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Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective

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Book cover Brain Informatics (BI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11309))

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Abstract

Advancing the knowledge about neural speech mechanisms is critical for developing next-generation, faster brain computer interface to assist in speech communication for the patients with severe neurological conditions (e.g., locked-in syndrome). Among current neuroimaging techniques, Magnetoencephalography (MEG) provides direct representation for the large-scale neural dynamics of underlying cognitive processes based on its optimal spatiotemporal resolution. However, the MEG measured neural signals are smaller in magnitude compared to the background noise and hence, MEG usually suffers from a low signal-to-noise ratio (SNR) at the single-trial level. To overcome this limitation, it is common to record many trials of the same event-task and use the time-locked average signal for analysis, which can be very time consuming. In this study, we investigated the effect of the number of MEG recording trials required for speech decoding using a machine learning algorithm. We used a wavelet filter for generating the denoised neural features to train an Artificial Neural Network (ANN) for speech decoding. We found that wavelet based denoising increased the SNR of the neural signal prior to analysis and facilitated accurate speech decoding performance using as few as 40 single-trials. This study may open up the possibility of limiting MEG trials for other task evoked studies as well.

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References

  1. Indefrey, P., Levelt, W.J.M.: The spatial and temporal signatures of word production components. Cognition 92(1), 101–144 (2004)

    Article  Google Scholar 

  2. Booth, J.R., Wood, L., Lu, D., Houk, J.C., Bitan, T.: The role of the basal ganglia and cerebellum in language processing. Brain Res. 1133, 136–144 (2007)

    Article  Google Scholar 

  3. Ackermann, H.: Cerebellar contributions to speech production and speech perception psycholinguistic and neurobiological perspectives. Trends Neurosci. 31(6), 256–272 (2008)

    Article  Google Scholar 

  4. Laureys, S.: The locked-in syndrome: what is it like to be conscious but paralyzed and voiceless? Progress Brain Res. 150, 495–611 (2005). The Boundaries of Consciousness: Neurobiology and Neuropathology

    Article  Google Scholar 

  5. Duffy, J.: Motor Speech Disorders Substrates, Differential Diagnosis, and Management, 3rd edn, p. 295. Elsevier, St. Louis (2012)

    Google Scholar 

  6. Herff, C., Schultz, T.: Automatic speech recognition from neural signals: a focused review. Front. Neurosci. 10, 429 (2016)

    Article  Google Scholar 

  7. Wolpaw, J.R., Mcfarland, D.: Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. PNAS 51, 49–54 (2004)

    Google Scholar 

  8. Yoshimura, N., et al.: Decoding of covert vowel articulation using electroencephalography cortical currents. Front. Neurosci. 10, 175 (2016)

    Article  Google Scholar 

  9. Birbaumer, N.: Brain computer-interface research: coming of age. Clin. Neurophysiol. 117(3), 479–483 (2006)

    Article  Google Scholar 

  10. Brumberg, J.S., et al.: Brain computer interfaces for speech communication. Speech Commun. 52(4), 367–379 (2010)

    Article  MathSciNet  Google Scholar 

  11. Leuthardt, E.C., Cunningham, J., Barbour, D.: Brain-computer interface research. In: Guger, C., Allison, B., Edlinger, G. (eds.) Towards a Speech BCI Using ECoG. SpringerBriefs in Electrical and Computer Engineering, pp. 93–110. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36083-1_10

    Chapter  Google Scholar 

  12. Herff, C., et al.: Brain-to-text: decoding spoken phrases from phone representations in the brain. Front. Neurosci. 9(217), 1–11 (2005)

    Google Scholar 

  13. Dash, D., Abrol, B., Sao, A., Biswal, B.: The model order limit: deep sparse factorization for resting brain. In: IEEE 15th International Symposium on Biomedical Imaging (ISBI), pp. 1244–1247 (2018)

    Google Scholar 

  14. Dash, D., Abrol, B., Sao, A., Biswal, B.: Spatial sparsification and low rank projection for fast analysis of multi-subject resting state fMRI data. In: IEEE 15th International Symposium on Biomedical Imaging (ISBI), pp. 1280–1283 (2018)

    Google Scholar 

  15. Formisano, E., De Martino, F., Bonte, M., Goebel, R.: Who is saying what? Brain-based decoding of human voice and speech. Science 322, 970–973 (2008)

    Article  Google Scholar 

  16. Cohen, D., Cuffin, B.N.: Demonstration of useful differences between magnetoencephalogram and electroencephalogram. Electroencephalogr. Clin. Neurophysiol. 56(1), 38–51 (1983)

