Skip to main content
SpringerLink
Log in

A Comprehensive Approach for Enhancing Motor Imagery EEG Classification in BCI’s

  • Conference paper
  • First Online:
Health Information Science (HIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14305))

Included in the following conference series:

  • 377 Accesses

Abstract

Electroencephalography (EEG) based on motor imagery has become a potential modality for brain-computer interface (BCI) systems, allowing users to control external devices by imagining doing particular motor activities. The existence of noise and the complexity of the brain signals, however, make it difficult to classify motor imagery EEG signals. This work suggests a systematic method for classifying motor imagery in the EEG. A technique known as Multiscale Principal Component Analysis (MSPCA) is used for efficient noise removal to improve the signal quality. A unique signal decomposition technique is proposed for modes extraction, allowing the separation of various oscillatory components related to motor imagery tasks. This breakdown makes it easier to isolate important temporal and spectral properties that distinguish various classes of motor imagery. These characteristics capture the dynamism and discriminative patterns present in motor imagery tasks. The motor imagery EEG signals are then classified using various machine learning and deep learning-based models based on the retrieved features. The findings of the classification show how well the suggested strategy works in generating precise and trustworthy classification success for various motor imaging tasks. The proposed method has enormous potential for BCI applications, allowing people with motor limitations to operate extrasensory equipment via brain signals.

Supported by organization x.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Coogan, C.G., He, B.: Brain-computer interface control in a virtual reality environment and applications for the internet of things. IEEE Access 6, 10 840–10 849 (2018)

    Google Scholar 

  2. Birbaumer, N., Murguialday, A.R., Cohen, L.: Brain-computer interface in paralysis. Curr. Opin. Neurol. 21(6), 634–638 (2008)

    Article  Google Scholar 

  3. Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)

    Article  Google Scholar 

  4. Sadiq, M.T., Siuly, S., Almogren, A., Li, Y., Wen, P.: Efficient novel network and index for alcoholism detection from EEGs. Health Inf. Sci. Syst. 11(1), 27 (2023)

    Article  Google Scholar 

  5. Akbari, H., et al.: Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain. Bratislava Med. J./Bratislavske Lekarske Listy 124(1) (2023)

    Google Scholar 

  6. Kaiser, V., Daly, I., Pichiorri, F., Mattia, D., Müller-Putz, G.R., Neuper, C.: Relationship between electrical brain responses to motor imagery and motor impairment in stroke. Stroke 43(10), 2735–2740 (2012)

    Article  Google Scholar 

  7. Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001)

    Article  Google Scholar 

  8. Alkadhi, H., et al.: What disconnection tells about motor imagery: evidence from paraplegic patients. Cereb. Cortex 15(2), 131–140 (2004)

    Article  Google Scholar 

  9. Pfurtscheller, G., et al.: Graz-BCI: state of the art and clinical applications. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 1–4 (2003)

    Article  Google Scholar 

  10. Akbari, H., Sadiq, M.T., Siuly, S., Li, Y., Wen, P.: Identification of normal and depression EEG signals in variational mode decomposition domain. Health Inf. Sci. Syst. 10(1), 24 (2022)

    Article  Google Scholar 

  11. Tufail, A.B., et al.: On disharmony in batch normalization and dropout methods for early categorization of Alzheimer’s disease. Sustainability 14(22), 14695 (2022)

    Google Scholar 

  12. Akbari, H., Sadiq, M.T., Payan, M., Esmaili, S.S., Baghri, H., Bagheri, H.: Depression detection based on geometrical features extracted from SODP shape of EEG signals and binary PSO. Traitement du Signal 38(1) (2021)

    Google Scholar 

  13. Akbari, H., Sadiq, M.T., Rehman, A.U.: Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain. Health Inf. Sci. Syst. 9(1), 1–15 (2021)

    Article  Google Scholar 

  14. Sadiq, M.T., et al.: Exploiting feature selection and neural network techniques for identification of focal and nonfocal EEG signals in TQWT domain. J. Healthcare Eng. 2021, 24 (2021)

    Article  Google Scholar 

  15. Hussain, W., Sadiq, M.T., Siuly, S., Rehman, A.U.: Epileptic seizure detection using 1 d-convolutional long short-term memory neural networks. Appl. Acoust. 177, 107941 (2021)

    Article  Google Scholar 

  16. Sadiq, M.T., Yu, X., Yuan, Z., Aziz, M.Z., Siuly, S., Ding, W.: Toward the development of versatile brain-computer interfaces. IEEE Trans. Artif. Intell. 2(4), 314–328 (2021)

    Article  Google Scholar 

  17. Sadiq, M.T., et al.: Motor imagery BCI classification based on multivariate variational mode decomposition. IEEE Trans. Emerg. Top. Comput. Intell. 6(5), 1177–1189 (2022)

