International Journal of Fuzzy Systems

, Volume 19, Issue 2, pp 566–579 | Cite as

EEG Classification of Imaginary Lower Limb Stepping Movements Based on Fuzzy Support Vector Machine with Kernel-Induced Membership Function

  • Wei-Chun Hsu
  • Li-Fong Lin
  • Chun-Wei Chou
  • Yu-Tsung Hsiao
  • Yi-Hung LiuEmail author


Although various kinds of motor imageries have been used for BCI applications, imaginary lower limb stepping movement has not been studied yet. The purpose of this study is to investigate the possibilities of using electroencephalography (EEG) signal to classify imaginary lower limb stepping movements and to design a robust motor imagery classifier based on support vector machine (SVM). A cue-based experimental paradigm is designed to record nine-channel EEG associated with imaginary left leg stepping (L-stepping) and right leg stepping (R-stepping) movements from eight healthy subjects. Features including band powers (BPs), common spatial pattern (CSP), and a filter-bank CSP (FB-CSP) were extracted from the recorded EEG. Fuzzy SVM (FSVM) is introduced to this study to classify L-stepping and R-stepping imageries. We propose a novel kernel-induced membership function to address the issue of data relative importance assignment. The FSVM with the membership function suggested in the original work of FSVM (Type-I FSVM) and the FSVM with the one we proposed (Type-II FSVM) is compared. Results indicated that the classification accuracies based on BP features are near the chance level (~50 %). Both alpha-band CSP (71.25 %) and FB-CSP (75.63 %) gave acceptable results as a simple k-NN classifier is performed. Results show that both types of FSVM performed better than the conventional SVM. Also, Type-II FSVM outperforms Type-I FSVM, especially when the alpha-CSP feature is employed, where the improvement in error reduction rate is over 15 %. The highest average L-stepping versus R-stepping classification accuracy over the eight subjects is achieved (86.25 % in single-trial analysis) by FB-CSP and FSVM-II. The high classification result suggests the feasibility of using lower limb stepping imagery to develop a BCI that can control devices or might be able to serve as a neurofeedback tool for users who need lower limb stepping imagery training for gait function improvement.


EEG Brain–computer interface Motor imagery Lower limb stepping Support vector machine Common spatial pattern 



This work was supported by the Ministry of Science and Technology (MOST), Taiwan, under Grant No. 103-2923-E-027-001-MY3.


