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Characterizing Masseter Surface Electromyography on EEG-Related Frequency Bands in Parkinson’s Disease Neuromotor Dysarthria

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Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications (IWINAC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13258))

Abstract

Speech has proven to be an effective neuromotor biomarker, capitalizing on the capabilities of contact-free technology. This study aims to evaluate the behavior of facial muscles’ activity estimating the entropy of their surface electromyographic (sEMG) activity during the production of diadochokinetic speech tests. The study explores the entropic behavior of the sEMG signal in certain frequency bands associated to EEG activity comparing participants affected by neuromotor diseases than in age-matched normative participants. Using recordings from two PD vs two HC participants on 5 EEG bands (\(\delta , \vartheta , \alpha , \beta , \gamma \)), the maximum entropy estimated on the HC group was 5.70 10\(^{-5}\), whereas the minimum entropy on the PD group was 7.25 10\(^{-5}\). A hypothesis test rejected the similarity between the PD and HC results with a p-value under 0.0003. This different behavior might open the way to a wider study in characterizing neuromotor disease alterations from neuromotor origin.

This research received funding from grants TEC2016-77791-C4-4-R (Ministry of Economic Affairs and Competitiveness of Spain), and Teca-Park-MonParLoc FGCSIC-CENIE 0348-CIE-6-E (InterReg Programme). The authors wish to thank Víctor Lorente for his inspiring thoughts (School of Veterinary, UCM, Spain).

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References

  1. Deuschl, G., et al.: The burden of neurological diseases in Europe: an analysis for the Global Burden of Disease Study 2017. Lancet Public Health 5, e551–e567 (2020). https://doi.org/10.1016/S2468-2667(20)30190-0

    Article  PubMed  Google Scholar 

  2. Duffy, J.R.: Motor Speech Disorders: Substrates, Differential Diagnosis, and Management, 3rd edn. Elsevier, St. Louis (2013)

    Google Scholar 

  3. Brambilla, C., Pirovano, I., Mira, R.M., Rizzo, G., Scano, A., Mastropietro, A.: Combined use of EMG and EEG techniques for neuromotor assessment in rehabilitative applications: a systematic review. Sensors 21(21), 7014 (2021). https://doi.org/10.3390/s21217014

    Article  PubMed  PubMed Central  Google Scholar 

  4. Gómez, P., et al.: Neuromechanical modelling of articulatory movements from surface electromyography and speech formants. Int. J. Neural Syst. 29(02), 1850039 (2019). https://doi.org/10.1142/S0129065718500399

    Article  Google Scholar 

  5. Cram, J.R.: The history of surface electromyography. Appl. Psychophys. Biofeedback 28(2), 89–91 (2003). https://doi.org/10.1023/A:1023802407132

    Article  Google Scholar 

  6. Baker, S.N., Olivier, E., Lemon, R.N.: Coherent oscillations in monkey motor cortex and hand muscle EMG show task-dependent modulation. J. Physiol. 501, 225–41 (1997). https://doi.org/10.1111/j.1469-7793.1997.225bo.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Krauth, R., et al.: Cortico-muscular coherence is reduced acutely post-stroke and increases bilaterally during motor recovery: a pilot study. Front. Neurol. 10, 126 (2019). https://doi.org/10.3389/fneur.2019.00126

  8. Gómez, A., Tsanas, A., Gómez, P., Palacios-Alonso, D., Rodellar, V., Álvarez, A.: Acoustic to kinematic projection in Parkinson’s Disease Dysarthria. Biomed. Signal Process. Control 66, 102422 (2021). https://doi.org/10.1016/j.bspc.2021.102422

  9. Palmer, S.J., Lee, P.W.H., Wang, Z.J., Au, W.L., McKeown, M.J.: \(\vartheta \), \(\beta \) But not \(\alpha \)-band EEG connectivity has implications for dual task performance in Parkinson’s disease. Park. Relat. Disord. 16(6), 393–397 (2010). https://doi.org/10.1016/j.parkreldis.2010.03.001

