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Cereb Cortex. 2015 Jul;25(7):1697-706. doi: 10.1093/cercor/bht355. Epub 2014 Jan 15.

Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG.

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School of Engineering, Trinity Centre for Bioengineering and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
School of Engineering, Trinity Centre for Bioengineering and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland Department of Psychology, Centre for Neuroscience in Education, University of Cambridge, Cambridge, UK.
Department of Neurological Surgery Department of Physiology, UCSF Center for Integrative Neuroscience, University of California, San Francisco, CA 94143, USA.
The Center for Computational Neuroscience and Neural Technology, Boston University, Boston, MA 02215, USA.
The Sheryl and Daniel R. Tishman Cognitive Neurophysiology Laboratory, Children's Evaluation and Rehabilitation Center, Departments of Pediatrics and Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Microsoft Research, Mountain View, CA 94043, USA.
Institute for Systems Research, University of Maryland, College Park, MD 20742, USA.


How humans solve the cocktail party problem remains unknown. However, progress has been made recently thanks to the realization that cortical activity tracks the amplitude envelope of speech. This has led to the development of regression methods for studying the neurophysiology of continuous speech. One such method, known as stimulus-reconstruction, has been successfully utilized with cortical surface recordings and magnetoencephalography (MEG). However, the former is invasive and gives a relatively restricted view of processing along the auditory hierarchy, whereas the latter is expensive and rare. Thus it would be extremely useful for research in many populations if stimulus-reconstruction was effective using electroencephalography (EEG), a widely available and inexpensive technology. Here we show that single-trial (≈60 s) unaveraged EEG data can be decoded to determine attentional selection in a naturalistic multispeaker environment. Furthermore, we show a significant correlation between our EEG-based measure of attention and performance on a high-level attention task. In addition, by attempting to decode attention at individual latencies, we identify neural processing at ∼200 ms as being critical for solving the cocktail party problem. These findings open up new avenues for studying the ongoing dynamics of cognition using EEG and for developing effective and natural brain-computer interfaces.


BCI; EEG; attention; cocktail party; speech; stimulus-reconstruction

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