Time-varying parametric modeling of ECoG for syllable decoding

Vasileios G. Kanas, Iosif Mporas, Griffin W. Milsap, Kyriakos N. Sgarbas, Nathan E. Crone, Anastasios Bezerianos

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


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.

Original languageEnglish
Title of host publicationBrain Informatics and Health - 8th International Conference, BIH 2015, Proceedings
EditorsYike Guo Y., Sean Hill S., Karl Friston, Hanchuan Peng, Aldo Faisal A.
PublisherSpringer Nature
Number of pages10
ISBN (Print)9783319233437
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event8th International Conference on Brain Informatics and Health, BIH 2015 - London, United Kingdom
Duration: 30 Aug 20152 Sept 2015

Publication series

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


Conference8th International Conference on Brain Informatics and Health, BIH 2015
Country/TerritoryUnited Kingdom


  • Brain machine interface
  • Electrocorticography
  • Speech rehabilitation
  • Time-varying autoregressive mode


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