Skip to main content
SpringerLink
Log in

Motor imagery-based neuro-feedback system using neuronal excitation of the active synapses

  • Published:
Annals of Telecommunications Aims and scope Submit manuscript

An Editorial Expression of Concern to this article was published on 05 January 2022

This article has been updated

Abstract

Neuronal excitation enables identifying the features of an electroencephalogram (EEG) signal for motor imagery detection. We propose a novel feature extraction algorithm supported by short-term cepstrum-based inverse filtration of neuronal excitation of the active synapse. The maximum power of the estimated neuronal excitation is subjected to a two-class Bayesian probabilistic classifier. The feature extraction algorithm with the Bayesian probabilistic classifier significantly improves the brain–computer interface performance compared with that of other conventional methods of EEG signal processing such as wavelet with a Bayesian classifier, autocorrelation and CSP filter with a naïve Bayes classifier over the BCI competition II and IV datasets. Consequently, this neuronal excitation feature allows the authors to develop a motor imagery neuro-feedback system; the performance of which achieves 87.2% average classification accuracy, which is 14% greater than that of the wavelet-based algorithm and 6.2% greater than that of the TRSP-based algorithm, with 53 ms of processing time allotted for each instruction in a real-time experiment. However, brain signal variation across different subjects and sessions significantly impairs decision accuracy. Our neuronal excitation base feature extraction algorithm minimizes these variations in classification accuracy.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Change history

References

  1. Prasad R (2016) Human bond communication. Wirel Pers Commun 87(3):619–627

    Article  Google Scholar 

  2. Dixit S, Prasad R (2017) Human bond communication: the holy grail of holistic communication and immersive experience. John Wiley & Sons

  3. Iftikhar T, Khattak HA, Ameer Z, Shah MA, Qureshi FF, Shakir MZ (2019) Human bond communications: architectures, challenges, and possibilities. IEEE Commun Mag 57(2):19–25

    Article  Google Scholar 

  4. Wolpaw JR, Birbaumer N et al (2000) Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehab Eng 8(2):164–173

    Article  Google Scholar 

  5. Tawfik NS, Youssef SM et al (2016) A hybrid automated detection of epileptic seizures in EEG records. Comput Electr Eng 53:177–190

    Article  Google Scholar 

  6. Hema CR et al (2011) Asynchronous brain machine interface-based control of a wheelchair. In software tools and algorithms for biological systems (pp. 565–572). Springer, New York, NY

  7. Tanaka K, Matsunaga K, Wang HO (2005) Electroencephalogram-based control of an electric wheelchair. IEEE Trans Robot 21(4):762–766

    Article  Google Scholar 

  8. Shyaa NS (2018) Electroencephalography (EEG) based mobile robot control through an adaptive brain robot Interface. Am Sci Res J Eng Technol Sci (ASRJETS) 42(1):139–147

    Google Scholar 

  9. Iacoviello D, Petracca A, Spezialetti M, Placidi G (2015) A real-time classification algorithm for EEG-based BCI driven by self-induced emotions. Comput Methods Prog Biomed 122(3):293–303

    Article  Google Scholar 

  10. Singh N, Huyck CR et al (2017) Neuron-based control mechanisms for a robotic arm and hand. Int J Comput Electr Autom Control Inf Eng 11(2):221–229

    Google Scholar 

  11. Batres-Mendoza, P., Ibarra-Manzano, M. A. et al.(2017). Improving EEG-based motor imagery classification for real-time applications using the QSA method. Comput Intell Neurosci 2017

  12. Wang YK, Chen SA, Lin CT (2014) An EEG-based brain–computer interface for dual task driving detection. Neurocomputing 129:85–93

    Article  Google Scholar 

  13. Junwei L, Ramkumar S et al (2018) Brain computer interface for neurodegenerative person using electroencephalogram. IEEE Access 7:2439–2452

    Article  Google Scholar 

  14. Mason SG, Birch GE (2003) A general framework for brain-computer interface design. IEEE Trans Neural Syst Rehab Eng 11(1):70–85

    Article  Google Scholar 

  15. Vigário R, Sarela J, Jousmiki V, Hamalainen M, Oja E (2000) Independent component approach to the analysis of EEG and MEG recordings. IEEE Trans Biomed Eng 47(5):589–593

