Voice activity detection from electrocorticographic signals

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

doi:10.1007/978-3-319-00846-2_405

Voice activity detection from electrocorticographic signals

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

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013 - MEDICON 2013
Publisher	Springer Verlag
Pages	1643-1646
Number of pages	4
ISBN (Print)	9783319008455
DOIs	https://doi.org/10.1007/978-3-319-00846-2_405
State	Published - 2014
Event	13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013, MEDICON 2013 - Seville, Spain Duration: Sep 25 2013 → Sep 28 2013

Publication series

Name	IFMBE Proceedings
Volume	41
ISSN (Print)	1680-0737

Other

Other	13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013, MEDICON 2013
Country	Spain
City	Seville
Period	9/25/13 → 9/28/13

Keywords

Brain machine interface
Electrocorticography
Machine learning
Voice activity detection

ASJC Scopus subject areas

Bioengineering
Biomedical Engineering

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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

Voice activity detection from electrocorticographic signals. / Kanas, Vasileios G.; Mporas, I.; Benz, H. L.; Huang, N.; Thakor, N. V.; Sgarbas, K.; Bezerianos, A.; Crone, N. E.

13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013 - MEDICON 2013. Springer Verlag, 2014. p. 1643-1646 (IFMBE Proceedings; Vol. 41).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kanas, VG, Mporas, I, Benz, HL, Huang, N, Thakor, NV, Sgarbas, K, Bezerianos, A & Crone, NE 2014, Voice activity detection from electrocorticographic signals. in 13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013 - MEDICON 2013. IFMBE Proceedings, vol. 41, Springer Verlag, pp. 1643-1646, 13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013, MEDICON 2013, Seville, Spain, 9/25/13. https://doi.org/10.1007/978-3-319-00846-2_405

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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.}",

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AU - Mporas, I.

AU - Benz, H. L.

AU - Huang, N.

AU - Thakor, N. V.

AU - Sgarbas, K.

AU - Bezerianos, A.

AU - Crone, N. E.

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N2 - 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.

AB - 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.

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KW - Electrocorticography

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Voice activity detection from electrocorticographic signals

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Keywords

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