Skip to main content
SpringerLink
Log in

A new phase-based feature extraction method for four-class motor imagery classification

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

abstract

The processing of electroencephalogram (EEG) signals is important in brain-computer interface applications. Many different spectral analysis or time–frequency analysis methods have been used for this process. However, most of the methods used in previous studies were based on the features obtained from the power or energy of EEG signals. To date, a sufficiently high level of accuracy in classifying four-class motor imagery (MI) states with these features has not been achieved. Therefore, studies are still being carried out on this subject to increase accuracy. Since different parts of the brain interact in MI applications, it may also be important to examine phase information in MI classifications. Based on this idea, the empirical mode decomposition method was applied to EEG signals from 22 channels in this study. Thus, the intrinsic mode functions (IMFs) of each channel were obtained. The instantaneous phase values were calculated by applying the Hilbert transform to these IMFs. Twenty-two eigenvalues were obtained for each window by sliding a 22 × 22 window with as many rows and columns as the number of channels on the instantaneous phase values. A feature vector was obtained by adding the eigenvalues of all windows, one under the other. Higher accuracy (89.89%) was achieved compared to state-of-the-art studies when feature vectors were applied to the long short-term memory (LSTM) deep network. This method increased the accuracy by 4.3%. This showed that the features obtained from the phase information are highly representative in MI classification applications and may be important for MI applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Wei, Q., Wang, Y., Gao, X., Gao, S.: Amplitude and phase coupling measures for feature extraction in an EEG-based brain–computer interface. J. Neural Eng. 4, 120 (2007)

    Article  Google Scholar 

  2. Venkatachalam, K., Devipriya, A., Maniraj, J., Sivaram, M., Ambikapathy, A., Iraj, S.A.: A novel method of motor imagery classification using eeg signal. Artif. Intell. Med. 103, 101787 (2020)

    Article  Google Scholar 

  3. Li, F., He, F., Wang, F., Zhang, D., Xia, Y., Li, X.: A novel simplified convolutional neural network classification algorithm of motor imagery EEG signals based on deep learning. Appl. Sci. 10, 1605 (2020)

    Article  Google Scholar 

  4. Zhou, J., Meng, M., Gao, Y., Ma, Y., Zhang, Q.: Classification of motor imagery EEG using wavelet envelope analysis and LSTM networks. In: Chinese Control and Decision Conference, pp. 5600–5605 (2018)

  5. Hu, D., Li, W., Chen, X.: Feature extraction of motor imagery EEG signals based on wavelet packet decomposition. In: International Conference on Complex Medical Engineering, pp. 694–697 (2011)

  6. Ting, W., Guo-Zheng, Y., Bang-Hua, Y., Hong, S.: EEG feature extraction based on wavelet packet decomposition for brain computer interface. Measurement 41, 618–625 (2008)

    Article  Google Scholar 

  7. Xu, B., Wei, Z., Song, A., Wu, C., Zhang, D., Li, W., Zeng, H.: Phase synchronization information for classifying motor imagery EEG from the same limb. IEEE Access 7, 153842–153852 (2019)

    Article  Google Scholar 

  8. Wang, P., Jiang, A., Liu, X., Shang, J., Zhang, L.: LSTM-based EEG classification in motor imagery tasks. IEEE Trans. Neural Syst. Rehabil. Eng. 26, 2086–2095 (2018)

    Article  Google Scholar 

  9. Brunner, C., Leeb, R., Müller-Putz, G., Schlögl, A., Pfurtscheller, G.: BCI Competition 2008–Graz data set A. Institute for Knowledge Discovery, Graz University of Technology, 16 (2008)

  10. Wang, D., Miao, D., Blohm, G.: Multi-class motor imagery EEG decoding for brain-computer interfaces. Front. Neurosci. 6, 151 (2012)

    Article  Google Scholar 

  11. Sakhavi, S., Guan, C., Yan, S.: Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Trans. Neural Netw Learn Syst. 29, 5619–5629 (2018)

    Article  MathSciNet  Google Scholar 

  12. Xie, X., Yu, Z.L., Lu, H., Gu, Z., Li, Y.: Motor imagery classification based on bilinear sub-manifold learning of symmetric positive-definite matrices. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 504–516 (2016)

    Article  Google Scholar 

  13. Selim, S., Tantawi, M.M., Shedeed, H.A., Badr, A.: A CSP\AM-BA-SVM approach for motor imagery BCI system. IEEE Access 6, 49192–49208 (2018)

