Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task

Presacco, Alessandro; Miran, Sina; Babadi, Behtash; Simon, Jonathan Z.

doi:10.1109/embc.2019.8857953

Cited by 6 publications

(7 citation statements)

References 14 publications

(23 reference statements)

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“…Thus, moment-to-moment fluctuations in attention might be associated with corresponding changes in the TRFs. While modeling this is a highly complex problem, with an even larger number of degrees of freedom, some initial progress has been made towards estimating deconvolution models with dynamic TRFs that can change over time (Babadi et al, 2010;Miran et al, 2018;Presacco et al, 2019).…”

Section: Further Applicationsmentioning

confidence: 99%

Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions

Brodbeck¹,

Das

Kulasingham

et al. 2021

Preprint

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Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have different, interdependent temporal structures. Deconvolution analysis has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of the time dimension. The deconvolution analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate temporal response function (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: 1) Is there a significant neural representation corresponding to this predictor variable? And if so, 2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through appropriate linking models.

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Section: Further Applicationsmentioning

confidence: 99%

Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions

Brodbeck¹,

Das

Kulasingham

et al. 2021

Preprint

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“…The at-will attention switching dataset is a subset of recordings in [51], where the participants included five younger-adult (22-33 years old) native English speakers with normal hearing recruited from the University of Maryland. Only one of the subjects exhibited a meaningful auditory neural response (i.e., auditory DSS rotation matrix; see MEG Data Preprocessing subsection for details) with a reliable behavioral report.…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…Prior to the experiment, a single-speaker pilot study was performed where subjects listened to three 60 s trials with similar stimuli. Further experimental details can be found in [51].…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…All MEG channels and speech envelopes were band-pass filtered between 2 Hz and 8 Hz (delta and theta bands), corresponding to the slow temporal modulations in speech [46,48], and downsampled to F s = 100 Hz. Similar to [47,50,51], we used the DSS algorithm [22] on pilot trials to decompose the MEG data into temporally uncorrelated components. By using an averaging bias filter for promoting consistency across trials, we ordered the DSS components according to their trial-to-trial phase-locking reliability and chose the first component as the auditory neural response.…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…The TRF framework has resulted in new insights into the mechanisms of speech processing in the brain in the cocktail party scenario [45,[48][49][50]. For instance, TRF components at specific lags may exhibit peaks which arise, persist, and disappear over time according to the attentional state of the listener [51]. The different local dynamics of TRF components under each of these conditions motivates a GM density to capture such evolution patterns.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise

et al. 2020

Self Cite

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Estimating the latent dynamics underlying biological processes is a central problem in computational biology. State-space models with Gaussian statistics are widely used for estimation of such latent dynamics and have been successfully utilized in the analysis of biological data. Gaussian statistics, however, fail to capture several key features of the dynamics of biological processes (e.g., brain dynamics) such as abrupt state changes and exogenous processes that affect the states in a structured fashion. Although Gaussian mixture process noise models have been considered as an alternative to capture such effects, data-driven inference of their parameters is not well-established in the literature. The objective of this paper is to develop efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and to utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting. We develop an algorithm based on Expectation-Maximization to estimate the process noise parameters from state-space observations. We apply our algorithm to simulated and experimentally-recorded MEG data from auditory experiments in the cocktail party paradigm to estimate the underlying dynamic Temporal Response Functions (TRFs). Our simulation results show that the richer representation of the process noise as a Gaussian mixture significantly improves state estimation and capturing the heterogeneity of the TRF dynamics. Application to MEG data reveals improvements over existing TRF estimation techniques, and provides a reliable alternative to current approaches for probing neural dynamics in a cocktail party scenario, as well as attention decoding in emerging applications such as smart hearing aids. Our proposed methodology provides a framework for efficient inference of Gaussian mixture process noise models, with application to a wide range of biological data with underlying heterogeneous and latent dynamics.

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Inference of the Selective Auditory Attention Using Sequential LMMSE Estimation

Kuruvila

Demir

Fischer³

et al. 2021

IEEE Trans. Biomed. Eng.

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Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task

Cited by 6 publications

References 14 publications

Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions

Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions

Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise

Inference of the Selective Auditory Attention Using Sequential LMMSE Estimation

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