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Brain Comput Interfaces (Abingdon). 2015;2(4):161-173. Epub 2015 Aug 26.

Identifying the Attended Speaker Using Electrocorticographic (ECoG) Signals.

Author information

1
Ctr for Adapt Neurotech, Wadsworth Center, New York State Department of Health, Albany, NY; Dept of Neurology, Albany Medical College, Albany, NY; Donders Inst for Brain, Cognition and Behaviour, Radboud Univ Nijmegen, The Netherlands.
2
Ctr for Adapt Neurotech, Wadsworth Center, New York State Department of Health, Albany, NY; Dept of Neurology, Albany Medical College, Albany, NY.
3
Ctr for Adapt Neurotech, Wadsworth Center, New York State Department of Health, Albany, NY; J. Crayton Pruitt Family Dept of Biomed Eng, Univ of Florida, Gainesville, FL.
4
Ctr for Adapt Neurotech, Wadsworth Center, New York State Department of Health, Albany, NY; Dept of Biomed Sci, State Univ of New York at Albany, Albany, NY.
5
Dept of Neurology, Albany Medical College, Albany, NY.
6
Donders Inst for Brain, Cognition and Behaviour, Radboud Univ Nijmegen, The Netherlands.
7
Ctr for Adapt Neurotech, Wadsworth Center, New York State Department of Health, Albany, NY; Dept of Neurology, Albany Medical College, Albany, NY; Dept of Biomed Sci, State Univ of New York at Albany, Albany, NY.

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. Thus, they cannot use traditional assistive communication devices that depend on muscle control, or brain-computer interfaces (BCIs) that depend on the ability to control gaze. While auditory and tactile BCIs can provide communication to such individuals, their use typically entails an artificial mapping between the stimulus and the communication intent. This makes these BCIs difficult to learn and use. In this study, we investigated the use of selective auditory attention to natural speech as an avenue for BCI communication. In this approach, the user communicates by directing his/her attention to one of two simultaneously presented speakers. We used electrocorticographic (ECoG) signals in the gamma band (70-170 Hz) to infer the identity of attended speaker, thereby removing the need to learn such an artificial mapping. Our results from twelve human subjects show that a single cortical location over superior temporal gyrus or pre-motor cortex is typically sufficient to identify the attended speaker within 10 s and with 77% accuracy (50% accuracy due to chance). These results lay the groundwork for future studies that may determine the real-time performance of BCIs based on selective auditory attention to speech.

KEYWORDS:

Auditory Attention; Brain-Computer Interface (BCI); Cocktail Party; Electrocorticography (ECoG)

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