Multiclass classification of motor imagery EEG signals using ensemble classifiers & cross-correlation

  • Authors

    • D Hari Krishna
    • I A.Pasha
    • T Satya Savithri
    2018-03-11
    https://doi.org/10.14419/ijet.v7i2.6.10144
  • Brain computer interfacing (BCI), Cross correlation, EEG classification, Ensemble, Motor Imagery (MI)

  • To communicate without any muscle movement and purely based on brain signal has been the goal of Brain computer interfacing (BCI). Recent BCI based studies reported more and more accurate detection of brain states. This paper proposes a study that detects EEG signal belonging todifferent imaginary motor activities (Right leg, right hand, left leg and left hand). The Electroencephalogram (EEG) signal has been conditioned by band pass filter (BPF) to improve signal to noise ratio (SNR). The proposed method is based on similarity between signals to extract features. For measuring the similarity between signals, Cross correlation (CC) is used. An ensemble set of five classifiers (Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes (NB) and Binary Decision Tree) was used collectively.  As the similarity measurement was binary in nature, one versus rest (OVR) approach was used for multi class classification. Random subset of features was used to train the ensemble of classifiers. The classification label was obtained by using majority voting. An average accuracy of 89.57% was observed among all 10 subjects.

  • References

    1. [1] S. Sanei and J.A. Chambers, EEG Signal Processing, Wiley, 2007

      [2] S. Bhattacharyya, A. Khasnobish, A. Konar, D.N. Tibarewala, A.K. Nagar, Performance analysis of left/right hand movement classification from EEG signal by intelligent algorithms, Computational Intelligence,Cognitive Algorithms, Mind, and Brain (CCMB), 2011 IEEE Symposium on , vol., no., pp.1,8, 11-15 April 2011

      [3] B. Blankertz, K. R. Muller, G. Curio, T. M. Vaughan, G. Schalk, J. R. Wolpaw, A. Schlogl, C. Neuper, G. Pfurtscheller, T. Hinterberger, M.Schroder, and N. Birbaumer, The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials, IEEE Transactions on Biomedical Engineering, vol. 51, pp. 1044-1051, 2004.

      [4] B. Xu, A. Song, J. Wu, Algorithm of Imagined Left-Right Hand Movement Classification Based on Wavelet Transform and AR Parameter Mode, Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on , pp.539,542, 6-8 July 2007

      [5] L. Ming-Ai, W. Rui, H. Dong-Mei, Y. Jin-Fu, Feature Extraction and Classification of Mental EEG for Motor Imagery, Natural Computation, 2009. ICNC ’09. Fifth International Conference on , vol.2, no.,pp.139,143, 14-16 Aug. 2009

      [6] C. Brunner, R. Scherer, B. Graimann, G. Supp, and G. Pfurtscheller, “Online control of a brain-computer interface using phase synchronization,â€IEEE Trans. Biomed. Engg., vol. 53, no. 12, pp. 2501-06, 2006

      [7] Y. Chae, J. Jeong, and S. Jo, “Toward brain-actuated humanoid robots: Asynchronous direct control using an EEG-based BCI,†IEEE Trans. Robotics, vol. 28, no. 5, pp. 1131-1144, 2012

      [8] S. Bhattacharyya, A. Sengupta, T. Chakraborti, D. Banerjee, A. Khasnobish, A. Konar, D.N. Tibarewala, R. Janarthanan, “EEG controlled remote robotic system from motor imagery classification,†In: 3rd Int.conf on Com., Comm. & Networking Tech. (ICCCNT) 2012, pp. 1-8, 26-28 July 2012

      [9] D. H. Krishna, I. A. Pasha and T. S. Savithri, "Autonomuos robot control based on EEG and cross-correlation," 2016 10th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, 2016, pp. 1-4.doi:10.1109/ISCO.2016.7727098

      [10] J. Long, Y. Li, H. Wang, T. Yu, J. Pan, and F. Li, “A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair,†IEEE Trans. Neural Sys. Rehab. Eng., vol. 20, no. 5,pp. 720-729, Sept. 2012

      [11] F. Galan, M. Nuttin, E. Lew, P.W. Ferrez, G. Vanacker, J. Philips, and J. del R. Millan, “A brain-actuated wheelchair: asynchronous and noninvasive brain-computer interfaces for continuous control of robots,†Clin. Neurophy., vol. 119, pp. 2159-2169, 2008

      [12] S. Sanei, and J.A. Chambers, EEG Signal Processing, John Wiley & Sons, 2008

      [13] G. Townsend, B. Graimann, and G. Pfurtscheller, “Continuous EEG classification during motor-imagery simulation of an asynchromous BCI,†IEEE Trans. Neural Sys. Rehab Engg., vol. 12, no. 2, pp. 258-265, 2004

      [14] S.G. Mason, and G.E. Birch, “A General Framework for Brain-Computer Interface Designâ€, IEEE Trans. Neural Sys. & Rehab. Eng.,vol. 11, no. 1, pp. 70-85, 2003

