Voice Activity Detection from Electrocorticographic Signals

Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 41)


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.


Voice activity detection electrocorticography brain machine interface machine learning 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of PatrasPatrasGreece
  2. 2.Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA
  3. 3.Singapore Institute for NeurotechnologyNational University of SingaporeSingaporeSingapore
  4. 4.Department of NeurologyJohns Hopkins UniversityBaltimoreUSA

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