    Article  Google Scholar 

  16. Hochberg LR, Donoghue JP (2006) Sensors for brain-computer interfaces. IEEE Eng Med Biol Mag 25(5):32–38

    Article  Google Scholar 

  17. Gu JN, Lu HT, Lu BL (2014) An integrated Gaussian mixture model to estimate vigilance level based on EEG recordings. Neurocomputing 129:107–113

    Article  Google Scholar 

  18. Bashashati A, Fatourechi M, Ward RK, Birch GE (2007) A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. J Neural Eng 4(2):R32–R57

    Article  Google Scholar 

  19. Schlögl A et al (2007) A fully automated correction method of EOG artifacts in EEG recordings. Clin Neurophysiol 118(1):98–104

    Article  MathSciNet  Google Scholar 

  20. Upadhyay R, Padhy PK et al (2016) EEG artifact removal and noise suppression by discrete orthonormal S-transform denoising. Comput Electr Eng 53:125–142

    Article  Google Scholar 

  21. Srinivasulu A, Reddy MS (2012) Artifacts removing from EEG signals by ICA Algorithms. IOSR J Electr Electron Eng (IOSRJEEE) 2(4):11–16

    Article  Google Scholar 

  22. Kam TE, Suk HI, Lee SW (2013) Non-homogeneous spatial filter optimization for electroencephalogram (EEG)-based motor imagery classification. Neurocomputing 108:58–68

    Article  Google Scholar 

  23. Thomas KP et al (2009) A new discriminative common spatial pattern method for motor imagery brain–computer interfaces. IEEE Trans Biomed Eng 56(11):2730–2733

    Article  Google Scholar 

  24. Sethi S, Upadhyay R et al (2018) Stockwell-common spatial pattern technique for motor imagery-based brain computer interface design. Comput Electr Eng 71:492–504

    Article  Google Scholar 

  25. Orović I, Zogović P et al (2008) Speech signals protection via logo watermarking based on the time–frequency analysis. Ann Telecommun 63(7–8):369–377

    Article  Google Scholar 

  26. Bhattacharyya S, Mukul MK (2018) Reactive frequency band based movement imagery classification. J Ambient Intell Humanized Comput 9:1–14. https://doi.org/10.1007/s12652-018-0725-3

    Article  Google Scholar 

  27. Gandhi V, Prasad G, Coyle D, Behera L, McGinnity T (2014) Quantum neural network-based EEG filtering for a brain–computer interface. IEEE Trans Neural Netw Learn Syst 25(2):278–288

    Article  Google Scholar 

  28. Bhattacharyya S, Mukul MK (2017) Brain-machine interface: human-computer interaction. Handbook of research on data science for effective healthcare practice and administration. IGI Glob Publ Chapter No.18, pp. 417–443

  29. Shermeh AE, Azimi H (2010) Blind signal-type classification using a novel robust feature subset selection method and neural network classifier. Ann Telecommun 65(9–10):625–633

    Article  Google Scholar 

  30. Bhattacharyya S, Mukul MK (2018) Reactive frequency band based real-time motor imagery classification. Int J Intell Syst Technol Appl 17(1/2)

  31. He L, Liu B et al (2016) Motor imagery EEG signals analysis based on Bayesian network with Gaussian distribution. Neurocomputing 188:217–224

    Article  Google Scholar 

  32. He L, Hu D, Wan M et al (2015) Common Bayesian network for classification of EEG-based multiclass motor imagery BCI. IEEE Trans Syst Man Cybern: Syst 46(6):843–854

    Article  Google Scholar 

  33. Bhattacharyya S, Mukul MK (2016) Cepstrum based algorithm for motor imagery classification . International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE 2016) © 2016 IEEE, Sep 22-23, pp. 397–402

  34. Mehmood RM, Lee HJ (2016) A novel feature extraction method based on late positive potential for emotion recognition in human brain signal patterns. Comput Electr Eng 53:444–457

    Article  Google Scholar 

  35. Bhattacharyya S, Mukul MK (2018) Time-frequency series based movement imagery classification. Int J Biomed Eng Technol 27(1/2):151–165

    Article  Google Scholar 

  36. Wang XW, Nie D, Lu BL (2014) Emotional state classification from EEG data using machine learning approach. Neurocomputing 129:94–106

    Article  Google Scholar 

  37. Siuly S, Li Y (2012) Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface. IEEE Trans Neural Syst Rehab Eng 20(4):526–538

