کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6024409 | 1188658 | 2016 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Robust decoding of selective auditory attention from MEG in a competing-speaker environment via state-space modeling
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 124, Part A, 1 January 2016, Pages 906-917
Journal: NeuroImage - Volume 124, Part A, 1 January 2016, Pages 906-917
نویسندگان
Sahar Akram, Alessandro Presacco, Jonathan Z. Simon, Shihab A. Shamma, Behtash Babadi,