Document Detail

Joint Spatial-Spectral Feature Space Clustering for Speech Activity Detection from ECoG Signals.
MedLine Citation:
PMID:  24658248     Owner:  NLM     Status:  In-Data-Review    
Brain-machine interfaces for speech restoration have been extensively studied for more than two decades. The success of such a system will depend in part on selecting the best brain recording sites and signal features corresponding to speech production. The purpose of this study was to detect speech activity automatically from electrocorticographic signals based on joint spatial-frequency clustering of the ECoG feature space. For this study, the ECoG signals were recorded while a subject performed two different syllable repetition tasks. We found that the optimal frequency resolution to detect speech activity from ECoG signals was 8 Hz, achieving 98.8% accuracy by employing support vector machines as a classifier. We also defined the cortical areas that held the most information about the discrimination of speech and nonspeech time intervals. Additionally, the results shed light on the distinct cortical areas associated with the two syllables repetition tasks and may contribute to the development of portable ECoG-based communication.
Vasileios G Kanas; Iosif Mporas; Heather L Benz; Kyriakos N Sgarbas; Anastasios Bezerianos; Nathan E Crone
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  61     ISSN:  1558-2531     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2014 Apr 
Date Detail:
Created Date:  2014-03-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1241-50     Citation Subset:  IM    
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