      [15] T. Yamaguchi, M. Fujio, K. Inoue, and G. Pfurtscheller, “Wavelet Analysis of EEG signals during Motor Imagery,†In: Int Conf. Wavelet Analysis & Pattern recog. 2008, pp. 454-59, Aug 2008

      [16] P. Herman, G. Prasad, T.M. McGinnity, and D. Coyle, “Comparative Analysis of Spectral Approaches to Feature Extraction for EEG based Motor Imagery Classification,†IEEE Trans. Neural Sys. Rehab. Engg.,vol. 16, no. 4, pp. 317-326, 2008

      [17] C. Vidaurre, N. Kramer, B. Blankertz, and G. Pfurtscheller, “Time Domain Parameters as a feature for EEG-based brain-computer interfaces,†Neural Networks, vol. 22, pp. 1313-1319, 2009

      [18] A. Schlogl, “Dynamic Spectral Analysis based on Auto-regressive model with time-varying coefficients,†IEEE-EMBC and CMBEC, 1995

      [19] Y. Fusheng, H. Bo, and T. Qingyu, Approximate entropy and its application to bio-signal analysis, In: Nonlinear Biomedical Signal Processing, Vol 2: Dynamic Analysis and Modeling, M.Akay, Ed., IEEE Press Series on Biomedical Engineering, NY (USA), pp. 72-92

      [20] E. Alpaydin, Introduction to machine learning, MIT Press, 2004

      [21] G. Townsend, B. Graimann, and G. Pfurtscheller, “Continuous EEG classification during motor-imagery simulation of an asynchromous BCI,†IEEE Trans. Neural Sys. Rehab Engg., vol. 12, no. 2, pp. 258-265,2004

      [22] S. Sun, C. Zhiang, D. Zhang, “An experimental evaluation of ensemble methods for EEG signal classification,†Pattern Recognition Letters, vol.28, pp. 2157-2163, 2007

      [23] P. Xiaomei, Z. Chongxun, X. Jin, and B. Guangyu, “Adaboost for improving classification of left and right hand motor imagery tasks,†In: 1st Int. Conf. on Neural Interface and Control, Wuhan, China, pp.10-13, May 2005

      [24] L. Breiman, “Bagging Predictors,†Machine Learning, vol. 24, no. 2, pp. 123-140, 1996

      [25] T.K. Ho, “The random subspace method for constructing decision forests,†IEEE Trans Pattern Analysis Mach. Int., vol. 20, no. 8, pp.832-844, 1998

      [26] F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche and B. Arnaldi, “A review of classification algorithms for EEG-based brain–computer interfacesâ€, Journal of Neural Engineering, Volume 4, 2007

      [27] A. Soria-Frisch, “A Critical Review on the Usage of Ensembles for BCI†in Towards Practical Brain-Computer Interfaces, 1st Edition, B. Z. Allison, Ed. Berlin: Springer Berlin Heidelberg, 2012, pp. 41-65

      [28] T. G. Dietterich, “Ensemble Methods in Machine Learning†in Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21-23, 2000 Proceedings, Ed. Berlin: Springer Berlin Heidelberg, 2000, pp. 1-15

      [29] L. Hansen and P. Salamon, “Neural Network Ensemblesâ€, IEEE Transactions on Patern Analysis and Machine Intelligence, Volume 12, 1990, pp. 993-1001

      [30] L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, Wiley-Interscience, 2004

      [31] A. J. Meng, G. Liu, G. Huang, and X. Zhu, “Automated selecting subset of channels based on CSP in motor imagery brain-computer system,†in Proc. 2009 IEEE Int. Conf. Robot. Bioinformat., Guilin, China, Dec. 19–23, 2009, pp. 2290–2294

      [32] Siuly, S., & Li, Y. (2012).Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 20(4), 526-538

      [33] Bhattacharyya, S., Konar, A., Tibarewala, D.N., Khasnobish, A. and Janarthanan, R., 2014, January.Performance analysis of ensemble methods for multi-class classification of motor imagery EEG signal.In Control, Instrumentation, Energy and Communication (CIEC), 2014 International Conference on (pp. 712-716)

      [34] Mahmood, A., Zainab, R., Ahmad, R.B., Saeed, M. and Kamboh, A.M., 2017, July. Classification of multi-class motor imagery EEG using four band common spatial pattern. In Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE (pp. 1034-1037)

      [35] Rakotomamonjy, A. and Guigue, V., 2008. BCI competition III: dataset II-ensemble of SVMs for BCI P300 speller. IEEE transactions on biomedical engineering, 55(3), pp.1147-1154

  • Downloads

  • How to Cite

    Hari Krishna, D., A.Pasha, I., & Satya Savithri, T. (2018). Multiclass classification of motor imagery EEG signals using ensemble classifiers & cross-correlation. International Journal of Engineering & Technology, 7(2.6), 163-167. https://doi.org/10.14419/ijet.v7i2.6.10144