Multimedia Tools and Applications

, Volume 77, Issue 16, pp 21305–21327 | Cite as

Comparison of EEG signal decomposition methods in classification of motor-imagery BCI

  • Eltaf Abdalsalam MohamedEmail author
  • Mohd Zuki Yusoff
  • Aamir Saeed Malik
  • Mohammad Rida Bahloul
  • Dalia Mahmoud Adam
  • Ibrahim Khalil Adam


A brain–computer interface (BCI) provides a link between the human brain and a computer. The task of discriminating four classes (left and right hands and feet) of motor imagery movements of a simple limb-based BCI is still challenging because most imaginary movements in the motor cortex have close spatial representations. We aimed to classify binary limb movements, rather than the direction of movement within one limb. We also investigated joint time-frequency methods to improve classification accuracies. Neither of these, to our knowledge, has been investigated previously in BCI. We recorded EEG data from eleven participants, and demonstrated the classification of four classes of simple-limb motor imagery with an accuracy of 91.46% using intrinsic time-scale decomposition and 88.99% using empirical mode decomposition. In binary classifications, we achieved average accuracies of 89.90% when classifying imaginary movements of left hand versus right hand, 93.1% for left hand versus right foot, 94.00% for left hand versus left foot, 83.82% for left foot versus right foot, 97.62% for right hand versus left foot, and 95.11% for right hand versus right foot. The results show that the binary classification performance is slightly better than that of four-class classification. Our results also show that there is no significant difference in terms of spatial distribution between left and right foot motor imagery movements. There is also no difference in classification performances involving left or right foot movement. This work demonstrates that binary and four-class movements of the left and right feet and hands can be classified using recorded EEG signals of the motor cortex, and an intrinsic time-scale decomposition (ITD) feature extraction method can be used for real time brain computer interface.


Brain–computer interface (BCI) Empirical mode decomposition (EMD) Electroencephalography (EEG) Intrinsic time-scale decomposition (ITD) Artificial neural network (ANN) 



The authors would like to thank the Universiti Teknologi PETRONAS for the Graduate Assistantship scheme (GA) given to the first author, the Ministry of Education Malaysia for providing Higher Institution Center of Excellence (HICoE) grant (cost center: 0153CA-004) and Centre for Intelligent Signal and Imaging Research (CISIR) for providing facilities and equipment. Also, we wish to thank the participants for their cooperation in the experiments.

Author contributions

EA developed the methodology and collected the data with the guidance of MZY and ASM. EA and IKA performed the analysis and drafted the manuscript. DM and MRB also participated in writing. MZY reviewed and proofread the manuscript. All the authors read and approved the manuscript.