    Article  Google Scholar 

  17. Chan, A.M., et al.: Decoding word and category-specific spatiotemporal representations from MEG and EEG. NeuroImage 54(4), 3028–3039 (2011)

    Article  Google Scholar 

  18. Wang, J., Kim, M., Hernandez-Mulero, A.H., Heitzman, D., Ferrari, P.: Towards decoding speech production from single-trial Magnetoencephalography (MEG) signals. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3036–3040 (2017)

    Google Scholar 

  19. Dash, D., Ferrari, P., Malik, S., Wang, J.: Overt speech retrieval from neuromagnetic signals using wavelets and artificial neural networks. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP) (2018)

    Google Scholar 

  20. Gross, J., Baillet, S., Barnes, G.R., Henson, R.N., Hillebrand, A., Jensen, O., Schoffelen, J.-M.: Good practice for conducting and reporting MEG research. Neuroimage 65(100), 349–363 (2013)

    Article  Google Scholar 

  21. Attal, Y., et al.: Modelling and detecting deep brain activity with MEG and EEG. IRBM - Biomed. Eng. Res. 30, 133 (2009)

    Google Scholar 

  22. Burgess, R.C., Funke, M.E., Bowyer, S.M., Lewine, J.F., Kirsch, H.E., Bagi, A.I.: American clinical magnetoencephalography society clinical practice guideline 2: presurgical functional brain mapping using magnetic evoked fields. J. Clin. Neurophysiol. 28, 355–361 (2011)

    Article  Google Scholar 

  23. Grill-Spector, K., Henson, R., Martin, A.: Repetition and the brain: neural models of stimulus-specific effects. Trends Cogn. Sci. 10(1), 14–23 (2006)

    Article  Google Scholar 

  24. Cheyne, D., Ferrari, P.: MEG studies of motor cortex gamma oscillations: evidence for a gamma fingerprint in the brain? Front. Hum. Neurosci. 7, 575 (2013)

    Article  Google Scholar 

  25. Dash. D., Kim, M., Ferrari, P., Wang, J.: Brain activation pattern analysis for speech production decoding from MEG signals. In: 25th Annual meeting of Biomedical Engineering Society (BMES) (2018)

    Google Scholar 

  26. Tadel, F., Baillet, S., Mosher, J.C., Pantazis, D., Leahy, R.M.: Brainstorm: a user-friendly Application for MEG/EEG analysis. Comput. Intell. Neurosci. 8, 8:1–8:13 (2011)

    Google Scholar 

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Acknowledgment

This project was supported by the University of Texas System Brain Research grant and by the National Institutes of Health (NIH) under award number R03 DC013990. We thank Dr. Mark McManis, Dr. Ted Mau, Dr. Angel W. Hernandez-Mulero, Kanishk Goel and the volunteering participants.

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Authors and Affiliations

  1. Department of Bioengineering, University of Texas at Dallas, Richardson, USA

    Debadatta Dash & Jun Wang

  2. Department of Psychology, University of Texas at Austin, Austin, USA

    Paul Ferrari

  3. MEG Laboratory, Dell Children’s Medical Center, Austin, USA

    Paul Ferrari

  4. MEG Lab, Cook Children’s Hospital, Fort Worth, TX, USA

    Saleem Malik

  5. Department of Radiology, UT Southwestern Medical Center, Dallas, USA

    Albert Montillo & Joseph A. Maldjian

  6. Department of Bioinformatics, UT Southwestern Medical Center, Dallas, USA

    Albert Montillo

  7. Callier Center for Communication Disorders, University of Texas at Dallas, Richardson, USA

    Jun Wang

Authors
  1. Debadatta Dash
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  2. Paul Ferrari
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  3. Saleem Malik
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  4. Albert Montillo
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  5. Joseph A. Maldjian
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  6. Jun Wang
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Corresponding author

Correspondence to Debadatta Dash .

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Editors and Affiliations

  1. University of Texas at Arlington, Arlington, TX, USA

    Shouyi Wang

  2. University of Southern California, West Hollywood, USA

    Vicky Yamamoto

  3. Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA

    Jianzhong Su

  4. Maebashi Institute of Technology, Gunma, Japan

    Yang Yang

  5. The University of Texas at Arlington, Arlington, USA

    Erick Jones

  6. Louisiana Tech University, Arlington, TX, USA

    Leon Iasemidis

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Tom Mitchell

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Dash, D., Ferrari, P., Malik, S., Montillo, A., Maldjian, J.A., Wang, J. (2018). Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_16

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  • DOI: https://doi.org/10.1007/978-3-030-05587-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05586-8

  • Online ISBN: 978-3-030-05587-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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