Linear Modeling of Neurophysiological Responses to Naturalistic Stimuli: Methodological Considerations for Applied Research

Crosse, Michael J.; Zuk, Nathaniel J.; Liberto, Giovanni M. Di; Nidiffer, Aaron; Molholm, Sophie; Lalor, Edmund C.

doi:10.31234/osf.io/jbz2w

Cited by 21 publications

(38 citation statements)

References 58 publications

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“…Permutation testing must be performed by computing a null distribution that controls for these covariates. Random permutations of EEG-or envelope-shaped noise (shaped with the same power spectrum as the original signal) are both valid choices (Crosse et al, 2021;Nichols and Holmes, 2002). The 95th percentile of permutations can be taken as the alpha = 0.05 significance level.…”

Section: Methodological Considerations For Mutual Information Applica...mentioning

confidence: 99%

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Beyond Linear Neural Envelope Tracking: A Mutual Information Approach

Clercq

Vanthornhout

Vandermosten

et al. 2022

Preprint

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The human brain tracks the temporal envelope of speech, which contains essential cues for speech understanding. Linear models are the most common tool to study neural envelope tracking. However, information on how speech is processed can be lost since nonlinear relations are precluded. As an alternative, mutual information (MI) analysis can detect both linear and nonlinear relations. Yet, several different approaches to calculating MI are applied without consensus on which approach to use. Furthermore, the added value of nonlinear techniques remains a subject of debate in the field. To resolve this, we applied linear and MI analyses to electroencephalography (EEG) data of participants listening to continuous speech. Comparing the different MI approaches, we conclude that results are most reliable and robust using the Gaussian copula approach, which first transforms the data to standard Gaussians. With this approach, the MI analysis is a valid technique for studying neural envelope tracking. Like linear models, it allows spatial and temporal interpretations of speech processing, peak latency analyses, and applications to multiple EEG channels combined. Finally, we demonstrate that the MI analysis can detect nonlinear components on the single-subject level, beyond the limits of linear models. We conclude that the MI analysis is a more informative tool for studying neural envelope tracking.

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Section: Methodological Considerations For Mutual Information Applica...mentioning

confidence: 99%

“…For a more in-depth explanation of linear models and ridge regression, we refer to Crosse et al (2021).…”

Section: Linear Backward Modelmentioning

confidence: 99%

Beyond Linear Neural Envelope Tracking: A Mutual Information Approach

Clercq

Vanthornhout

Vandermosten

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…EEGs were re-referenced to average mastoids for further analysis. Data from 0-1000 ms after the onset of each 2-minute epoch were discarded to avoid transient brain responses in the subsequent analysis (Crosse et al, 2021). Epochs were then concatenated for each of the five conditions, resulting in 8 min of continuous data per condition.…”

Section: Methodsmentioning

confidence: 99%

“…EEG and stimulus signals were both z-score normalized. As with conventional event-related potentials (ERPs), TRFs were computed for each participant to account for inherent inter-subject variability in neural response tracking (Crosse et al, 2021). We used 6-fold cross-validation to derive TRFs per condition.…”

Section: Electrophysiological Data Analysis: Temporal Response Functi...mentioning

confidence: 99%

“…We then used each participant's optimal λ parameter to derive TRFs for all other conditions. This approach preserves response consistency within subjects while avoiding model overfitting (Crosse et al, 2021). The resulting TRF waveforms represents the time-varying weights how the EEG signal at each electrode changes in response to a unit change in the speech stimulus envelope.…”

Section: Electrophysiological Data Analysis: Temporal Response Functi...mentioning

confidence: 99%

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Familiarity of Background Music Modulates the Cortical Tracking of Target Speech at the Cocktail Party

Brown

Bidelman

2022

Preprint

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The “cocktail party” problem – how a listener perceives speech in noisy environments – is typically studied using speech (multi-talker babble) or noise maskers. However, realistic cocktail party scenarios often include background music (e.g., coffee shops, concerts). Studies investigating music’s effects on concurrent speech perception have predominantly used highly controlled synthetic music or shaped noise which do not reflect naturalistic listening environments. Behaviorally, familiar background music and songs with vocals/lyrics inhibit concurrent speech recognition. Here, we investigated the neural bases of these effects. While recording multichannel EEG, participants listened to an audiobook while popular songs (or silence) played in the background at 0 dB signal-to-noise ratio. Songs were either familiar or unfamiliar to listeners and featured either vocals or isolated instrumentals from the original audio recordings. Comprehension questions probed task engagement. We used temporal response functions (TRFs) to isolate cortical tracking to the target speech envelope and analyzed neural responses around 100 ms (i.e., auditory N1 wave). We found that speech comprehension was, expectedly, impaired during background music(s) compared to silence. Target speech tracking was further hindered by the presence of vocals. When masked by familiar music, response latencies to speech were less susceptible to informational masking, suggesting concurrent neural tracking of speech was easier during music known to the listener. These differential effects of music familiarity were further exacerbated in listeners with less musical ability. Our neuroimaging results and their dependence on listening skills are consistent with early attentional gain mechanisms where familiar music is easier to tune out (listeners already know the song’s expectancies) and thus can allocate fewer attentional resources to the background music to better monitor concurrent speech material.

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