TY - JOUR T1 - Identifying the Attended Speaker Using Electrocorticographic (ECoG) Signals. JF - Journal of Neural Engineering Y1 - 2015 A1 - Dijkstra, K. A1 - Peter Brunner A1 - Gunduz, Aysegul A1 - Coon, W.G. A1 - A L Ritaccio A1 - Farquhar, Jason A1 - Gerwin Schalk KW - auditory attention KW - Brain-computer interface (BCI) KW - Cocktail Party KW - electrocorticography (ECoG) AB - 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. UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4776341/ ER - TY - CHAP T1 - Towards an Auditory Attention BCI T2 - Brain-Computer Interface Research: A State-of-the-Art Summary Y1 - 2015 A1 - Peter Brunner A1 - Dijkstra, K. A1 - Coon, W.G. A1 - Mellinger, Jürgen A1 - A L Ritaccio A1 - Gerwin Schalk AB - 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 that 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. JF - Brain-Computer Interface Research: A State-of-the-Art Summary PB - Springer International Publishing CY - New York City, NY SN - 978-3-319-25188-2 UR - http://link.springer.com/chapter/10.1007%2F978-3-319-25190-5_4 ER -