Semiparametric Latent ANOVA Model for Event-Related Potentials

Abstract

Event-related potentials (ERPs) extracted from electroencephalography (EEG) data in response to stimuli are widely used in psychological and neuroscience experiments. A major goal is to link ERP characteristic components to subject-level covariates. Existing methods typically follow two-step approaches, first identifying ERP components using peak detection methods and then relating them to the covariates. This approach, however, can lead to loss of efficiency due to inaccurate estimates in the initial step, especially considering the low signal-to-noise ratio of EEG data. To address this challenge, we propose a semiparametric latent ANOVA model (SLAM) that unifies inference on ERP components and their association with covariates. SLAM models ERP waveforms via a structured Gaussian process (GPs) prior that encode ERP latency in its derivative and links the subject-level latencies to covariates using a latent ANOVA. This unified Bayesian framework provides estimation at both population- and subject-levels, improving the efficiency of the inference by leveraging information across subjects. We automate posterior inference and hyperparameter tuning using a Monte Carlo expectation–maximization (MCEM) algorithm. We demonstrate the advantages of SLAM over competing methods via simulations. Our method allows us to examine how factors or covariates affect the magnitude and/or latency of ERP components, which in turn reflect cognitive, psychological, or neural processes. We exemplify this via an application to data from an ERP experiment on speech recognition, where we assess the effect of age on two components of interest. Our results verify the scientific findings that older people take a longer reaction time to respond to external stimuli because of the delay in perception and brain processes.

Keywords:

• Event-related potentials
• latent ANOVA
• Gaussian processes
• amplitude
• latency
• peak detection

Disclosure Statement

The authors have no relevant financial or non-financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

Data Availability Statement

Behavioral and Event-Related Potentials data that support the findings in this paper are available on PsyArxiv Noe and Fischer-Baum (2020) at https://osf.io/c7k4s/ (DOI 10.17605/OSF.IO/C7K4S).