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A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG

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Abstract

A stable grasp is attained through appropriate hand preshaping and precise fingertip forces. Here, we have proposed a method to decode grasp patterns from motor imagery and subsequent fingertip force estimation model with a slippage avoidance strategy. We have developed a feature-based classification of electroencephalography (EEG) associated with imagination of the grasping postures. Chaotic behaviour of EEG for different grasping patterns has been utilised to capture the dynamics of associated motor activities. We have computed correlation dimension (CD) as the feature and classified with “one against one” multiclass support vector machine (SVM) to discriminate between different grasping patterns. The result of the analysis showed varying classification accuracies at different subband levels. Broad categories of grasping patterns, namely, power grasp and precision grasp, were classified at a 96.0% accuracy rate in the alpha subband. Furthermore, power grasp subtypes were classified with an accuracy of 97.2% in the upper beta subband, whereas precision grasp subtypes showed relatively lower 75.0% accuracy in the alpha subband. Following assessment of fingertip force distributions while grasping, a nonlinear autoregressive (NAR) model with proper prediction of fingertip forces was proposed for each grasp pattern. A slippage detection strategy has been incorporated with automatic recalibration of the regripping force. Intention of each grasp pattern associated with corresponding fingertip force model was virtualised in this work. This integrated system can be utilised as the control strategy for prosthetic hand in the future.

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References

  1. Acharya S, Fifer MS, Benz HL, Crone NE, Thakor NV (2010) Electrocorticographic amplitude predicts finger positions during slow grasping motions of the hand. J Neural Eng 7(4):046,002

    Article  Google Scholar 

  2. Agashe HA, Contreras-Vidal JL (2013) Decoding the evolving grasping gesture from electroencephalographic (EEG) activity. In: 2013 35th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5590–5593

  3. Agashe HA, Paek AY, Zhang Y, Contreras-Vidal JL (2015) Global cortical activity predicts shape of hand during grasping. Front Neurosci 9:121

    Article  PubMed  PubMed Central  Google Scholar 

  4. Benmouiza K, Cheknane A (2013) Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models. Energy Convers Manag 75:561–569

    Article  Google Scholar 

  5. Bennett KP, Campbell C (2000) Support vector machines: hype or hallelujah? Acm Sigkdd Explor Newslett 2(2):1–13

    Article  Google Scholar 

  6. Berk RA (2016) Erratum to: statistical learning from a regression perspective. In: Statistical learning from a regression perspective. Springer, p 61

  7. Birznieks I, Jenmalm P, Goodwin AW, Johansson RS (2001) Encoding of direction of fingertip forces by human tactile afferents. J Neurosci 21(20):8222–8237

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowl Discov 2(2):121–167

    Article  Google Scholar 

  9. Cadoret G, Smith AM (1996) Friction, not texture, dictates grip forces used during object manipulation. J Neurophysiol 75(5):1963–1969

    Article  CAS  PubMed  Google Scholar 

  10. Cao L (1997) Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlin Phenom 110(1-2):43–50

    Article  Google Scholar 

  11. Chow T, Leung CT (1996) Nonlinear autoregressive integrated neural network model for short-term load forecasting. IEE Proc-Gen Transm Distrib 143(5):500–506

    Article  Google Scholar 

  12. Collinger JL, Wodlinger B, Downey JE, Wang W, Tyler-Kabara EC, Weber DJ, McMorland AJ, Velliste M, Boninger ML, Schwartz AB (2013) High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381(9866):557–564

    Article  PubMed  PubMed Central  Google Scholar 

  13. Cordella F, Gentile C, Zollo L, Barone R, Sacchetti R, Davalli A, Siciliano B, Guglielmelli E (2016) A force-and-slippage control strategy for a poliarticulated prosthetic hand. In: 2016 IEEE International conference on robotics and automation (ICRA). IEEE, pp 3524–3529

  14. Debnath R, Takahide N, Takahashi H (2004) A decision based one-against-one method for multi-class support vector machine. Pattern Anal Applic 7(2):164–175

    Article  Google Scholar 

  15. Eder C, Sokić D, Ċoviċković-Ṡternić N, Mijajlović M, Savić M, Sinkjær T, Popović D (2006) Symmetry of post-movement beta-ERS, and motor recovery from stroke: a low-resolution EEG pilot study. Eur J Neurol 13(12):1312–1323

    Article  CAS  PubMed  Google Scholar 

  16. Engeberg ED, Meek SG (2013) Adaptive sliding mode control for prosthetic hands to simultaneously prevent slip and minimize deformation of grasped objects. IEEE/ASME Trans Mechatron 18(1):376–385

    Article  Google Scholar 

  17. Erfanian A, Moghaddas S (1999) Prediction of hand movement from the event-related EEG using neural networks. In: [Engineering in medicine and biology, 1999. 21st Annual conference and the 1999 annual fall meetring of the biomedical engineering society] BMES/EMBS Conference, 1999. Proceedings of the first joint, vol 1. IEEE, pp 430–vol

  18. Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP (2006) Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099):164

    Article  CAS  PubMed  Google Scholar 

  19. Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD, Vogel J, Haddadin S, Liu J, Cash SS, van der Smagt P et al (2012) Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485(7398):372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425

    Article  PubMed  Google Scholar 

  21. Iasemidis LD, Olson LD, Savit RS, Sackellares JC (1994) Time dependencies in the occurrences of epileptic seizures. Epilepsy Res 17(1):81–94

