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Behtash Babadi - One of the best experts on this subject based on the ideXlab platform.
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real time tracking of Selective Auditory Attention from m eeg a bayesian filtering approach
Frontiers in Neuroscience, 2018Co-Authors: Sina Miran, Sahar Akram, Alireza Sheikhattar, Jonathan Z Simon, Tao Zhang, Behtash BabadiAbstract:Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the Attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the Attentional state. Our proposed framework consists of three main modules: 1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, 2) Extracting reliable markers of the Attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and 3) Devising a near real-time state-space estimator that translates the noisy and variable Attention markers to robust and statistically interpretable estimates of the Attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, l_1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurately as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.
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robust and real time decoding of Selective Auditory Attention from m eeg a state space modeling approach
Journal of the Acoustical Society of America, 2018Co-Authors: Sina Miran, Sahar Akram, Alireza Sheikhattar, Jonathan Z Simon, Tao Zhang, Behtash BabadiAbstract:Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the Attentional state of a listener in a competing-speaker environment from M/EEG recordings. Most existing approaches operate in an offline fashion and require the entire data duration and multiple trials to provide robust results. Therefore, they cannot be used in emerging applications such as smart hearing aids, where a single trial must be used in real-time to decode the Attentional state. In this work, we close this gap by integrating various techniques from state-space modeling paradigm such as adaptive filtering, sparse estimation, and Expectation-Maximization, and devise a framework for robust and real-time decoding of the Attentional state from M/EEG recordings. We validate the performance of this framework using ...
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real time tracking of Selective Auditory Attention from m eeg a bayesian filtering approach
bioRxiv, 2017Co-Authors: Sina Miran, Sahar Akram, Alireza Sheikhattar, Jonathan Z Simon, Tao Zhang, Behtash BabadiAbstract:Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the Attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). These procedures operate in an offline fashion, i.e., require the entire duration of the experiment and multiple trials to provide robust results. Therefore, they cannot be used in emerging applications such as smart hearing aid devices, where a single trial must be used in real-time to decode the Attentional state. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the Attentional state. Our proposed framework consists of three main modules: 1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, 2) Extracting reliable markers of the Attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and 3) Devising a near real-time state-space estimator that translates the noisy and variable Attention markers to robust and reliable estimates of the Attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, l1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurate as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.
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dynamic estimation of the Auditory temporal response function from meg in competing speaker environments
IEEE Transactions on Biomedical Engineering, 2017Co-Authors: Sahar Akram, Jonathan Z Simon, Behtash BabadiAbstract: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 $\ell _1$ -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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robust decoding of Selective Auditory Attention from meg in a competing speaker environment via state space modeling
NeuroImage, 2016Co-Authors: Sahar Akram, Jonathan Z Simon, Alessandro Presacco, Shihab A Shamma, Behtash BabadiAbstract: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.
Sahar Akram - One of the best experts on this subject based on the ideXlab platform.
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real time tracking of Selective Auditory Attention from m eeg a bayesian filtering approach
Frontiers in Neuroscience, 2018Co-Authors: Sina Miran, Sahar Akram, Alireza Sheikhattar, Jonathan Z Simon, Tao Zhang, Behtash BabadiAbstract:Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the Attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the Attentional state. Our proposed framework consists of three main modules: 1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, 2) Extracting reliable markers of the Attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and 3) Devising a near real-time state-space estimator that translates the noisy and variable Attention markers to robust and statistically interpretable estimates of the Attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, l_1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurately as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.
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robust and real time decoding of Selective Auditory Attention from m eeg a state space modeling approach
Journal of the Acoustical Society of America, 2018Co-Authors: Sina Miran, Sahar Akram, Alireza Sheikhattar, Jonathan Z Simon, Tao Zhang, Behtash BabadiAbstract:Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the Attentional state of a listener in a competing-speaker environment from M/EEG recordings. Most existing approaches operate in an offline fashion and require the entire data duration and multiple trials to provide robust results. Therefore, they cannot be used in emerging applications such as smart hearing aids, where a single trial must be used in real-time to decode the Attentional state. In this work, we close this gap by integrating various techniques from state-space modeling paradigm such as adaptive filtering, sparse estimation, and Expectation-Maximization, and devise a framework for robust and real-time decoding of the Attentional state from M/EEG recordings. We validate the performance of this framework using ...
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real time tracking of Selective Auditory Attention from m eeg a bayesian filtering approach
bioRxiv, 2017Co-Authors: Sina Miran, Sahar Akram, Alireza Sheikhattar, Jonathan Z Simon, Tao Zhang, Behtash BabadiAbstract:Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the Attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). These procedures operate in an offline fashion, i.e., require the entire duration of the experiment and multiple trials to provide robust results. Therefore, they cannot be used in emerging applications such as smart hearing aid devices, where a single trial must be used in real-time to decode the Attentional state. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the Attentional state. Our proposed framework consists of three main modules: 1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, 2) Extracting reliable markers of the Attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and 3) Devising a near real-time state-space estimator that translates the noisy and variable Attention markers to robust and reliable estimates of the Attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, l1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurate as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.