  1. 1.
    Alkadhi H, Kollias SS, Crelier GR, Golay X, Hepp-Reymond MC, Valavanis A (2000) Plasticity of the Human Motor Cortex in Patients with Arteriovenous Malformations: A Functional MR Imaging Study. Neuroradiology 21:1423–1433Google Scholar
  2. 2.
    Aydemir O, Kayikcioglu T (2014) Decision tree structure based classification of EEG signals recorded during two dimensional cursor movement imagery. J Neurosci Methods 229:68–75CrossRefGoogle Scholar
  3. 3.
    Blankertz B, Dornhege G, Krauledat M, Muller KR, Curio G (2007) The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects. NeuroImage 37:539–550CrossRefGoogle Scholar
  4. 4.
    Brunner C, Naeem M, Leeb R, Graimann B, Pfurtscheller G (2007) Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis. Pattern Recogn Lett 28:957–964CrossRefGoogle Scholar
  5. 5.
    Galán F, Nuttin M, Lew E, Ferrez PW, Vanacker G, Philips J, Millán JR (2008) A brain-actuated wheelchair: asynchronous and non-invasive brain–computer interfaces for continuous control of robots. Clin Neurophysiol 119:2159–2169CrossRefGoogle Scholar
  6. 6.
    Galan F, Nuttin M, Lew E, Ferrez PW, Vanacker G, Philips J, Millan Jdel R (2008) A brain-actuated wheelchair: asynchronous and non-invasive Brain-computer interfaces for continuous control of robots. Clin Neurophysiol 119:2159–2169CrossRefGoogle Scholar
  7. 7.
    Gert Pfurtscheller CN (1997) Motor imagery activates primary sensorimotor area in humans. Neurosci Lett 239:65–68CrossRefGoogle Scholar
  8. 8.
    Guo Z, Xie L, Ye T, Horch A (2014) Online detection of time-variant oscillations based on improved ITD. Control Eng Pract 32:64–72CrossRefGoogle Scholar
  9. 9.
    Hari R, Forss N, Avikainen S, Kirveskari E, Salenius S, Rizzolatti G (1998) Activation of human primary motor cortex during action observation: a neuromagnetic study. Proc Natl Acad Sci 95:15061–15065CrossRefGoogle Scholar
  10. 10.
    Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung C-C, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. R Soc 454:903–995MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Huang D, Qian K, Fei D-Y, Jia W, Chen X, Bai O (2012) Electroencephalography (EEG)-based brain–computer interface (BCI): A 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control. IEEE Trans Neural Syst Rehabil Eng 20:379–388CrossRefGoogle Scholar
  12. 12.
    Jeon Y, Nam CS, Kim Y-J, Whang MC (2011) Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: Implications for brain–computer interfaces. Int J Ind Ergon 41:428–436CrossRefGoogle Scholar
  13. 13.
    Kevric J, Subasi A (2017) Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomed Signal Process Control 31:398–406CrossRefGoogle Scholar
  14. 14.
    Krepki R, Blankertz B, Curio G, Müller K-R (2007) The Berlin Brain-Computer Interface (BBCI)–towards a new communication channel for online control in gaming applications. Multimed Tools Appl 33:73–90CrossRefGoogle Scholar
  15. 15.
    Kus R, Valbuena D, Zygierewicz J, Malechka T, Graeser A, Durka P (2012) Asynchronous BCI based on motor imagery with automated calibration and neurofeedback training. IEEE Trans Neural Syst Rehabil Eng 20:823–835CrossRefGoogle Scholar
  16. 16.
    Li Y, Long J, Yu T, Yu Z, Wang C, Zhang H, Guan C (2010) An EEG-based BCI system for 2-D cursor control by combining Mu/Beta rhythm and P300 potential. IEEE Trans Biomed Eng 57:2495–2505CrossRefGoogle Scholar
  17. 17.
    Li J, Liang J, Zhao Q, Li J, Hong K, Zhang L (2013) Design of assistive wheelchair system directly steered by human thoughts. Int J Neural Syst 23:1350013CrossRefGoogle Scholar
  18. 18.
    Li S, Zhou W, Yuan Q, Geng S, Cai D (2013) Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med 43:807–816CrossRefGoogle Scholar
  19. 19.
    Li Y, Zhou G, Graham D, Holtzhauer A (2016) Towards an EEG-based brain-computer interface for online robot control. Multimed Tools Appl 75:7999–8017CrossRefGoogle Scholar
  20. 20.
    Liang R-Z, Liang G, Li W, Gu Y, Li Q, Wang JJ-Y (2016) Learning convolutional neural network to maximize Pos@ Top performance measure. arXiv preprint arXiv:1609.08417Google Scholar
  21. 21.
    Liang R-Z, Shi L, Wang H, Meng J, Wang JJ-Y, Sun Q, Gu Y (2016) Optimizing top precision performance measure of content-based image retrieval by learning similarity function. In: Pattern Recognition (ICPR), 2016 23rd International Conference on, pp 2954–2958Google Scholar
  22. 22.
    Liang R-Z, Xie W, Li W, Wang H, Wang JJ-Y, Taylor L (2016) A novel transfer learning method based on common space mapping and weighted domain matching. In: Tools with Artificial Intelligence (ICTAI), 2016 I.E. 28th International Conference on, pp 299–303Google Scholar
  23. 23.
    Lotte F, Congedo M, Lecuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng 4:R1–R13CrossRefGoogle Scholar
  24. 24.
    Martis RJ, Acharya UR, Tan JH, Petznick A, Tong L, Chua CK, Ng EYK (2013) Application of intrinsic time-scale decomposition (ITD) to EEG signals for automated seizure prediction. Int J Neural Syst 23:1350023CrossRefGoogle Scholar
  25. 25.
    McFarland DJ (2015) The advantages of the surface Laplacian in brain–computer interface research. Int J Psychophysiol 97:271–276CrossRefGoogle Scholar
  26. 26.
