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EEG Signal Processing and Its Classification for Rehabilitation Device Control

  • Angana Saikia
  • Sudip Paul
Chapter

Abstract

At this present technology-based world, electroencephalography (EEG) instruments have become a tool for various research and diagnoses of different human health disorders. The brain-computer interface (BCI) is an area which is a very much emerging technology that uses the human brain signals to control external devices. For excellent and accurate results, BCI has recognized the need for systems that makes it more user-friendly, real time, manageable, and suited for people like clinical and disabled patients. Thus, this chapter will refer to the processing of the EEG signal and different classification techniques which will further be used to control the rehabilitation devices through BCI system.

A medical diagnostic technique that reads the electrical activity of the scalp which is generated by a human brain is known as electroencephalography, and the recording is called electroencephalogram (EEG). The electrical activity from the scalp of the brain is mainly picked up using metal electrodes having a conductive media. An EEG recording system is a combination of a couple of instruments. They are electrodes consisting of a conductive media, amplifiers and filters, analog-to-digital converters, and recording device/printer. Feature extraction and classification of electroencephalograph signals for human subjects is a challenge for both the engineers and scientists. Mainly fast Fourier transform (FFT), Lyapunov exponent, correlation dimension, and wavelet transformation are the tools for EEG signal processing. There are also various signal processing techniques for classification of nonlinear and nonstationary signals like EEG. Some of the signal processing techniques are support vector machine (SVM) and multilayer perceptron (MLP)-based classifier, back-propagation neural network, self-organizing feature maps followed by an autoregressive modeling and artificial neural network, etc. The classification rate calculated using the various classification techniques can further be used to control the rehabilitation devices like artificial limbs (hand and leg). The advances in brain-computer interface (BCI) research and its applications have given a significant impact to biomedical research. This would be a boon for the person with disability so that they can interact and go through their day-to-day work smoothly with the help of the rehabilitation devices.

Keywords

Electroencephalograph (EEG) Rehabilitation Signal processing Signal classification 

Notes

Acknowledgments

This study has been ethically approved by the Institutional Ethical Committee, NEHU, Shillong, vide no: IECHSP/2017/42 and also from the collaborating institute: “North Eastern Indira Gandhi Regional Institute of Health & Medical Science,” Shillong, vide no: NEIGR/IEC/M6/F13/18.

