Voice activity detection from electrocorticographic signals

Vasileios G. Kanas, I. Mporas, H. L. Benz, N. Huang, N. V. Thakor, K. Sgarbas, anastasios Bezerianos, N. E. Crone

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

Abstract

The purpose of this study was to explore voice activity detection (VAD) in a subject with implanted electrocorticographic (ECoG) electrodes. Accurate VAD is an important preliminary step before decoding and reconstructing speech from ECoG. For this study we used ECoG signals recorded while a subject performed a picture naming task. We extracted time-domain features from the raw ECoG and spectral features from the ECoG high gamma band (70-110Hz). The RelieF algorithm was used for selecting a subset of features to use with seven machine learning algorithms for classification. With this approach we were able to detect voice activity from ECoG signals, achieving a high accuracy using the 100 best features from all electrodes (96%) or only 12 features from the two best electrodes (94%) using the support vector machines or a linear regression classifier. These findings may contribute to the development of ECoG-based brain machine interface (BMI) systems for rehabilitating individuals with communication impairments.

Original languageEnglish (US)
Title of host publication13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013 - MEDICON 2013
PublisherSpringer Verlag
Pages1643-1646
Number of pages4
ISBN (Print)9783319008455
DOIs
StatePublished - Jan 1 2014
Event13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013, MEDICON 2013 - Seville, Spain
Duration: Sep 25 2013Sep 28 2013

Publication series

NameIFMBE Proceedings
Volume41
ISSN (Print)1680-0737

Other

Other13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013, MEDICON 2013
CountrySpain
CitySeville
Period9/25/139/28/13

Keywords

  • Brain machine interface
  • Electrocorticography
  • Machine learning
  • Voice activity detection

ASJC Scopus subject areas

  • Bioengineering
  • Biomedical Engineering

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  • Cite this

    Kanas, V. G., Mporas, I., Benz, H. L., Huang, N., Thakor, N. V., Sgarbas, K., Bezerianos, A., & Crone, N. E. (2014). Voice activity detection from electrocorticographic signals. In 13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013 - MEDICON 2013 (pp. 1643-1646). (IFMBE Proceedings; Vol. 41). Springer Verlag. https://doi.org/10.1007/978-3-319-00846-2_405