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dynamic estimation of the Auditory temporal response function from meg in competing speaker environments
IEEE Transactions on Biomedical Engineering, 2017Co-Authors: Sahar Akram, Jonathan Z Simon, Behtash BabadiAbstract: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 $\ell _1$ -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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robust decoding of Selective Auditory Attention from meg in a competing speaker environment via state space modeling
NeuroImage, 2016Co-Authors: Sahar Akram, Jonathan Z Simon, Alessandro Presacco, Shihab A Shamma, Behtash BabadiAbstract: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.
Helen J Neville - One of the best experts on this subject based on the ideXlab platform.
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differences in the neural mechanisms of Selective Attention in children from different socioeconomic backgrounds an event related brain potential study
Developmental Science, 2009Co-Authors: Courtney Stevens, Brittni Lauinger, Helen J NevilleAbstract:Previous research indicates that children from lower socioeconomic backgrounds show deficits in aspects of Attention, including a reduced ability to filter irrelevant information and to suppress prepotent responses. However, less is known about the neural mechanisms of group differences in Attention, which could reveal the stages of processing at which Attention deficits arise. The present study examined this question using an event-related brain potential (ERP) measure of Selective Auditory Attention. Thirty-two children aged from 3 to 8 years participated in the study. Children were cued to attend Selectively to one of two simultaneously presented narrative stories. The stories differed in location (left/right speaker), narration voice (male/female), and content. ERPs were recorded to linguistic and non-linguistic probe stimuli embedded in the attended and unattended stories. Children whose mothers had lower levels of educational attainment (no college experience) showed reduced effects of Selective Attention on neural processing relative to children whose mothers had higher levels of educational attainment (at least some college). These differences occurred by 100 milliseconds after probe onset. Furthermore, the differences were related specifically to a reduced ability to filter irrelevant information (i.e. to suppress the response to sounds in the unattended channel) among children whose mothers had lower levels of education. These data provide direct evidence for differences in the earliest stages of processing within neural systems mediating Selective Attention in children from different socioeconomic backgrounds. Results are discussed in the context of intervention programs aimed at improving Attention and self-regulation abilities in children at-risk for school failure.
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neurophysiological evidence for Selective Auditory Attention deficits in children with specific language impairment
Brain Research, 2006Co-Authors: Courtney Stevens, Lisa D Sanders, Helen J NevilleAbstract:Recent behavioral studies suggest that children with poor language abilities have difficulty with Attentional filtering, or noise exclusion. However, as behavioral performance represents the summed activity of multiple stages of processing, the temporal locus of the filtering deficit remains unclear. Here, we used an event-related potential (ERP) paradigm to compare the earliest mechanisms of Selective Auditory Attention in 12 children with specific language impairment (SLI) and 12 matched control children. Participants were cued to attend Selectively to one of two simultaneously presented narrative stories. The stories differed in location (left/right speaker), narration voice (male/female), and content. ERPs were recorded to linguistic and nonlinguistic probe stimuli embedded in the attended and unattended story. By 100 ms, typically developing children showed an amplification of the sensorineural response to attended as compared to unattended stimuli. In contrast, children with SLI showed no evidence of sensorineural modulation with Attention, despite behavioral performance indicating that they were performing the task as directed. These data are the first to show that SLI children have marked and specific deficits in the neural mechanisms of Attention and, further, localize the timing of the Attentional deficit to the earliest stages of sensory processing. Deficits in the effects of Selective Attention on early sensorineural processing may give rise to the diverse set of sensory and linguistic impairments in SLI children.
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An Event-related Potential Study of Selective Auditory Attention in Children and Adults
Journal of Cognitive Neuroscience, 2005Co-Authors: Donna Coch, Lisa D Sanders, Helen J NevilleAbstract:In a dichotic listening paradigm, event-related potentials (ERPs) were recorded to linguistic and nonlinguistic probe stimuli embedded in 2 different narrative contexts as they were either attended or unattended. In adults, the typical N1 Attention effect was observed for both types of probes: Probes superimposed on the attended narrative elicited an enhanced negativity compared to the same probes when unattended. Overall, this sustained Attention effect was greater over medial and left lateral sites, but was more posteriorly distributed and of longer duration for linguistic as compared to nonlinguistic probes. In contrast, in 6-to 8-year-old children the ERPs were morphologically dissimilar to those elicited in adults and children displayed a greater positivity to both types of probe stimuli when embedded in the attended as compared to the unattended narrative. Although both adults and children showed Attention effects beginning at about 100 msec, only adults displayed left-lateralized Attention effects and a distinct, posterior distribution for linguistic probes. These results suggest that the Attentional networks indexed by this task continue to develop beyond the age of 8 years.
