OBJECTIVE: A central problem in computational neuroscience is to characterize brain function using neural activity recorded from the brain in response to sensory inputs with statistical confidence. Most of existing estimation techniques, such as those based on reverse correlation, exhibit two main limitations: first, they are unable to produce dynamic estimates of the neural activity at a resolution comparable with that of the recorded data, and second, they often require heavy averaging across time as well as multiple trials in order to construct statistical confidence intervals for a precise interpretation of data. In this paper, we address the above-mentioned issues for estimating auditory temporal response function (TRF) as a parametric computational model for selective auditory attention in competing-speaker environments. METHODS: The TRF is a sparse kernel which regresses auditory MEG data with respect to the envelopes of the speech streams. We develop an efficient estimation technique by exploiting the sparsity of the TRF and adopting an ℓ1-regularized least squares estimator which is capable of producing dynamic TRF estimates as well as confidence intervals at sampling resolution from single-trial MEG data. RESULTS: We evaluate the performance of our proposed estimator using evoked MEG responses from the human brain in an auditory attention experiment with two competing speakers. The TRFs are estimated dynamically over time using the proposed technique with multisecond resolution, which is a significant improvement over previous results with a temporal resolution of the order of a minute. CONCLUSION: Application of our method to MEG data reveals a precise characterization of the modulation of M50 and M100 evoked responses with respect to the attentional state of the subject at multisecond resolution. SIGNIFICANCE: Our proposed estimation technique provides a high resolution real-time attention decoding framework in multispeaker environments with potential application in smart hearing aid technology.
OBJECTIVE: A central problem in computational neuroscience is to characterize brain function using neural activity recorded from the brain in response to sensory inputs with statistical confidence. Most of existing estimation techniques, such as those based on reverse correlation, exhibit two main limitations: first, they are unable to produce dynamic estimates of the neural activity at a resolution comparable with that of the recorded data, and second, they often require heavy averaging across time as well as multiple trials in order to construct statistical confidence intervals for a precise interpretation of data. In this paper, we address the above-mentioned issues for estimating auditory temporal response function (TRF) as a parametric computational model for selective auditory attention in competing-speaker environments. METHODS: The TRF is a sparse kernel which regresses auditory MEG data with respect to the envelopes of the speech streams. We develop an efficient estimation technique by exploiting the sparsity of the TRF and adopting an ℓ1-regularized least squares estimator which is capable of producing dynamic TRF estimates as well as confidence intervals at sampling resolution from single-trial MEG data. RESULTS: We evaluate the performance of our proposed estimator using evoked MEG responses from the human brain in an auditory attention experiment with two competing speakers. The TRFs are estimated dynamically over time using the proposed technique with multisecond resolution, which is a significant improvement over previous results with a temporal resolution of the order of a minute. CONCLUSION: Application of our method to MEG data reveals a precise characterization of the modulation of M50 and M100 evoked responses with respect to the attentional state of the subject at multisecond resolution. SIGNIFICANCE: Our proposed estimation technique provides a high resolution real-time attention decoding framework in multispeaker environments with potential application in smart hearing aid technology.
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