title: Robust decoding of selective auditory attention from MEG in a competing-speaker environment via state-space modeling.
abstract: 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 resolution of the order of seconds, together with statistical confidence intervals. We evaluate the performance of the proposed model using numerical simulations and experimentally measured evoked MEG responses from the human brain. Our analysis reveals considerable performance gains provided by the state-space model in terms of temporal resolution, computational complexity and decoding accuracy.
authors.affiliation: Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA; Institute for Systems Research, University of Maryland, College Park, MD 20742, USA. Electronic address: sakram@umd.edu.
authors.forename: Sahar
authors.initials: S
authors.lastname: Akram
authors.affiliation: Department of Hearing and Speech Science, University of Maryland, College Park, MD 20742, USA.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Presacco
authors.affiliation: Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA; Institute for Systems Research, University of Maryland, College Park, MD 20742, USA; Department of Biology, University of Maryland, College Park, MD 20742, USA.
authors.forename: Jonathan Z
authors.initials: JZ
authors.lastname: Simon
authors.affiliation: Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA; Institute for Systems Research, University of Maryland, College Park, MD 20742, USA.
authors.forename: Shihab A
authors.initials: SA
authors.lastname: Shamma
authors.affiliation: Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA; Institute for Systems Research, University of Maryland, College Park, MD 20742, USA. Electronic address: behtash@umd.edu.
authors.forename: Behtash
authors.initials: B
authors.lastname: Babadi
mesh_terms: D000161:Acoustic Stimulation; D000328:Adult; D000465:Algorithms; D001288:Attention / Q000502:physiology*; D001307:Auditory Perception / Q000502:physiology; D004777:Environment; D005260:Female; D006801:Humans; D008144:Loudness Perception / Q000502:physiology*; D015225:Magnetoencephalography; D008297:Male; D008959:Models, Neurological; D013067:Speech Perception / Q000502:physiology*; D055815:Young Adult
keywords: Attention; MEG; Nonlinear filtering; Speech segregation; State-space models