References

  1. 1.
    Al-Fahoum AS, Al-Fraihat AA (2014) Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neurosci 2014:7.  https://doi.org/10.1155/2014/730218CrossRefGoogle Scholar
  2. 2.
    Alix JJP, Ponnusamy A, Pilling E, Hart AR (2017) An introduction to neonatal EEG. Paediatr Child Health 27(3):135–142.  https://doi.org/10.1016/j.paed.2016.11.003CrossRefGoogle Scholar
  3. 3.
    Atwood HL, MacKay WA (1989) Essentials of neurophysiology. Decker, TorontoGoogle Scholar
  4. 4.
    Azlan WAW, Low YF (2014) Feature extraction of electroencephalogram (EEG) signal – a review. Paper presented at the 2014 IEEE conference on Biomedical Engineering and Sciences (IECBES), December 8–10Google Scholar
  5. 5.
    Bruce EN, Bruce MC, Vennelaganti S (2009) Sample entropy tracks changes in EEG power Spectrum with sleep state and aging. J Clin Neurophysiol 26(4):257–266.  https://doi.org/10.1097/WNP.0b013e3181b2f1e3CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Chi YM, Jung TP, Cauwenberghs G (2010) Dry-contact and noncontact biopotential electrodes: methodological review. IEEE Rev Biomed Eng 3:106–119.  https://doi.org/10.1109/RBME.2010.2084078CrossRefPubMedGoogle Scholar
  7. 7.
    Croft RJ, Barry RJ (2000) Removal of ocular artifact from the EEG: a review. Neurophysiologie Clinique/Clin Neurophysiol 30(1):5–19.  https://doi.org/10.1016/S0987-7053(00)00055-1CrossRefGoogle Scholar
  8. 8.
    El-Naqa I, Yongyi Y, Wernick MN, Galatsanos NP, Nishikawa RM (2002) A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging 21(12):1552–1563.  https://doi.org/10.1109/TMI.2002.806569CrossRefPubMedGoogle Scholar
  9. 9.
    Guerrero-Mosquera C, Vazquez AN (2009) New approach in features extraction for EEG signal detection. In: Conference proceedings : Annual international conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual conference, 2009, 13–16.  https://doi.org/10.1109/iembs.2009.5332434
  10. 10.
    Guler I, Derya Ubeyli E (2005) Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients, vol 148CrossRefGoogle Scholar
  11. 11.
    Hsin HC, Li CC, Sun M, Sclabassi RJ (1992) An adaptive training algorithm for back-propagation neural networks. Paper presented at the [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics, October 18–21Google Scholar
  12. 12.
    Ince NF, Tewfik A, Arica S (2005). Classification of movement EEG with local discriminant bases. Paper presented at the proceedings. (ICASSP ‘05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, March 18–23Google Scholar
  13. 13.
    Islam MK, Rastegarnia A, Yang Z (2016) Methods for artifact detection and removal from scalp EEG: a review. Neurophysiologie Clinique/Clin Neurophysiol 46(4):287–305.  https://doi.org/10.1016/j.neucli.2016.07.002CrossRefGoogle Scholar
  14. 14.
    Jian-Zhong X, Hui Z, Chong-Xun Z, Xiang-Guo Y (2003) Wavelet packet transform for feature extraction of EEG during mental tasks. Paper presented at the proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), November 2–5Google Scholar
  15. 15.
    Kalpakam NV, Venkataramanan S (2004) Haar wavelet decomposition of EEG signal for ocular artifact de-noising: a mathematical analysisGoogle Scholar
  16. 16.
    Kok A (1997) Event-related-potential (ERP) reflections of mental resources: a review and synthesis. Biol Psychol 45(1):19–56.  https://doi.org/10.1016/S0301-0511(96)05221-0CrossRefPubMedGoogle Scholar
  17. 17.
    Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Paper presented at the proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive TechnologiesGoogle Scholar
  18. 18.
    Li K, Sun G, Zhang B, Wu S, Wu G (2009) Correlation between forehead EEG and sensorimotor area EEG in motor imagery task. Paper presented at the 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, December 12–14Google Scholar
  19. 19.
    Makeig S, Jung T-P (1996) Changes in alertness is principal component of variance in the EEG spectrum, vol 7Google Scholar
  20. 20.
    Maki H, Toda T, Sakti S, Neubig G, Nakamura S (2015) EEG signal enhancement using multi-channel wiener filter with a spatial correlation prior. Paper presented at the 2015 IEEE international conference on Acoustics, Speech and Signal Processing (ICASSP), 19–24 April 2015Google Scholar
  21. 21.
    Martinez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23(2):228–233.  https://doi.org/10.1109/34.908974CrossRefGoogle Scholar
  22. 22.
    McFarland DJ, Wolpaw JR (2017) EEG-based brain–computer interfaces. Curr Opin Biomed Eng 4:194–200.  https://doi.org/10.1016/j.cobme.2017.11.004CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Motamedi-Fakhr S, Moshrefi-Torbati M, Hill M, Hill CM, White PR (2014) Signal processing techniques applied to human sleep EEG signals—a review. Biomed Signal Process Control 10:21–33.  https://doi.org/10.1016/j.bspc.2013.12.003CrossRefGoogle Scholar
  24. 24.
    Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36(2, part 1):2027–2036.  https://doi.org/10.1016/j.eswa.2007.12.065CrossRefGoogle Scholar
  25. 25.
    Oweis RJ, Abdulhay EW (2011) Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomed Eng Online 10:38–38.  https://doi.org/10.1186/1475-925X-10-38CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Panahi N, Shayesteh MG, Mihandoost S, Varghahan BZ (2011) Recognition of different datasets using PCA, LDA, and various classifiers. Paper presented at the 2011 5th international conference on Application of Information and Communication Technologies (AICT), 12–14 Oct 2011Google Scholar
  27. 27.
    Petersen SE, Posner MI (2012) The attention system of the human brain: 20 years after. Annu Rev Neurosci 35:73–89.  https://doi.org/10.1146/annurev-neuro-062111-150525CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Pregenzer M, Pfurtscheller G, Flotzinger D (1996) Automated feature selection with a distinction sensitive learning vector quantizer. Neurocomputing 11(1):19–29.  https://doi.org/10.1016/0925-2312(94)00071-9CrossRefGoogle Scholar
  29. 29.
    Roman-Gonzalez A (2010) Communication technologies based on brain activity. Paper presented at the 2010 World Congress in Computer Science, Computer Engineering and Applied Computing – WORLDCOMP 2010, July 26, Las VegasGoogle Scholar
  30. 30.
    Sai CY, Mokhtar N, Arof H, Cumming P, Iwahashi M (2018) Automated classification and removal of EEG artifacts with SVM and wavelet-ICA. IEEE J Biomed Health Inf PP(99):1–1.  https://doi.org/10.1109/JBHI.2017.2723420CrossRefGoogle Scholar
  31. 31.
    Sarma P, Tripathi P, Sarma MP, Sarma KK (2016) Pre-processing and feature extraction techniques for EEG-BCI applications-a review of recent research. ADBU J Eng Technol 5(1)Google Scholar
  32. 32.
    Schomer DL, Lopes da Silva FH (2018) Niedermeyer’s electroencephalography: basic principles, clinical applications, and related fieldsGoogle Scholar
  33. 33.
    Tan L (2008) Digital signal processing fundamentals and applications. From http://www.books24x7.com/marc.asp?bookid=28057
  34. 34.
    Tatum WO, Rubboli G, Kaplan PW, Mirsatari SM, Radhakrishnan K, Gloss D, Beniczky S (2018) Clinical utility of EEG in diagnosing and monitoring epilepsy in adults. Clin Neurophysiol 129(5):1056–1082.  https://doi.org/10.1016/j.clinph.2018.01.019CrossRefPubMedGoogle Scholar
  35. 35.
    Wang Y, Makeig S (2009) Predicting intended movement direction using EEG from human posterior parietal cortex. Paper presented at the Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience, Berlin/HeidelbergCrossRefGoogle Scholar
  36. 36.
    Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113(6):767–791.  https://doi.org/10.1016/S1388-2457(02)00057-3CrossRefPubMedGoogle Scholar
  37. 37.
    Zurada JM (1992) Introduction to artificial neural systems. West Publishing Company, St. PaulGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Angana Saikia
    • 1
  • Sudip Paul
    • 1
  1. 1.Department of Biomedical Engineering, School of TechnologyNorth-Eastern Hill UniversityShillongIndia

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