    Article  Google Scholar 

  10. Hülsdünker, T., Mierau, A., Neeb, C., Kleinöder, H., Strüder, H.K.: Cortical processes associated with continuous balance control as revealed by EEG spectral power. Neurosci. Lett. 592, 1–5 (2015). https://doi.org/10.1016/j.neulet.2015.02.049

    Article  CAS  PubMed  Google Scholar 

  11. Ofner, P., Schwarz, A., Pereira, J., Müller-Putz, G.R.: Upper limb movements can be decoded from the time-domain of low-frequency EEG. PLoS ONE 12(8), e0182578 (2017). https://doi.org/10.1371/journal.pone.0182578

  12. Liu, J., Sheng, Y., Liu, H.: Corticomuscular coherence and its applications: a review. Front. Hum. Neurosci. 13, 100 (2019). https://doi.org/10.3389/fnhum.2019.00100

    Article  PubMed  PubMed Central  Google Scholar 

  13. Hickok, G.: A cortical circuit for voluntary laryngeal control: implications for the evolution language. Psychonomic Bull. Rev. 24(1), 56–63 (2016). https://doi.org/10.3758/s13423-016-1100-z

    Article  Google Scholar 

  14. Kent, R.D., Duffy, J.R., Slama, A., Kent, J.F., Clift, A.: Clinicoanatomic studies in dysarthria. J. Speech Lang. Hear. Res. 44(3), 535–551 (2001). https://doi.org/10.1044/1092-4388(2001/042)

    Article  CAS  PubMed  Google Scholar 

  15. Cover, T.M., Thomas, J.A.: Elements of Information Theory, 2nd edn. Wiley, Hoboken (2006)

    Google Scholar 

  16. Delgado-Bonal, A., Marshak, A.: Approximate entropy and sample entropy: a comprehensive tutorial. Entropy 21(6), 541 (2019). https://doi.org/10.3390/e21060541

    Article  PubMed Central  Google Scholar 

  17. Costa, M., Goldberger, A.L., Peng, C.K.: Multiscale entropy analysis of biological signals. Phys. Rev. E 71(2), 021906 (2005). https://doi.org/10.1103/PhysRevE.71.021906

  18. Vicente, R., Wibral, M., Lindner, M., Pipa, G.: Transfer entropy-a model-free measure of effective connectivity for the neurosciences. J. Comput. Neurosci. 30, 45–67 (2011). https://doi.org/10.1007/s10827-010-0262-3

    Article  PubMed  Google Scholar 

  19. Bowman, A.W., Azzalini, A.: Computational aspects of nonparametric smoothing with illustrations from the SM library. Comp. Stat. Data Anal. 42, 545–560 (2003). https://doi.org/10.1016/S0167-9473(02)00118-4

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Medicine, Usher Institute, University of Edinburgh, Edinburgh, UK

    Andrés Gómez-Rodellar & Athanasios Tsanas

  2. NeuSpeLab, CTB, Universidad Politécnica de Madrid, 28220, Pozuelo de Alarcón, Madrid, Spain

    Pedro Gómez-Vilda

  3. Universidad Politécnica de Cartagena, Campus Muralla del Mar, Pza. Hospital 1, 30202, Cartagena, Spain

    JoséManuel Ferrández-Vicente

Authors
  1. Andrés Gómez-Rodellar
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  2. Pedro Gómez-Vilda
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  3. JoséManuel Ferrández-Vicente
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  4. Athanasios Tsanas
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Corresponding author

Correspondence to Pedro Gómez-Vilda .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Cartagena, Cartagena, Spain

    José Manuel Ferrández Vicente

  2. Universidad Nacional de Educación a Distancia, Madrid, Spain

    José Ramón Álvarez-Sánchez

  3. Universidad Nacional de Educación a Distancia, Madrid, Spain

    Félix de la Paz López

  4. Ohio State University, Columbus, OH, USA

    Hojjat Adeli

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Gómez-Rodellar, A., Gómez-Vilda, P., Ferrández-Vicente, J., Tsanas, A. (2022). Characterizing Masseter Surface Electromyography on EEG-Related Frequency Bands in Parkinson’s Disease Neuromotor Dysarthria. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_22

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  • DOI: https://doi.org/10.1007/978-3-031-06242-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06241-4

  • Online ISBN: 978-3-031-06242-1

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