    Article  Google Scholar 

  14. Yu, Z., Song, J.: Multi-class motor imagery classification by singular value decomposition and deep boltzmann machine. In: IEEE 3rd Information Technology and Mechatronics Engineering Conference, pp. 376–379 (2017)

  15. Rahman, M.A., Khanam, F., Ahmad, M., Uddin, M.S.: Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation. Brain Inf. 7, 1-11.15 (2020)

    Article  Google Scholar 

  16. Kumar, S.U., Inbarani, H.H.: PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task. Neural Comput. Appl. 28, 3239–3258 (2017)

    Article  Google Scholar 

  17. Razi, S., Mollaei, M.R.K., Ghasemi, J.: A novel method for classification of BCI multi-class motor imagery task based on Dempster-Shafer theory. Inf. Sci. 484, 14–26 (2019)

    Article  Google Scholar 

  18. Xu, B., Zhang, L., Song, A., Wu, C., Li, W., Zhang, D., Zeng, H.: Wavelet transform time-frequency image and convolutional network-based motor imagery EEG classification. IEEE Access 7, 6084–6093 (2018)

    Article  Google Scholar 

  19. Rodrigues, P.G., Stefano Filho, C.A., Attux, R., Castellano, G., Soriano, D.C.: Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces. Med. Biol. Eng. Comput. 57, 1709–1725 (2019)

    Article  Google Scholar 

  20. Hsu, W.Y.: Enhancing the performance of motor imagery EEG classification using phase features. Clin. EEG Neurosci. 46, 113–118 (2015)

    Article  Google Scholar 

  21. Song, L., Gordon, E., Gysels, E.: Phase synchrony rate for the recognition of motor imagery in brain-computer interface. In: Advances in Neural Information Processing Systems, pp. 1265–1272 (2006)

  22. Liu, C., Fu, Y., Yang, J., Xiong, X., Sun, H., Yu, Z.: Discrimination of motor imagery patterns by electroencephalogram phase synchronization combined with frequency band energy. IEEE/CAA J. Autom. Sin. 4, 551–557 (2016)

    Article  Google Scholar 

  23. Bagh, N., Reddy, M.R.: Hilbert transform-based event-related patterns for motor imagery brain computer interface. Biomed. Signal Process. Control 62, 102020 (2020)

    Article  Google Scholar 

  24. Wang, L., Xu, G., Wang, J., Yang, S., Yan, W.: Motor imagery BCI research based on hilbert-huang transform and genetic algorithm. In: 5th International Conference on Bioinformatics and Biomedical Engineering, pp. 1–4 (2011)

  25. Xu, C., Sun, C., Jiang, G., Chen, X., He, Q., Xie, P.: Two-level multi-domain feature extraction on sparse representation for motor imagery classification. Biomed. Signal Process. Control 62, 102160 (2020)

    Article  Google Scholar 

  26. Wang, L., Xu, G., Yang, S., Yan, W.: Application of Hilbert-Huang transform for the study of motor imagery tasks. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3848–3851 (2008)

  27. Gaur, P., Pachori, R.B., Wang, H., Prasad, G.: A multivariate empirical mode decomposition based filtering for subject independent BCI. In: 27th Irish Signals and Systems Conference, pp. 1–7 (2016)

  28. Talukdar, M.T.F., Sakib, S.K., Shahnaz, C., Fattah, S.A.: Motor imagery EEG signal classification scheme based on entropy of intrinsic mode function. In: 8th International Conference on Electrical and Computer Engineering, pp. 703–706 (2014)

  29. Taran, S., Bajaj, V., Sharma, D., Siuly, S., Sengur, A.: Features based on analytic IMF for classifying motor imagery EEG signals in BCI applications. Measurement 116, 68–76 (2018)

    Article  Google Scholar 

  30. Bashar, S.K., Bhuiyan, M.I.H.: Classification of motor imagery movements using multivariate empirical mode decomposition and short time Fourier transform based hybrid method. Eng. Sci. Technol. Int. J. 19, 1457–1464 (2016)

    Google Scholar 

  31. Ji, N., Ma, L., Dong, H., Zhang, X.: EEG signals feature extraction based on DWT and EMD combined with approximate entropy. Brain Sci. 9, 201 (2019)

