Time-Varying Parametric Modeling of ECoG for Syllable Decoding

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9250)


As a step toward developing neuroprostheses, the purpose of this study is to explore syllable decoding in a subject with implanted electrocorticographic (ECoG) recordings. For this study, we use ECoG signals recorded while a subject volunteered to perform a task in which the patient has been visually cued to speak isolated consonant-vowel syllables varying in their articulatory features. We propose a recursive estimation method to calculate the parametric model coefficients in each time instant and band power features from individual ECoG sites are extracted to decode the articulated syllables. Our findings may contribute to the development of brain machine interface (BMI) systems for syllable-level speech rehabilitation in handicapped individuals.


Electrocorticography Time-varying autoregressive model Speech rehabilitation Brain machine interface 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of PatrasPatrasGreece
  2. 2.Computer Informatics Engineering DepartmentTEI of Western GreecePatrasGreece
  3. 3.Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA
  4. 4.Department of NeurologyJohns Hopkins UniversityBaltimoreUSA
  5. 5.Department of Medical Physics, School of MedicineUniversity of PatrasPatrasGreece
  6. 6.Singapore Institute for NeurotechnologyNational University of SingaporeSingaporeSingapore

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