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

• Published in: International Conference on Digital Signal Processing proceedings pp. 862 - 865
• Main Authors: Kanas, Vasileios G., Mporas, Iosif, Benz, Heather L., Sgarbas, Kyriakos N., Bezerianos, Anastasios, Crone, Nathan E.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2014
• Subjects: Accuracy; Brain-machine interfaces (BMIs); Digital signal processing; electrocorticography (ECoG); Electrodes; Feature extraction; Real-time systems; Speech; Time-frequency analysis; voice activity detection
• ISSN: 1546-1874
• DOI: 10.1109/ICDSP.2014.6900790

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.