@inproceedings{cdceb1bd33914925bcbd426670a75cc8,
title = "Voice activity detection from electrocorticographic signals",
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.",
keywords = "Brain machine interface, Electrocorticography, Machine learning, Voice activity detection",
author = "Kanas, {Vasileios G.} and I. Mporas and Benz, {H. L.} and N. Huang and Thakor, {N. V.} and K. Sgarbas and A. Bezerianos and Crone, {N. E.}",
year = "2014",
doi = "10.1007/978-3-319-00846-2_405",
language = "English (US)",
isbn = "9783319008455",
series = "IFMBE Proceedings",
publisher = "Springer Verlag",
pages = "1643--1646",
booktitle = "13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013 - MEDICON 2013",
note = "13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013, MEDICON 2013 ; Conference date: 25-09-2013 Through 28-09-2013",
}