Multiway Canonical Correlation Analysis of Brain Signals

Cheveigné, Alain de; Liberto, Giovanni M. Di; Arzounian, Dorothée; Wong, DD; Hjortkjær, Jens; S, Fuglsang; Lc, Parra

doi:10.1101/344960

Cited by 6 publications

(4 citation statements)

References 69 publications

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“…Richer representations have been explored, such as auditory filterbank (Biesmans et al, 2017), higher-order linguistic structure (Di Liberto et al, 2015), onsets (Oganian and Chang, 2019), or voice pitch (Forte et al, 2017; Teoh et al, 2019), etc., but they remain to be developed further and integrated. Multi-set CCA (MCCA), which allows merging EEG across subjects, may ease development of such stimulus representations (de Cheveigné et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

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Auditory Stimulus-response Modeling with a Match-Mismatch Task

Cheveigné

Slaney

Fuglsang

et al. 2020

Preprint

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The relation between a continuous ongoing stimulus and the brain response that it evokes can be characterized by a stimulus-response model fit to the data. This systems-identification approach offers insight into perceptual processes within the brain, and it is also of potential practical use for devices such as Brain Computer Interfaces (BCI). The quality of the model can be quantified by measuring the fit with a regression problem, or by applying it to a classification task and measuring its performance. Here we focus on a {\em match-mismatch}\ task that entails deciding whether a segment of brain signal matches, via a model, the auditory stimulus that evoked it. The match-mismatch task can be used to compare performance of different stimulus-response models. We show that performance in a match-mismatch task and metrics summarizing regression accuracies can provide complementary insights in the relation between stimulus and response. Importantly, the match-mismatch task provides information about discriminatory power, making it directly applicable to BCI applications. Evaluation is performed on a freely available database, and code is available for scripts and functions to allow scrutiny of our results and facilitate comparative evaluation of future developments.

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Section: Discussionmentioning

confidence: 99%

“…have been explored but remain to be developed further and integrated. Multi-set CCA (MCCA), which allows merging EEG across subjects, may ease development of such stimulus representations (de Cheveigné et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Auditory Stimulus-response Modeling with a Match-Mismatch Task

Cheveigné

Slaney

Fuglsang

et al. 2020

Preprint

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“…An independent component analysis (68) with 30 principal components, implemented in Fieldtrip, was performed to remove EOG and electrocardiography (ECG) artifacts. Finally, we concatenated all trials into one matrix to derive the most common component of the EEG signals reflecting neural responses to musical pieces, using MCCA (69).…”

Section: Data Preprocessingmentioning

confidence: 99%

Eyes robustly blink to musical beats like tapping

Wu,

Teng,

2024

Preprint

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Auditory-motor synchronization with musical rhythm may extend beyond overt body movements like fingers, hands, or feet. In four experiments with 123 young non-musicians, we demonstrated that spontaneous eye blinks can robustly track musical beats. Participants listened to Bach chorales with regular beat rates while their eye movements were monitored. The blinking rate consistently aligned with the beat rate across various tempi and independent of melodic cues. Blinking showed selective tuning to different beat phases, with inhibition immediately before beat onset and increased blinking afterward. EEG recordings revealed a dynamic correspondence between blink and neural beat tracking. Mechanistically, this synchronization reflects dynamic auditory attention and temporal modulation of visual sampling. Our findings introduce ‘eye tapping,’ where eye blinks track musical beats, expanding the behavioral repertoire of auditory-motor synchronization during music listening. This highlights the intricate relationship between music perception and peripheral oculomotor activity, enriching our understanding of cross-modal active sensing.

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“…With highly correlated predictors, it can sometimes be relevant to transform the predictors using data dimensionality reduction techniques [60,61,62,63,64,65] and then fit model using the transformed data. This can potentially reduce computational burden without compromising predictive accuracy.…”

Section: Encoding-and Decoding Models With Correlated Predictorsmentioning

confidence: 99%

Exploring an EM-algorithm for banded regression in computational neuroscience

Fuglsang,

Madsen,

Puonti

et al. 2023

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Regression is a principal tool for relating brain responses to stimuli or tasks in computational neuroscience. This often involves fitting linear models with predictors that can be divided into groups, such as distinct stimulus feature subsets in encoding models or features of different neural response channels in decoding models. When fitting such models, it can be relevant to impose differential shrinkage of the different groups of regression weights. Here, we explore a framework that allow for straightforward definition and estimation of such models. We present an expectation-maximization algorithm for tuning hyperparameters that control shrinkage of groups of weights. We highlight properties, limitations, and potential use-cases of the model using simulated data. Next, we explore the model in the context of a BOLD fMRI encoding analysis and an EEG decoding analysis. Finally, we discuss cases where the model can be useful and scenarios where regularization procedures complicate model interpretation.

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Multiway Canonical Correlation Analysis of Brain Signals

Cited by 6 publications

References 69 publications

Auditory Stimulus-response Modeling with a Match-Mismatch Task

Auditory Stimulus-response Modeling with a Match-Mismatch Task

Eyes robustly blink to musical beats like tapping

Exploring an EM-algorithm for banded regression in computational neuroscience

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