  1. 1.
    Schaechter, J.D.: Motor rehabilitation and brain plasticity after hemiparetic stroke. Progr. Neurobiol. 73(1), 61–72 (2004)CrossRefGoogle Scholar
  2. 2.
    Flansbjer, U.B., Holmbäck, A.M., Downham, D., Patten, C., Lexell, J.: Reliability of gait performance tests in men and women with hemiparesis after stroke. J. Rehabil. Med. 35(2), 75–82 (2005)Google Scholar
  3. 3.
    Pollock, A., Baer, G., Pomeroy, V. M., Langhorne, P.: Physiotherapy treatment approaches for the recovery of postural control and lower limb function following stroke. Cochrane Database Syst. Rev. 21, 395–410 (2007)Google Scholar
  4. 4.
    Malfait, B., Staes, F., de Vries, A., Smeets, A., Hawken, M.: Robinson dynamic neuromuscular control of the lower limbs in response to unexpected single-planner versus multi-planner support perturbations in young, active adults. PLoS One (2015). doi: 10.1371/journal.pone.0133147 Google Scholar
  5. 5.
    McFadyen, B.J., Carnahan, H.: Anticipatory locomotor adjustments for accommodating versus avoiding level changes in humans. Exp. Brain Res. 114(3), 500–506 (1997)CrossRefGoogle Scholar
  6. 6.
    Ietswaart, M., Johnston, M., Dijkerman, H.C., Joice, S., Scott, C.L., MacWalter, R.S., et al.: Mental practice with motor imagery in stroke recovery: randomized controlled trial of efficacy. Brain 134(5), 1373–1386 (2011). doi: 10.1093/brain/awr077. PMID: 21515905 CrossRefGoogle Scholar
  7. 7.
    Page, S.J., Levine, P., Leonard, A.: Mental practice in chronic stroke: results of a randomized, placebo-controlled trial. Stroke 38, 1293–1297 (2007)CrossRefGoogle Scholar
  8. 8.
    Dickstein, R., Deutsch, J.E.: Motor imagery in physical therapist practice. Phys. Ther. 87(7), 87–942 (2007)CrossRefGoogle Scholar
  9. 9.
    Dickstein, R., Dunsky, A., Marcovitz, E.: Motor imagery for gait rehabilitation in post-stroke hemiparesis. Phys. Ther. 84(12), 84–1167 (2004)Google Scholar
  10. 10.
    Malouin, F., Richards, C.L.: Mental practice for relearning locomotor skills. Phys. Ther. 90(2), 240–251 (2010)CrossRefGoogle Scholar
  11. 11.
    Pressaco, A., Forrester, L., Contreras Vidal, J.L.: Towards a non-invasive brain-machine interface system to restore gait function in humans. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4588–4591. Boston (2011)Google Scholar
  12. 12.
    Belda-Lois, J.M., et al.: Rehabilitation of gait after stroke: a review towards a top-down approach. J. Neuroeng. Rehabil. 8(1), 66 (2011)CrossRefGoogle Scholar
  13. 13.
    Broetz, D., Braun, C., Weber, C., Soekadar, S.R., Caria, A., Birbaumer, N.: Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report. Neurorehabil. Neural Repair 24(7), 674–679 (2010)CrossRefGoogle Scholar
  14. 14.
    Caria, A., Weber, C., Brötz, D., Ramos, A., Ticini, L.F., Gharabaghi, A., Braun, C., Birbaumer, N.: Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology 48(4), 578–582 (2010)CrossRefGoogle Scholar
  15. 15.
    Buch, E., Weber, C., Cohen, L. G., Braun, C., Dimyan, M.A., Ard, T., Mellinger, J., Caria, A., Soekadar, S., Fourkas, A., Birbaumer, N.: Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 39, 910–917 (2008)Google Scholar
  16. 16.
    Varkuti, B.: Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil. Neural Repair 27(1), 53–62 (2013)CrossRefGoogle Scholar
  17. 17.
    Xu, R., Jiang, N., Mrachacz-Kersting, N., Lin, C., Prieto, G.A., Moreno, J.C., Pons, J.L., Dremstrup, K., Farina, D.: A closed-loop brain–computer interface triggering an active ankle–foot orthosis for inducing cortical neural plasticity. IEEE Trans. Biomed. Eng. 61(7), 2092–2101 (2014)CrossRefGoogle Scholar
  18. 18.
    Fatourechi, M., Ward, R.K., Birch, G.E.: A self-paced brain-computer interface system with a low false positive rate. J. Neural Eng. 5, 9–23 (2008)CrossRefGoogle Scholar
  19. 19.
    Liao, K., Xiao, R., Conzalez, J., Ding, L.: Decoding individuals finger movements from one hand using human EEG signals. PLos One (2014). doi: 10.1371/journal.pone.0085192 Google Scholar
  20. 20.
    Yang, B., Li, H., Wang, Q., Zhang, Y.: Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces. Comput. Methods Progr. Biomed. 129, 21–28 (2016)CrossRefGoogle Scholar
  21. 21.
    Ghani, F., Sultan, H., Anwar, D., Farooq, O., Khan, Y.U.: Classification of wrist movements using EEG signals. J. Next Gener. Inf. Technol. 4, 29–39 (2013)CrossRefGoogle Scholar
  22. 22.
    Yong, X., Menon, C.: EEG classification of different imaginary movements with the same limb. PLoS One (2015). doi: 10.1371/journal.pone.0121896 Google Scholar
  23. 23.
    Pfurtscheller, G.: Functional brain imaging based on ERD/ERS. Vis. Res. 41, 1257–1260 (2001)CrossRefGoogle Scholar
  24. 24.
    Chae, Y., Jeong, J., Jo, S.: Toward brain-actuated humanoid robots: asynchronous direct control using an EEG-based BCI. IEEE Trans. Robot. 28, 1131–1144 (2012)CrossRefGoogle Scholar
  25. 25.
    Pfurtscheller, G., Guger, C., Müller, G., Krausz, G., Neuper, C.: Brain oscillations control hand orthosis in a tetraplegic. Neurosci. Lett. 292(3), 211–214 (2000)CrossRefGoogle Scholar
  26. 26.
    Qian, K., Nikolov, P., Huang, D., Fei, D.Y., Chen, X., Bai, O.: A motor imagery-based online interactive brain-controlled switch: paradigm development and preliminary test. Clin. Neurophysiol. 121, 1304–1313 (2010)CrossRefGoogle Scholar
  27. 27.
