Optimal selection of electrocorticographic sensors for voice activity detection

Vasileios G. Kanas, Iosif Mporas, Heather L. Benz, Kyriakos N. Sgarbas, Nathan E Crone, Anastasios Bezerianos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An effective speech brain machine interface requires selecting the best cortical recording sites and signal features for decoding speech production, but also minimal clinical risk for the patient. Motivated by this need to reduce patient risk, the purpose of this study is to detect voice activity (speech onset and offset) automatically from spatial-spectral features of electrocorticographic signals using the optimal number of sensors (minimal invasiveness). ECoG signals were recorded while a subject performed two different syllable repetition tasks. We found that the optimal frequency resolution for detecting voice activity is 8 Hz using 31 sensors out of 55, achieving 98.2% accuracy by employing support vector machines (SVM) as a classifier, and that acceptable accuracy of 96.7% was achieved using 15 sensors, which would permit a less invasive surgery for the placement of electrodes. The proposed voice activity detector may be utilized as a part of an ECoG-based automated natural speech BMI system.

Original languageEnglish (US)
Title of host publication2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages29-32
Number of pages4
ISBN (Print)9781479951994
DOIs
Publication statusPublished - Mar 19 1997
Event2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 - Singapore, Singapore
Duration: Dec 10 2014Dec 12 2014

Other

Other2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
CountrySingapore
CitySingapore
Period12/10/1412/12/14

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Keywords

  • Brain machine interface (BMI)
  • electrocorticography (ECoG)
  • minimal clinical risk
  • voice activity detection (VAD)

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Artificial Intelligence
  • Control and Systems Engineering

Cite this

Kanas, V. G., Mporas, I., Benz, H. L., Sgarbas, K. N., Crone, N. E., & Bezerianos, A. (1997). Optimal selection of electrocorticographic sensors for voice activity detection. In 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 (pp. 29-32). [7064274] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICARCV.2014.7064274