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|Title:||Voice activity detection from electrocorticographic signals||Authors:||Kanas, V.G.
|Keywords:||Brain machine interface
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|
|Appears in Collections:||Staff Publications|
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