    Leeb, R., Lancelle, M., Kaiser, V., Fellner, D.W., Pfurtscheller, G.: Thinking penguin: multi-modal brain-computer interface control of a VR game. IEEE Trans. Comput. Intell. AI Games 5(2), 117–128 (2013)CrossRefGoogle Scholar
  28. 28.
    Pfurtscheller, G., Neuper, C., Andrew, C., Edlinger, G.: Foot and hand area mu rhythms. Int. J. Psychophysiol. 26, 121–135 (1997)CrossRefGoogle Scholar
  29. 29.
    Pfurtscheller, G., Brunner, C., Schlögl, A., da Silva, F.H.: Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31, 153–159 (2006)CrossRefGoogle Scholar
  30. 30.
    Hashimoto, Y., Ushiba, J.: EEG-based classification of imaginary left and right foot movements using beta rebound. Clin. Neurophysiol. 124, 2153–2160 (2013)CrossRefGoogle Scholar
  31. 31.
    Müller-Putz, G.R., Kaiser, V., Solis-Escalante, T., Pfurtscheller, G.: Fast set-up asynchronous brain-switch based on detection of foot motor imagery in 1-channel EEG. Med. Biol. Eng. Comput. 48, 229–233 (2010)CrossRefGoogle Scholar
  32. 32.
    Niazi, I.K., Jiang, N., Tiberghien, O., Nielsen, J.F., Dremstrup, K., Farina, D.: Detection of movement intention from single-trial movement-related cortical potentials. J. Neural Eng. (2011). doi: 10.1088/1741-2560/8/6/066009 Google Scholar
  33. 33.
    Penfieled, W., Boldrey, E.: Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain 60(4), 389–443 (1937)CrossRefGoogle Scholar
  34. 34.
    Stippich, C., Ochmann, H., Sartor, K.: Somatotopic mapping of the human primary sensorimotor cortex during motor imagery and motor execution by functional magnetic resonance imaging. Neurosci. Lett. 331, 50–54 (2002)CrossRefGoogle Scholar
  35. 35.
    Jaeger, L., Marchal-Crespo, L., Wolf, P., Riener, R., Michels, L., Kollias, S.: Brain activations associated with active and passive lower limb stepping. Front. Hum. Neurosci. (2014). doi: 10.3389/fnhum.2014.00828 Google Scholar
  36. 36.
    Saladin, K.: Anatomy and Physiology: The Unity of Form and Function. McGraw Hill, New York (2007)Google Scholar
  37. 37.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  38. 38.
    Pfurtscheller, G., Solis-Escalante, T.: Could the beta rebound in the EEG be suitable to realize a brain switch? Clin. Neurophysiol. 120, 24–29 (2009)CrossRefGoogle Scholar
  39. 39.
    Rakotomamonjy, A., Guigue, V.: BCI competition III: dataset II- ensemble of SVMs for BCI p300 speller. IEEE Trans. Biomed. Eng. 55, 1147–1154 (2008)CrossRefGoogle Scholar
  40. 40.
    Liu, Y.H., Wang, S.H., Hu, M.R.: A self-paced P300 healthcare brain-computer interface system with SSVEP-based switching control and kernel FDA + SVM-based detector. Appl. Sci. (2016). doi: 10.3390/app6050142 Google Scholar
  41. 41.
    Liu, Y.H., Wu, C.T., Cheng, W.T., Hsiao, Y.T., Chen, P.M., Teng, J.T.: Emotion recognition from single trial EEG based on kernel Fisher’s emotion pattern and imbalanced quasiconformal kernel support vector machine. Sensors 14(8), 13361–13388 (2014)CrossRefGoogle Scholar
  42. 42.
    Lin, C.F., Wang, S.D.: Fuzzy support vector machines. IEEE Trans. Neural Netw 13(2), 464–471 (2002)CrossRefGoogle Scholar
  43. 43.
    Liu, Y.H., Chen, Y.T.: Face recognition using total margin-based adaptive fuzzy support vector machines. IEEE Trans. Neural Netw. 18, 178–192 (2007)CrossRefGoogle Scholar
  44. 44.
    Abe, S.: Fuzzy support vector machines for multilabel classification. Pattern Recognit. 48(6), 2110–2117 (2015)CrossRefGoogle Scholar
  45. 45.
    Burges, C.J.C.: Geometry and invariance in kernel based methods. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods—Support Vector Learning, pp. 89–116. MIT Press, Cambridge (1999)Google Scholar
  46. 46.
    Liu, Y.H., Liu, Y.C., Chen, Y.J.: Fast support vector data descriptions for novelty detection. IEEE Trans. Neural Netw. 21, 1296–1313 (2010)CrossRefGoogle Scholar
  47. 47.
    Müller-Gerking, J., Pfurtscheller, G., Flyvbjerg, H.: Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin. Neurophysiol. 110, 787–798 (1999)CrossRefGoogle Scholar
  48. 48.
    Ramoser, H., Müller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng. 8(4), 441–446 (2000)CrossRefGoogle Scholar
  49. 49.
    Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Müller, K.-R.: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process. Mag. 25, 41–56 (2008)CrossRefGoogle Scholar
  50. 50.
    Zheng Yang, C., Kai Keng, A., Chuanchu, W., Cuntai, G., Haihong, Z.: Multi-class filter bank common spatial pattern for four-class motor imagery BCI. In: Proceedings of 31st Annual International Conference IEEE EMBC, pp. 571–574 (2009)Google Scholar
  51. 51.
    Gonzalez, A., Nambu, I., Hokari, H., Wada, Y.: EEG channel selection using particle swarm optimization for the classification of auditory event-related potentials. Sci. World J. 2014, 350270 (2014)Google Scholar

Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Institute of Biomedical EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan
  2. 2.Department of Physical Medicine and Rehabilitation, Shuang Ho HospitalTaipei Medical UniversityTaipeiTaiwan
  3. 3.Institute of Gerontology and Health ManagementTaipei Medical UniversityTaipeiTaiwan
  4. 4.Department of Mechanical EngineeringChung Yuan Christian UniversityChungliTaiwan
  5. 5.Institute of Mechatronic EngineeringNational Taipei University of TechnologyTaipeiTaiwan
  6. 6.Department of Mechanical EngineeringNational Taipei University of TechnologyTaipeiTaiwan

Personalised recommendations