TY - GEN
T1 - Real-time voice activity detection for ECoG-based speech brain machine interfaces
AU - Kanas, Vasileios G.
AU - Mporas, Iosif
AU - Benz, Heather L.
AU - Sgarbas, Kyriakos N.
AU - Bezerianos, Anastasios
AU - Crone, Nathan E.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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 timefrequency 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.
AB - 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 timefrequency 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.
KW - Brain-machine interfaces (BMIs)
KW - Electrocorticography (ECoG)
KW - Time-frequency analysis
KW - Voice activity detection
UR - http://www.scopus.com/inward/record.url?scp=84940747522&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84940747522&partnerID=8YFLogxK
U2 - 10.1109/ICDSP.2014.6900790
DO - 10.1109/ICDSP.2014.6900790
M3 - Conference contribution
AN - SCOPUS:84940747522
T3 - International Conference on Digital Signal Processing, DSP
SP - 862
EP - 865
BT - 2014 19th International Conference on Digital Signal Processing, DSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 19th International Conference on Digital Signal Processing, DSP 2014
Y2 - 20 August 2014 through 23 August 2014
ER -