Robust decoding of selective auditory attention from MEG in a competing-speaker environment via state-space modeling

The underlying mechanism of how the human brain solves the cocktail party problem is largely unknown. Recent neuroimaging studies, however, suggest salient temporal correlations between the auditory neural response and the attended auditory object. Using magnetoencephalography (MEG) recordings of the neural responses of human subjects, we propose a decoding approach for tracking the attentional state while subjects are selectively listening to one of the two speech streams embedded in a competing-speaker environment. We develop a biophysically-inspired state-space model to account for the modulation of the neural response with respect to the attentional state of the listener. The constructed decoder is based on a maximum a posteriori (MAP) estimate of the state parameters via the Expectation Maximization (EM) algorithm. Using only the envelope of the two speech streams as covariates, the proposed decoder enables us to track the attentional state of the listener with a temporal resolut...

• Type: Journal
• Year: 2016
• Where: NEUROIMAGE
• Authors: Sahar Akram, Alessandro Presacco, Jonathan Z. Simon, Shihab A. Shamma, Behtash Babadi
• Keywords: NEUROIMAGE 2016