    McFarland DJ, McCane LM, David SV, Wolpaw JR (1997) Spatial filter selection for EEG-based communication. Electroencephalogr Clin Neurophysiol 103:386–394CrossRefGoogle Scholar
  27. 27.
    McFarland DJ, Sarnacki WA, Wolpaw JR (2010) Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng 7:036007CrossRefGoogle Scholar
  28. 28.
    Morash V, Bai O, Furlani S, Lin P, Hallett M (2008) Classifying EEG signals preceding right hand, left hand, tongue, and right foot movements and motor imageries. Clin Neurophysiol 119:2570–2578CrossRefGoogle Scholar
  29. 29.
    Müller-Gerking J, Pfurtscheller G, Flyvbjerg H (2000) Classification of movement-related EEG in a memorized delay task experiment. Clin Neurophysiol 111:1353–1365CrossRefGoogle Scholar
  30. 30.
    Naeem M, Brunner C, Leeb R, Graimann B, Pfurtscheller G (2006) Seperability of four-class motor imagery data using independent components analysis. J Neural Eng 3:208–216CrossRefGoogle Scholar
  31. 31.
    Neuper C, Scherer R, Wriessnegger S, Pfurtscheller G (2009) Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. Clin Neurophysiol 120:239–247CrossRefGoogle Scholar
  32. 32.
    Ortner R, Allison BZ, Korisek G, Gaggl H, Pfurtscheller G (2011) An SSVEP BCI to control a hand orthosis for persons with tetraplegia. IEEE Trans Neural Syst Rehabil Eng 19:1–5CrossRefGoogle Scholar
  33. 33.
    Osorio I, Frei MG (2007) Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals. Proc R Soc A Math Phys Eng Sci 463:321–342MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Park C, Looney D, ur Rehman N, Ahrabian A, Mandic DP (2013) Classification of motor imagery BCI using multivariate empirical mode decomposition. IEEE Trans Neural Syst Rehabil Eng 21:10–22CrossRefGoogle Scholar
  35. 35.
    Pfurtscheller G (2001) Functional brain imaging based on ERD/ERS. Vis Res 41:257–260CrossRefGoogle Scholar
  36. 36.
    Pfurtscheller G, Lopes da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110:1842–1857CrossRefGoogle Scholar
  37. 37.
    Pfurtscheller G, Neuper C, Flotzinger D, Pregenzer M (1997) EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol 103:642–651CrossRefGoogle Scholar
  38. 38.
    Pfurtscheller G, Leeb R, Keinrath C, Friedman D, Neuper C, Guger C, Slater M (2006) Walking from thought. Brain Res 1071:145–152CrossRefGoogle Scholar
  39. 39.
    Pfurtscheller G, Brunner C, Schlogl A, Lopes da Silva FH (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31:153–159CrossRefGoogle Scholar
  40. 40.
    Pfurtscheller G, Scherer R, Muller-Putz GR, Lopes da Silva FH (2008) Short-lived brain state after cued motor imagery in naive subjects. Eur J Neurosci 28:1419–1426CrossRefGoogle Scholar
  41. 41.
    Pfurtscheller G, Linortner P, Winkler R, Korisek G, Muller-Putz G (2009) Discrimination of motor imagery-induced EEG patterns in patients with complete spinal cord injury. Comput Intell Neurosci:104180.
  42. 42.
    Prieto A, Prieto B, Ortigosa EM, Ros E, Pelayo F, Ortega J, Rojas I (2016) Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing 214:242–268CrossRefGoogle Scholar
  43. 43.
    Scherer R, Lee F, Schlogl A, Leeb R, Bischof H, Pfurtscheller G (2008) Toward self-paced brain–computer communication: navigation through virtual worlds. IEEE Trans Biomed Eng 55:675–682CrossRefGoogle Scholar
  44. 44.
    Shenoy P, Krauledat M, Blankertz B, Rao RP, Müller K-R (2006) Towards adaptive classification for BCI. J Neural Eng 3:R13CrossRefGoogle Scholar
  45. 45.
    Wolpaw JR, McFarland DJ (2004) Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci U S A 101:17849–17854CrossRefGoogle Scholar
  46. 46.
    Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM (2000) Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8:164–173CrossRefGoogle Scholar
  47. 47.
    Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113:767–791CrossRefGoogle Scholar
  48. 48.
    Yacine B, Amal F, Walter B (2014) Significant improvement in one-dimensional cursor control using Laplacian electroencephalography over electroencephalography. J Neural Eng 11:035014CrossRefGoogle Scholar
  49. 49.
    Yi W, Qiu S, Qi H, Zhang L, Wan B, Ming D (2013) EEG feature comparison and classification of simple and compound limb motor imagery. Neuroeng Rehabil 106:1–12Google Scholar
  50. 50.
    Yi W, Qiu S, Wang K, Qi H, Zhang L, Zhou P, He F, Ming D (2014) Evaluation of EEG Oscillatory Patterns and Cognitive Process during Simple and Compound Limb Motor Imagery. PLoS One 9:1–19Google Scholar
  51. 51.
    Yong X, Menon C (2015) EEG classification of different imaginary movements within the same limb. PLoS One 10:e0121896CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Eltaf Abdalsalam Mohamed
    • 1
    Email author
  • Mohd Zuki Yusoff
    • 1
  • Aamir Saeed Malik
    • 1
  • Mohammad Rida Bahloul
    • 1
  • Dalia Mahmoud Adam
    • 2
  • Ibrahim Khalil Adam
    • 3
  1. 1.Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering DepartmentUniversiti Teknologi PETRONAS (UTP)Seri IskandarMalaysia
  2. 2.Al-Neelain UniversityKhartoumSudan
  3. 3.Centre for Automotive Research and Electric Mobility, Mechanical Engineering DepartmentUniversiti Teknologi PETRONASSeri IskandarMalaysia

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