Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-319-00846-2_405
Title: Voice activity detection from electrocorticographic signals
Authors: Kanas, V.G.
Mporas, I.
Benz, H.L.
Huang, N.
Thakor, N.V. 
Sgarbas, K.
Bezerianos, A. 
Crone, N.E.
Keywords: Brain machine interface
Electrocorticography
Machine learning
Voice activity detection
Issue Date: 2014
Citation: 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. IFMBE Proceedings 41 : 1643-1646. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-319-00846-2_405
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. © Springer International Publishing Switzerland 2014.
Source Title: IFMBE Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/128688
ISBN: 9783319008455
ISSN: 16800737
DOI: 10.1007/978-3-319-00846-2_405
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