    Article  Google Scholar 

  32. Peng, C.J., Chen, Y.C., Chen, C.C., Chen, S.J., Cagneau, B., Chassagne, L.: An EEG-based attentiveness recognition system using hilbert-huang transform and support vector machine. J. Med. Biol. Eng. 40, 230–238 (2020)

    Article  Google Scholar 

  33. Pigorini, A., Casali, A.G., Casarotto, S., Ferrarelli, F., Baselli, G., Mariotti, M., Rosanova, M.: Time–frequency spectral analysis of TMS-evoked EEG oscillations by means of Hilbert-Huang transform. J. Neurosci. Methods 198, 236–245 (2011)

    Article  Google Scholar 

  34. Peng, Z.K., Peter, W.T., Chu, F.L.: An improved Hilbert-Huang transform and its application in vibration signal analysis. J. Sound Vib. 286, 187–205 (2005)

    Article  Google Scholar 

  35. Le Van Quyen, M., Foucher, J., Lachaux, J.P., Rodriguez, E., Lutz, A., Martinerie, J., Varela, F.J.: Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony. J. Neurosci. Methods 111, 83–98 (2001)

    Article  Google Scholar 

  36. Loboda, A., Margineanu, A., Rotariu, G., Lazar, A.M.: Discrimination of EEG-based motor imagery tasks by means of a simple phase information method. Int J Adv Res Artif Intell 3, 11–15 (2014)

    Article  Google Scholar 

  37. Ghanbari, A.A., Kousarrizi, M.N., Teshnehlab, M., Aliyari, M.: Wavelet and Hilbert transform-based Brain Computer Interface. In: International Conference on Advances in Computational Tools for Engineering Applications, pp. 438–442 (2009)

  38. Sorzano, C. O. S.: Eigenvalues and eigenvectors. Biomedical Engineering (2013). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.397.214&rep=rep1&type=pdf. Accessed 1 Feb 2021

  39. Thomas, M.O., Stewart, S.: Eigenvalues and eigenvectors: Embodied, symbolic and formal thinking. Math. Educ. Res. J. 23, 275 (2011)

    Article  Google Scholar 

  40. Michielli, N., Acharya, U.R., Molinari, F.: Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Comput. Biol. Med. 106, 71–81 (2019)

    Article  Google Scholar 

  41. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Berlin (2000)

    Book  Google Scholar 

  42. Lashgari, E., Liang, D., Maoz, U.: Data augmentation for deep-learning-based electroencephalography. J. Neurosci. Methods 346, 10885 (2020)

    Article  Google Scholar 

  43. Sakai, A., Minoda, Y., Morikawa, K.: Data augmentation methods for machine-learning-based classification of bio-signals. In: 10th Biomedical Engineering International Conference, pp. 1–4 (2017)

  44. Alazrai, R., Aburub, S., Fallouh, F., Daoud, M.I.: EEG-based BCI system for classifying motor imagery tasks of the same hand using empirical mode decomposition. In: 10th International Conference on Electrical and Electronics Engineering, pp. 615–619 (2017)

  45. Surui, L., Lijuan, S., Zhenxin, L., Bingchao, D.: Motor imagery EEG discrimination using Hilbert-Huang Entropy. Biomedical Research 28(2), 727–733 (2017)

    Google Scholar 

  46. Tang, X., Li, W., Li, X., Ma, W., Dang, X.: Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network. Expert Syst Appl 149, 113285 (2020)

    Article  Google Scholar 

  47. Bagh, N., Machireddy, R., Shahlaei, F.: Classification of motor imagery tasks using phase space reconstruction and empirical mode decomposition. In: IEEE Canadian Conference of Electrical and Computer Engineering, pp. 1–4 (2019)

  48. Qureshi, M.N.I., Cho, D., Lee, B.: EEG classification for motor imagery BCI using phase-only features extracted by independent component analysis. In: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2097–2100 (2017)

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Electronics Engineering, Faculty of Simav Technology, Kutahya Dumlupinar University, 43500, Kutahya, Turkey

    Mustafa Tosun & Osman Çetin

Authors
  1. Mustafa Tosun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Osman Çetin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mustafa Tosun.

Ethics declarations

Conflict of interest

We declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tosun, M., Çetin, O. A new phase-based feature extraction method for four-class motor imagery classification. SIViP 16, 283–290 (2022). https://doi.org/10.1007/s11760-021-02035-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-021-02035-9

Keywords

Search

Navigation