    Article  Google Scholar 

  18. Yu, X., Aziz, M.Z., Sadiq, M.T., Jia, K., Fan, Z., Xiao, G.: Computerized multidomain EEG classification system: a new paradigm. IEEE J. Biomed. Health Inform. 26(8), 3626–3637 (2002)

    Article  Google Scholar 

  19. Sadiq, M.T., Siuly, S., Rehman, A.U.: Evaluation of power spectral and machine learning techniques for the development of subject-specific BCI. In: Artificial Intelligence-Based Brain-Computer Interface, pp. 99–120. Elsevier (2022)

    Google Scholar 

  20. Sadiq, M.T., et al.: Motor imagery EEG signals decoding by multivariate empirical wavelet transform-based framework for robust brain-computer interfaces. IEEE Access 7, 171431–171451 (2019)

    Article  Google Scholar 

  21. Sadiq, M.T., et al.: Motor imagery EEG signals classification based on mode amplitude and frequency components using empirical wavelet transform. IEEE Access 7, 127678–127692 (2019)

    Article  Google Scholar 

  22. Sadiq, M.T., Yu, X., Yuan, Z., Aziz, M.Z.: Motor imagery BCI classification based on novel two-dimensional modeling in empirical wavelet transform. Electron. Lett. 56(25), 1367–1369 (2020)

    Article  Google Scholar 

  23. Sadiq, M.T., Yu, X., Yuan, Z., Aziz, M.Z.: Identification of motor and mental imagery EEG in two and multiclass subject-dependent tasks using successive decomposition index. Sensors 20(18), 5283 (2020)

    Article  Google Scholar 

  24. Sadiq, M.T., Aziz, M.Z., Almogren, A., Yousaf, A., Siuly, S., Rehman, A.U.: Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework. Comput. Biol. Med. 143, 105242 (2022)

    Article  Google Scholar 

  25. Sadiq, M.T., Akbari, H., Siuly, S., Yousaf, A., Rehman, A.U.: A novel computer-aided diagnosis framework for EEG-based identification of neural diseases. Comput. Biol. Med. 138, 104922 (2021)

    Article  Google Scholar 

  26. Sadiq, M.T., Yu, X., Yuan, Z., Aziz, M.Z., Siuly, S., Ding, W.: A matrix determinant feature extraction approach for decoding motor and mental imagery EEG in subject-specific tasks. IEEE Trans. Cogn. Dev. Syst. 14(2), 375–387 (2020)

    Article  Google Scholar 

  27. Yu, X., Aziz, M.Z., Sadiq, M.T., Fan, Z., Xiao, G.: A new framework for automatic detection of motor and mental imagery EEG signals for robust BCI systems. IEEE Trans. Instrum. Meas. 70, 1–12 (2021)

    Google Scholar 

  28. Kronegg, J., Chanel, G., Voloshynovskiy, S., Pun, T.: EEG-based synchronized brain-computer interfaces: a model for optimizing the number of mental tasks. IEEE Trans. Neural Syst. Rehabil. Eng. 15(1), 50–58 (2007)

    Article  Google Scholar 

  29. Kosmyna, N., Lécuyer, A.: A conceptual space for EEG-based brain-computer interfaces. PLoS ONE 14(1), e0210145 (2019)

    Article  Google Scholar 

  30. Mellinger, J., et al.: An MEG-based brain-computer interface (BCI). Neuroimage 36(3), 581–593 (2007)

    Article  Google Scholar 

  31. Sitaram, R., et al.: FMRI brain-computer interface: a tool for neuroscientific research and treatment. Comput. Intell. Neurosci. 2007, 1 (2007)

    Article  Google Scholar 

  32. Zhu, Y., et al.: PET mapping for brain-computer-interface-based stimulation in a rat model with intracranial electrode implantation in the ventro-posterior medial thalamus. J. Nuclear Med. jnumed-115 (2016)

    Google Scholar 

  33. Fukuyama, H., et al.: Brain functional activity during gait in normal subjects: a SPECT study. Neurosci. Lett. 228(3), 183–186 (1997)

    Article  Google Scholar 

  34. Rodríguez-Bermúdez, G., García-Laencina, P.J.: Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces. J. Med. Syst. 36(1), 51–63 (2012)

    Article  Google Scholar 

  35. Polat, K., Güneş, S.: Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl. Math. Comput. 187(2), 1017–1026 (2007)

    MathSciNet  MATH  Google Scholar 

  36. Pfurtscheller, G., Neuper, C., Schlogl, A., Lugger, K.: Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans. Rehabil. Eng. 6(3), 316–325 (1998)

    Article  Google Scholar 

  37. Schlögl, A., Neuper, C., Pfurtscheller, G.: Estimating the mutual information of an EEG-based brain-computer interface. Biomedizinische Technik/Biomed. Eng. 47(1–2), 3–8 (2002)

