NDLI: Real-time voice activity detection for ECoG-based speech brain machine interfaces

Content Provider	IEEE Xplore Digital Library
Author	Kanas, V.G. Mporas, I. Benz, H.L. Sgarbas, K.N. Bezerianos, A. Crone, N.E.
Copyright Year	2014
Description	Author affiliation: Singapore Inst. for Neurotechnology, Nat. Univ. of Singapore, Singapore, Singapore (Bezerianos, A.) \|\| Dept. of Biomed. Eng., Johns Hopkins Univ., Baltimore, MD, USA (Benz, H.L.) \|\| Dept. of Electr. & Comput. Eng., Univ. of Patras, Patras, Greece (Kanas, V.G.; Sgarbas, K.N.) \|\| Dept. of Mech. Eng., TEI of Western Greece, Patras, Greece (Mporas, I.) \|\| Dept. of Neurology, Johns Hopkins Univ., Baltimore, MD, USA (Crone, N.E.)
Abstract	In this article, we investigated the performance of a real-time voice activity detection module exploiting different time-frequency methods for extracting signal features in a subject with implanted electrocorticographic (ECoG) electrodes. We used ECoG signals recorded while the subject performed a syllable repetition task. The voice activity detection module used, as input, ECoG data streams, on which it performed feature extraction and classification. With this approach we were able to detect voice activity (speech onset and offset) from ECoG signals with high accuracy. The results demonstrate that different time-frequency representations carried complementary information about voice activity, with the S-transform achieving 92% accuracy using the 86 best features and support vector machines as the classifier. The proposed real-time voice activity detector may be used as a part of an automated natural speech BMI system for rehabilitating individuals with communication deficits.
Starting Page	862
Ending Page	865
File Size	379333
Page Count	4
File Format	PDF
ISBN	9781479946129
ISSN	21653577
DOI	10.1109/ICDSP.2014.6900790
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Feature extraction Accuracy Time-frequency analysis Electrodes Real-time systems Digital signal processing voice activity detection Brain-machine interfaces (BMIs) electrocorticography (ECoG) time-frequency analysis
Content Type	Text
Resource Type	Article

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