    Article  CAS  PubMed  Google Scholar 

  22. Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37

    Article  Google Scholar 

  23. Kubanek J, Miller K, Ojemann J, Wolpaw J, Schalk G (2009) Decoding flexion of individual fingers using electrocorticographic signals in humans. J Neural Eng 6(6):066,001

    Article  CAS  Google Scholar 

  24. Kuiken TA, Dumanian G, Lipschutz R, Miller L, Stubblefield K (2004) The use of targeted muscle reinnervation for improved myoelectric prosthesis control in a bilateral shoulder disarticulation amputee. Prosthet Orthot Int 28(3):245–253

    CAS  PubMed  Google Scholar 

  25. Kuiken TA, Li G, Lock BA, Lipschutz RD, Miller LA, Stubblefield KA, Englehart KB (2009) Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. Jama 301(6):619–628

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lee E (1999) Force and impact control for robot manipulators using time delay. In: Proceedings of the IEEE international symposium on industrial electronics, 1999. ISIE’99, vol 1. IEEE, pp 151–156

  27. Li G, Schultz AE, Kuiken TA (2010) Quantifying pattern recognition—based myoelectric control of multifunctional transradial prostheses. IEEE Trans Neural Syst Rehab Eng Publ IEEE Eng Med Biol Soc 18(2):185

    Article  Google Scholar 

  28. Miller K, Zanos S, Fetz E, Den Nijs M, Ojemann J (2009) Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans. J Neurosci 29(10):3132–3137

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Napier JR, Tuttle RH (1993) Hands. Princeton University Press

  30. Naranjo J, Brovelli A, Longo R, Budai R, Kristeva R, Battaglini P (2007) EEG dynamics of the frontoparietal network during reaching preparation in humans. Neuroimage 34(4):1673–1682

    Article  CAS  PubMed  Google Scholar 

  31. Pfurtscheller G, Da Silva FL (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110(11):1842–1857

    Article  CAS  PubMed  Google Scholar 

  32. Pistohl T, Schulze-Bonhage A, Aertsen A, Mehring C, Ball T (2012) Decoding natural grasp types from human ECoG. Neuroimage 59(1):248–260

    Article  PubMed  Google Scholar 

  33. Roy R, Mahadevappa M, Kumar C (2016) Trajectory path planning of EEG controlled robotic arm using GA. Procedia Comput Sci 84:147–151

    Article  Google Scholar 

  34. Roy R, Sikdar D, Mahadevappa M, Kumar C (2017) EEG based motor imagery study of time domain features for classification of power and precision hand grasps. In: 2017 8th International IEEE/EMBS conference on neural engineering (NER). IEEE, pp 440–443

  35. Salenius S, Salmelin R, Neuper C, Pfurtscheller G, Hari R (1996) Human cortical 40 hz rhythm is closely related to emg rhythmicity. Neurosci Lett 213(2):75–78

    Article  CAS  PubMed  Google Scholar 

  36. Takens F et al. (1981) Dynamical systems and turbulence. Lect Notes Math 898(9):366

    Article  Google Scholar 

  37. Velliste M, Perel S, Spalding MC, Whitford AS, Schwartz AB (2008) Cortical control of a prosthetic arm for self-feeding. Nature 453(7198):1098

    Article  CAS  PubMed  Google Scholar 

  38. Wheaton L, Fridman E, Bohlhalter S, Vorbach S, Hallett M (2009) Left parietal activation related to planning, executing and suppressing praxis hand movements. Clin Neurophysiol 120(5):980–986

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wheaton LA, Carpenter M, Mizelle J, Forrester L (2008) Preparatory band specific premotor cortical activity differentiates upper and lower extremity movement. Exper Brain Res 184(1):121–126

    Article  Google Scholar 

  40. Williams GP (1997) Chaos theory tamed. Joseph Henry Press

  41. Wodlinger B, Downey J, Tyler-Kabara E, Schwartz A, Boninger M, Collinger J (2014) Ten-dimensional anthropomorphic arm control in a human brain- machine interface: difficulties, solutions, and limitations. J Neural Eng 12(1):016,011

    Article  Google Scholar 

  42. Xu Y, Hollerbach JM, Ma D (1995) A nonlinear PD controller for force and contact transient control. IEEE Control Syst Mag 15(1):15–21

    Article  Google Scholar 

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Funding

The authors acknowledge INSPIRE Fellowship, Department of Science & Technology, Ministry of Science & Technology, Govt. of India and Sponsored Research & Industrial Consultancy (SRIC), Indian Institute of Technology Kharagpur, No: IIT/SRIC/ME-SMST/RNC/2014-15/15, dated: 21-01-2014, for providing the financial support. The authors state that they have no financial conflicts while preparing this document.

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Authors and Affiliations

  1. Advanced Technology and Development Centre, Indian Institute of Technology, Kharagpur, India

    Rinku Roy

  2. School of Medical Science & Technology, Indian Institute of Technology, Kharagpur, India

    Debdeep Sikdar & Manjunatha Mahadevappa

  3. Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, India

    C. S. Kumar

Authors
  1. Rinku Roy
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  2. Debdeep Sikdar
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  3. Manjunatha Mahadevappa
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  4. C. S. Kumar
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Correspondence to Manjunatha Mahadevappa.

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This research adhered to the tenets of the Declaration of Helsinki. Written consents were taken from the subjects before participating in the experiment.

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The authors declare that they have no conflict of interest.

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Roy, R., Sikdar, D., Mahadevappa, M. et al. A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG. Med Biol Eng Comput 56, 2095–2107 (2018). https://doi.org/10.1007/s11517-018-1833-0

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  • DOI: https://doi.org/10.1007/s11517-018-1833-0

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