    Article  Google Scholar 

  38. Burke, D.P., Kelly, S.P., de Chazal, P., Reilly, R.B., Finucane, C.: A parametric feature extraction and classification strategy for brain-computer interfacing. IEEE Trans. Neural Syst. Rehabil. Eng. 13(1), 12–17 (2005)

    Article  Google Scholar 

  39. Jansen, B.H., Bourne, J.R., Ward, J.W.: Autoregressive estimation of short segment spectra for computerized EEG analysis. IEEE Trans. Biomed. Eng. 9, 630–638 (1981)

    Article  Google Scholar 

  40. Krusienski, D.J., McFarland, D.J., Wolpaw, J.R.: An evaluation of autoregressive spectral estimation model order for brain-computer interface applications. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1323–1326. IEEE (2006)

    Google Scholar 

  41. Kevric, J., Subasi, A.: Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomed. Signal Process. Control 31, 398–406 (2017)

    Article  Google Scholar 

  42. Ramoser, H., Muller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng. 8(4), 441–446 (2000)

    Article  Google Scholar 

  43. Yong, X., Ward, R.K., Birch, G.E.: Sparse spatial filter optimization for EEG channel reduction in brain-computer interface. In: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 417–420. IEEE (2008)

    Google Scholar 

  44. Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: Regularized common spatial patterns with generic learning for EEG signal classification. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6599–6602. IEEE (2009)

    Google Scholar 

  45. Lotte, F., Guan, C.: Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans. Biomed. Eng. 58(2), 355–362 (2010)

    Article  Google Scholar 

  46. Zhang, R., Xu, P., Guo, L., Zhang, Y., Li, P., Yao, D.: Z-score linear discriminant analysis for EEG based brain-computer interfaces. PLoS ONE 8(9), e74433 (2013)

    Article  Google Scholar 

  47. Lu, H., Eng, H.-L., Guan, C., Plataniotis, K.N., Venetsanopoulos, A.N.: Regularized common spatial pattern with aggregation for EEG classification in small-sample setting. IEEE Trans. Biomed. Eng. 57(12), 2936–2946 (2010)

    Article  Google Scholar 

  48. Jiao, Y., et al.: Sparse group representation model for motor imagery EEG classification. IEEE J. Biomed. Health Inform. 23(2), 631–641 (2018)

    Article  Google Scholar 

  49. Zhang, Y., Nam, C.S., Zhou, G., Jin, J., Wang, X., Cichocki, A.: Temporally constrained sparse group spatial patterns for motor imagery BCI. IEEE Trans. Cybern. 49(9), 3322–3332 (2018)

    Article  Google Scholar 

  50. Feng, J.K., et al.: An optimized channel selection method based on multifrequency CSP-rank for motor imagery-based BCI system. Comput. Intell. Neurosci. 2019 (2019)

    Google Scholar 

  51. Li, Y., Wen, P.P., et al.: Clustering technique-based least square support vector machine for EEG signal classification. Comput. Methods Programs Biomed. 104(3), 358–372 (2011)

    Article  Google Scholar 

  52. Wu, W., Gao, X., Hong, B., Gao, S.: Classifying single-trial EEG during motor imagery by iterative spatio-spectral patterns learning (ISSPL). IEEE Trans. Biomed. Eng. 55(6), 1733–1743 (2008)

    Article  Google Scholar 

  53. Song, L., Epps, J.: Classifying EEG for brain-computer interface: learning optimal filters for dynamical system features. Comput. Intell. Neurosci. 2007, 3 (2007)

    Article  Google Scholar 

  54. Wang, S., James, C.J.: Extracting rhythmic brain activity for brain-computer interfacing through constrained independent component analysis. Comput. Intell. Neurosci. 2007 (2007)

    Google Scholar 

  55. Kutlu, Y., Kuntalp, D.: Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients. Comput. Methods Programs Biomed. 105(3), 257–267 (2012)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  57. Thomas, J., Maszczyk, T., Sinha, N., Kluge, T., Dauwels, J.: Deep learning-based classification for brain-computer interfaces. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 234–239. IEEE (2017)

    Google Scholar 

  58. Jin, Z., Zhou, G., Gao, D., Zhang, Y.: EEG classification using sparse Bayesian extreme learning machine for brain-computer interface. Neural Comput. Appl. 1–9 (2018)

    Google Scholar 

  59. Zhang, Y., Wang, Y., Jin, J., Wang, X.: Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification. Int. J. Neural Syst. 27(02), 1650032 (2017)

    Article  Google Scholar 

  60. Zhang, X., Yao, L., Wang, X., Monaghan, J., Mcalpine, D.: A survey on deep learning based brain computer interface: recent advances and new frontiers. arXiv preprint arXiv:1905.04149 (2019)