Jonathan Z Simon - One of the best experts on this subject based on the ideXlab platform.
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real time tracking of Selective Auditory Attention from m eeg a bayesian filtering approach
Frontiers in Neuroscience, 2018Co-Authors: Sina Miran, Sahar Akram, Alireza Sheikhattar, Jonathan Z Simon, Tao Zhang, Behtash BabadiAbstract:Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the Attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the Attentional state. Our proposed framework consists of three main modules: 1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, 2) Extracting reliable markers of the Attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and 3) Devising a near real-time state-space estimator that translates the noisy and variable Attention markers to robust and statistically interpretable estimates of the Attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, l_1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurately as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.
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robust and real time decoding of Selective Auditory Attention from m eeg a state space modeling approach
Journal of the Acoustical Society of America, 2018Co-Authors: Sina Miran, Sahar Akram, Alireza Sheikhattar, Jonathan Z Simon, Tao Zhang, Behtash BabadiAbstract:Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the Attentional state of a listener in a competing-speaker environment from M/EEG recordings. Most existing approaches operate in an offline fashion and require the entire data duration and multiple trials to provide robust results. Therefore, they cannot be used in emerging applications such as smart hearing aids, where a single trial must be used in real-time to decode the Attentional state. In this work, we close this gap by integrating various techniques from state-space modeling paradigm such as adaptive filtering, sparse estimation, and Expectation-Maximization, and devise a framework for robust and real-time decoding of the Attentional state from M/EEG recordings. We validate the performance of this framework using ...
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real time tracking of Selective Auditory Attention from m eeg a bayesian filtering approach
bioRxiv, 2017Co-Authors: Sina Miran, Sahar Akram, Alireza Sheikhattar, Jonathan Z Simon, Tao Zhang, Behtash BabadiAbstract:Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the Attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). These procedures operate in an offline fashion, i.e., require the entire duration of the experiment and multiple trials to provide robust results. Therefore, they cannot be used in emerging applications such as smart hearing aid devices, where a single trial must be used in real-time to decode the Attentional state. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the Attentional state. Our proposed framework consists of three main modules: 1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, 2) Extracting reliable markers of the Attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and 3) Devising a near real-time state-space estimator that translates the noisy and variable Attention markers to robust and reliable estimates of the Attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, l1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurate as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.
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dynamic estimation of the Auditory temporal response function from meg in competing speaker environments
IEEE Transactions on Biomedical Engineering, 2017Co-Authors: Sahar Akram, Jonathan Z Simon, Behtash BabadiAbstract: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 $\ell _1$ -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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robust decoding of Selective Auditory Attention from meg in a competing speaker environment via state space modeling
NeuroImage, 2016Co-Authors: Sahar Akram, Jonathan Z Simon, Alessandro Presacco, Shihab A Shamma, Behtash BabadiAbstract: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.
Diana I Lurie - One of the best experts on this subject based on the ideXlab platform.
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chronic low level lead exposure affects the monoaminergic system in the mouse superior olivary complex
The Journal of Comparative Neurology, 2009Co-Authors: Tyler Fortune, Diana I LurieAbstract:Low-level lead (Pb) exposure is associated with behavioral and cognitive dysfunction, but it is not clear how Pb produces these behavioral changes. Pb has been shown to alter Auditory temporal processing in both humans and animals. Auditory temporal processing occurs in the superior olivary complex (SOC) in the brainstem, where it is an important component in sound detection in noisy environments and in Selective Auditory Attention. The SOC receives a serotonergic innervation from the dorsal raphe, and serotonin has been implicated in Auditory temporal processing within the brainstem and inferior colliculus. Because Pb exposure modulates Auditory temporal processing, the serotonergic system is a potential target for Pb. The current study was undertaken to determine whether developmental Pb exposure preferentially changes the serotonergic system within the SOC. Pb-treated mice were exposed to no Pb, very low Pb (0.01 mM), or low Pb (0.1 mM) throughout gestation and through 21 days postnatally. Brainstem sections from control and Pb-exposed mice were immunostained for the vesicular monoamine transporter 2 (VMAT2), serotonin (5-HT), and dopamine-beta-hydroxylase (DbetaH; a marker for norepinephrine) in order to elucidate the effect of Pb on monoaminergic input into the SOC. Sections were also immunolabeled with antibodies to vesicular glutamate transporter 1 (VGLUT1), vesicular gamma-aminobutyric acid (GABA) transporter (VGAT), and vesicular acetylcholine transporter (VAChT) to determine whether Pb exposure alters the glutaminergic, GABAergic, or cholinergic systems. Pb exposure caused a significant decrease in VMAT2, 5-HT, and DbetaH expression, whereas VGLUT1, VGAT, and VAChT showed no change. These results provide evidence that Pb exposure during development alters normal monoaminergic expression in the Auditory brainstem.