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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.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Akram S,Presacco A,Simon JZ,Shamma SA,Babadi B

doi

10.1016/j.neuroimage.2015.09.048

subject

Has Abstract

pub_date

2016-01-01 00:00:00

pages

906-917

issue

Pt A

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(15)00870-8

journal_volume

124

pub_type

杂志文章

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