  61. Gilles, J.: Empirical wavelet transform. IEEE Trans. Signal Process. 61(16), 3999–4010 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  62. Blankertz, B., et al.: The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans. Neural Syst. Rehabil. Eng. 14(2), 153–159 (2006)

    Article  Google Scholar 

  63. Jurcak, V., Tsuzuki, D., Dan, I.: 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. Neuroimage 34(4), 1600–1611 (2007)

    Article  Google Scholar 

  64. Akbari, H., Sadiq, M.T.: Detection of focal and non-focal EEG signals using non-linear features derived from empirical wavelet transform rhythms. Phys. Eng. Sci. Med. 44(1), 157–171 (2021)

    Article  Google Scholar 

  65. Akbari, H., Ghofrani, S., Zakalvand, P., Sadiq, M.T.: Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features. Biomed. Signal Process. Control 69, 102917 (2021)

    Article  Google Scholar 

  66. Akbari, H., Sadiq, M.T., Siuly, S., Li, Y., Wen, P.: An automatic scheme with diagnostic index for identification of normal and depression EEG signals. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds.) HIS 2021. LNCS, vol. 13079, pp. 59–70. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90885-0_6

    Chapter  Google Scholar 

  67. Sadiq, M.T., Siuly, S., Ur Rehman, A., Wang, H.: Auto-correlation based feature extraction approach for EEG alcoholism identification. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds.) HIS 2021. LNCS, vol. 13079, pp. 47–58. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90885-0_5

    Chapter  Google Scholar 

  68. Sadiq, M.T., Akbari, H., Siuly, S., Li, Y., Wen, P.: Alcoholic EEG signals recognition based on phase space dynamic and geometrical features. Chaos Solitons Fractals 158, 112036 (2022)

    Article  MathSciNet  Google Scholar 

  69. Asif, R.M., et al.: Design and analysis of robust fuzzy logic maximum power point tracking based isolated photovoltaic energy system. Eng. Rep. 2(9), e12234 (2020)

    Article  Google Scholar 

  70. Akbari, H., et al.: Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features. Appl. Acoust. 179, 108078 (2021)

    Article  Google Scholar 

  71. Akhter, M.P., Jiangbin, Z., Naqvi, I.R., Abdelmajeed, M., Sadiq, M.T.: Automatic detection of offensive language for Urdu and Roman Urdu. IEEE Access 8, 91213–91226 (2020)

    Article  Google Scholar 

  72. Akhter, M.P., Jiangbin, Z., Naqvi, I.R., Abdelmajeed, M., Mehmood, A., Sadiq, M.T.: Document-level text classification using single-layer multisize filters convolutional neural network. IEEE Access 8, 42689–42707 (2020)

    Article  Google Scholar 

  73. Fan, Z., Jamil, M., Sadiq, M.T., Huang, X., Yu, X.: Exploiting multiple optimizers with transfer learning techniques for the identification of Covid-19 patients. J. Healthcare Eng. 2020 (2020)

    Google Scholar 

  74. Sadiq, M.T., Akbari, H., Siuly, S., Li, Y., Wen, P.: Fractional Fourier transform aided computerized framework for alcoholism identification in EEG. In: Traina, A., Wang, H., Zhang, Y., Siuly, S., Zhou, R., Chen, L. (eds.) HIS 2022. LNCS, vol. 13705, pp. 100–112. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20627-6_10

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Advanced Engineering Center, School of Architecture, Technology and Engineering, University of Brighton, Brighton, BN2 4AT, UK

    Muhammad Tariq Sadiq

  2. Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia

    Siuly Siuly

  3. School of Mathematics Physics and Computing, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350, Australia

    Yan Li

  4. School of Engineering, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350, Australia

    Paul Wen

Authors
  1. Muhammad Tariq Sadiq
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siuly Siuly
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paul Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Tariq Sadiq .

Editor information

Editors and Affiliations

  1. University of Southern Queensland, Darling Heights, Australia

    Yan Li

  2. Vrije University, Amsterdam, The Netherlands

    Zhisheng Huang

  3. DAV University Jalandhar, Jalandhar, Punjab, India

    Manik Sharma

  4. Swinburne University of Technology, Hawthorn, VIC, Australia

    Lu Chen

  5. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sadiq, M.T., Siuly, S., Li, Y., Wen, P. (2023). A Comprehensive Approach for Enhancing Motor Imagery EEG Classification in BCI’s. In: Li, Y., Huang, Z., Sharma, M., Chen, L., Zhou, R. (eds) Health Information Science. HIS 2023. Lecture Notes in Computer Science, vol 14305. Springer, Singapore. https://doi.org/10.1007/978-981-99-7108-4_21

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-7108-4_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7107-7

  • Online ISBN: 978-981-99-7108-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation