Determining the optimal number of MEG trials: A machine learning and speech decoding perspective

Debadatta Dash, Paul Ferrari, Saleem Malik, Albert Montillo, Joseph A Maldjian, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationBrain Informatics - International Conference, BI 2018, Proceedings
EditorsYang Yang, Vicky Yamamoto, Shouyi Wang, Erick Jones, Jianzhong Su, Tom Mitchell, Leon Iasemidis
PublisherSpringer Verlag
Pages163-172
Number of pages10
ISBN (Print)9783030055868
DOIs
StatePublished - Jan 1 2018
EventInternational Conference on Brain Informatics, BI 2018 - Arlington, United States
Duration: Dec 7 2018Dec 9 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11309 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Conference on Brain Informatics, BI 2018
CountryUnited States
CityArlington
Period12/7/1812/9/18

Keywords

  • Artificial neural network
  • MEG
  • Speech
  • Wavelets

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    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 Y. Yang, V. Yamamoto, S. Wang, E. Jones, J. Su, T. Mitchell, & L. Iasemidis (Eds.), Brain Informatics - International Conference, BI 2018, Proceedings (pp. 163-172). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11309 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-05587-5_16