B. Graiman, B. Allison, G. Pfurtscheller. (2010). Brain-Computer Interfaces: A Gentle Introduction in Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction. DOI: 10.1016/0013-4694(88)90149-6.
[3] G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer. et al.(2004). BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering.51(6):1034-1043. DOI: 10.1016/0013-4694(88)90149-6.
[4] V. J. Samar, K. P. Swartz, M. R. Raghuveer. (1995). Multiresolution analysis of event-related potentials by wavelet decomposition. Brain and Cognition.27(3):398-438. DOI: 10.1016/0013-4694(88)90149-6.
[5] J. R. Wolpaw, E. W. Wolpaw. (2012). Brain-Computer Interfaces: Principles and Practice. DOI: 10.1016/0013-4694(88)90149-6.
[6] B. Rivet, A. Souloumiac, V. Attina, G. Gibert. et al.(2009). xDAWN algorithm to enhance evoked potentials: application to brain–computer interface. IEEE Transactions on Biomedical Engineering.56(8):2035-2043. DOI: 10.1016/0013-4694(88)90149-6.
[7] W. Huang, Z. Huang. A real-time distributed computing mechanism for P300 speller BCI. . DOI: 10.1016/0013-4694(88)90149-6.
[8] A. K. Aniyan, N. S. Philip, V. J. Samar, J. A. Desjardins. et al.(2014). A wavelet based algorithm for the identification of oscillatory event-related potential components. Journal of Neuroscience Methods.233:63-72. DOI: 10.1016/0013-4694(88)90149-6.
[9] V. J. Samar, A. Bopardikar, R. Rao, K. Swartz. et al.(1999). Wavelet analysis of neuroelectric waveforms: a conceptual tutorial. Brain and Language.66(1):7-60. DOI: 10.1016/0013-4694(88)90149-6.
[10] Z. Huang, H. Zheng. Combining AR filter and sparse wavelet representation for P300 speller. . DOI: 10.1016/0013-4694(88)90149-6.
[11] S. Guo, S. Lin, Z. Huang. Feature extraction of P300s in EEG signal with discrete wavelet transform and fisher criterion. . DOI: 10.1016/0013-4694(88)90149-6.
[12] Q. Li, S. Liu, J. Li, O. Bai. et al.(2015). Use of a green familiar faces paradigm improves p300-speller brain-computer interface performance. PLoS One.10(6). DOI: 10.1016/0013-4694(88)90149-6.
[13] S. Halder, A. Pinegger, I. Kathner. (2015). Brain-controlled applications using dynamic P300 speller matrices. Artificial Intelligence in Medicine.63(1):7-17. DOI: 10.1016/0013-4694(88)90149-6.
[14] T. Krumpe, C. Walter, W. Rosenstiel, M. Spuler. et al.(2016). Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task. Journal of Neural Engineering.13(4). DOI: 10.1016/0013-4694(88)90149-6.
[15] N. Robinson, A. P. Vinod, K. K. Ang, K. P. Tee. et al.(2013). EEG-based classification of fast and slow hand movements using wavelet-CSP algorithm. IEEE Transactions on Biomedical Engineering.60(8):2123-2132. DOI: 10.1016/0013-4694(88)90149-6.
[16] B. Perseh, A. R. Sharafat. (2012). An efficient P300-based BCI using wavelet features and IBPSO-based channel selection. Journal of Medical Signals and Sensors.2(3):128-143. DOI: 10.1016/0013-4694(88)90149-6.
[17] A. Toshniwal, S. Taneja, A. Shukla. Storm@twitter. . DOI: 10.1016/0013-4694(88)90149-6.
[18] T. Demiralp, A. Ademoglu, M. Schrmann, C. Basar-Eroglu. et al.(1999). Detection of P300 waves in single trials by the wavelet transform (WT). Brain and Language.66(1):108-128. DOI: 10.1016/0013-4694(88)90149-6.
[19] X. Mao, M. Li, W. Li. (2017). Progress in EEG-based brain robot interaction systems. Computational Intelligence and Neuroscience.2017-25. DOI: 10.1016/0013-4694(88)90149-6.
[20] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller. et al.(2002). Brain computer interfaces for communication and control. Clinical Neurophysiology.113(6):767-791. DOI: 10.1016/0013-4694(88)90149-6.
[21] Z. Huang. (2013). A MapReduce computation model for brain-computer interface. Journal of Fuzhou University.41(6):981-985. DOI: 10.1016/0013-4694(88)90149-6.
[22] R. C. Panicker, S. Puthusserypady, Y. Sun. (2011). An asynchronous P300 BCI With SSVEP-based control state detection. IEEE Transactions on Biomedical Engineering.58(6):1781-1788. DOI: 10.1016/0013-4694(88)90149-6.
[23] T. Demiralp, A. Ademoglu, Y. Istefanopulos, C. Basar-Eroglu. et al.(2001). Wavelet analysis of oddball P300. International Journal of Psychophysiology.39(2-3):221-227. DOI: 10.1016/0013-4694(88)90149-6.
[24] R. O. Duda, P. E. Hart, D. G. Stork. (2001). Pattern Classification. DOI: 10.1016/0013-4694(88)90149-6.
[25] J. Jin, H. Zhang, I. Daly, X. Wang. et al.(2017). An improved P300 pattern in BCI to catch users attention. Journal of Neural Engineering.14(3). DOI: 10.1016/0013-4694(88)90149-6.
[26] F. Lotte, L. Bougrain, A. Cichocki. (2018). A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. Journal of Neural Engineering.15(3). DOI: 10.1016/0013-4694(88)90149-6.
[27] C. Li, J. Zhang, Y. Luo. (2017). Real-time scheduling based on optimized topology and communication traffic in distributed real-time computation platform of storm. Journal of Network and Computer Applications.87:100-115. DOI: 10.1016/0013-4694(88)90149-6.
[28] T. Fawcett. (2006). An introduction to ROC analysis. Pattern Recognition Letters.27(8):861-874. DOI: 10.1016/0013-4694(88)90149-6.
[29] L. A. Farwell, E. Donchin. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology.70(6):510-523. DOI: 10.1016/0013-4694(88)90149-6.
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