    Article  Google Scholar 

  38. Louet Y, Palicot J (2008) A classification of methods for efficient power amplification of signals. Ann Telecommun 63(7–8):351–368

    Article  Google Scholar 

  39. Wang B, Wong CM et al (2018) Trial pruning based on genetic algorithm for single-trial EEG classification. Comput Electr Eng 38(1):35–44

    Article  Google Scholar 

  40. Zhang S et al (2017) Two-stage plant species recognition by local mean clustering and weighted sparse representation classification. Clust Comput 20(2):1517–1525

    Article  Google Scholar 

  41. Zhang R et al (2017) Medical image classification based on multi-scale non-negative sparse coding. Artif Intell Med 83:44–51

    Article  Google Scholar 

  42. Vapnik V (2013) The nature of statistical learning theory, Springer science & business media

  43. Duda RO et al (2012) Pattern classification. John Wiley & Sons

  44. Le Q et al (2014) Effects of repetitive transcranial magnetic stimulation on hand function recovery and excitability of the motor cortex after stroke: a meta-analysis. Am J Phys Med Rehab 93(5):422–430

    Article  Google Scholar 

  45. Wang Y, Veluvolu KC, Cho JH, Defoort M (2012) Adaptive estimation of EEG for subject-specific reactive band identification and improved ERD detection. Neurosci Lett 528(2):137–142

    Article  Google Scholar 

  46. Deller JR, et al (1993) Discrete time processing of speech signals. Prentice Hall PTR

  47. Sanei S, Chambers JA (2013) EEG signal processing. John Wiley & Sons

  48. BCI Competition II (2003) [Online].[Accessed 14 August 2018]. Available from http://www.bbci.de/competition/ii/

  49. Schlögl A et al (2002) Estimating the mutual information of an EEG-based brain-computer interface. Biomed Tech/ Biomed Eng 47(1–2):3–8

    Article  Google Scholar 

  50. BCI Competition IV (2008) [Online]. From: http://www.bbci.de/competition /iv/desc2b. pdf. Accessed 14 Aug 2018

  51. BCI competition II: results [Online]. [Accessed 14 August 2018]. http://www.bbci.de/competition/ii/results/TR_BCI2003_III.pdf

  52. Wang X et al (2014) A multiple autocorrelation analysis method for motor imagery EEG feature extraction, In: Control and decision conference (2014 CCDC),the 26th Chinese, IEEE, pp 3000–3004

  53. Stock VN, Balbinot A (2016) Movement imagery classification in MOTIV cap-based system by naïve Bayes. In: Engineering in medicine and biologysociety (EMBC). 2016 IEEE 38th annual international conference of theIEEE, pp. 4435–4438

  54. BCI competition IV: final results [Online]. [Accessed on 14 August 2018]. Available from: http://bbci.de/competition/iv/results/#dataset2b

  55. McFarland DJ, Sarnacki WA, Wolpaw JR (2003) Brain–computer interface (BCI) operation: optimizing information transfer rates. Biol Psychol 63(3):237–251

    Article  Google Scholar 

  56. Pierce J (1980) An introduction to information theory. Dover, New York

    MATH  Google Scholar 

Download references

Funding

No funding recieved for this research work.

Author information

Authors and Affiliations

  1. BIT Mesra, Ranchi, India

    Sumanta Bhattacharyya & Manoj K. Mukul

  2. Cambridge Institute of Technology, Ranchi, India

    Sumanta Bhattacharyya

  3. The PNG University of Technology, Lae, Morobe, Papua New Guinea

    Ashish Kr. Luhach

  4. Federal University of Piauí (UFPI), Teresina, Piauí, Brazil

    Joel J. P. C. Rodrigues

  5. Instituto de Telecomunicações, Aveiro, Portugal

    Joel J. P. C. Rodrigues

Authors
  1. Sumanta Bhattacharyya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manoj K. Mukul
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashish Kr. Luhach
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joel J. P. C. Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashish Kr. Luhach.

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

Bhattacharyya, S., Mukul, M.K., Luhach, A.K. et al. Motor imagery-based neuro-feedback system using neuronal excitation of the active synapses. Ann. Telecommun. 76, 413–428 (2021). https://doi.org/10.1007/s12243-019-00740-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12243-019-00740-8

Keywords

Search

Navigation