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Brain-Computer Interface ResearchAn ECoG-Based BCI Based on Auditory Attention to Natural Speech

Brain-Computer Interface Research: An ECoG-Based BCI Based on Auditory Attention to Natural Speech [People affected by severe neuro-degenerative diseases (e.g., late-stage amyotrophic lateral sclerosis (ALS) or locked-in syndrome) eventually lose all muscular control and are no longer able to gesture or speak. For this population, an auditory BCI is one of only a few remaining means of communication. All currently used auditory BCIs require a relatively artificial mapping between a stimulus and a communication output. This mapping is cumbersome to learn and use. Recent studies suggest electrocorticographic (ECoG) signals in the gamma band (i.e., 70–170 Hz) can be used to infer the identity of auditory speech stimuli, effectively removing the need to learn such an artificial mapping. However, BCI systems that use this physiological mechanism for communication purposes have not yet been described. In this study, we explore this possibility by implementing a BCI2000-based real-time system that uses ECoG signals to identify the attended speaker.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Brain-Computer Interface ResearchAn ECoG-Based BCI Based on Auditory Attention to Natural Speech

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/lp/springer-journals/brain-computer-interface-research-an-ecog-based-bci-based-on-auditory-GBbv948LPz
Publisher
Springer International Publishing
Copyright
© The Author(s) 2017
ISBN
978-3-319-57131-7
Pages
7 –19
DOI
10.1007/978-3-319-57132-4_2
Publisher site
See Chapter on Publisher Site

Abstract

[People affected by severe neuro-degenerative diseases (e.g., late-stage amyotrophic lateral sclerosis (ALS) or locked-in syndrome) eventually lose all muscular control and are no longer able to gesture or speak. For this population, an auditory BCI is one of only a few remaining means of communication. All currently used auditory BCIs require a relatively artificial mapping between a stimulus and a communication output. This mapping is cumbersome to learn and use. Recent studies suggest electrocorticographic (ECoG) signals in the gamma band (i.e., 70–170 Hz) can be used to infer the identity of auditory speech stimuli, effectively removing the need to learn such an artificial mapping. However, BCI systems that use this physiological mechanism for communication purposes have not yet been described. In this study, we explore this possibility by implementing a BCI2000-based real-time system that uses ECoG signals to identify the attended speaker.]

Published: Apr 30, 2017

Keywords: Amyotrophic Lateral Sclerosis; Superior Temporal Gyrus; Middle Temporal Gyrus; Speech Stimulus; Audio Stream

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