Publications

EEG recordings are typically long and contain large amounts of data, making manual cleaning a time-consuming and error-prone task. Automated preprocessing pipelines can facilitate the efficient and objective extraction of artifacts, enabling standardized and reproducible analyses. However, automated preprocessing pipelines typically remove data considered artifacts and return a subset of irreversibly transformed signals. This approach obfuscates preprocessing decisions and often makes it impossible to recover the original data or modify the preprocessing steps. Further, it complicates collaboration among research teams working on a common dataset, as different analyses may require specific preprocessing steps. Given the large amount of resources devoted to collecting EEG, tools that can efficiently and transparently preprocess data are greatly needed. PyLossless addresses this need by creating a non-destructive, automated preprocessing pipeline that maintains the continuous EEG structure. It offers a user-friendly API, is well documented, tested through continuous integration, easily deployable, and integrates with the popular MNE-Python environment. The pipeline also provides a browser-based quality control review (QCR) dashboard that allows researchers to visualize and edit automated artifact flags for sensors, time periods, and independent components. The end product of PyLossless is a lossless annotated data state that can be shared and used with analysis-specific artifact rejection policies, allowing for an optimal balance between flexibility and standardization.

Misophonia is an emerging condition in which everyday sounds, such as chewing, sniffing, or tapping, evoke disproportionately intense emotional and physiological responses. Despite growing recognition of its clinical significance, progress in understanding misophonia has been hindered by the limited availability of standardized and ecologically valid stimulus sets. Here, we present a large, open-access archive of over 1,400 five-second audiovisual clips spanning 12 empirically informed categories of misophonic triggers. This resource includes a diverse array of real-world triggers and extends beyond commonly studied orofacial movement-related sounds, while its audiovisual format enables systematic investigation of how visual context shapes responses to misophonic sounds. The archive lowers the barrier for laboratories to study misophonia, promotes reproducibility across sites, and may support applications ranging from crowdsourced assessments of population-level sensitivities to machine learning approaches for automated trigger detection. By providing a large and diverse audiovisual misophonia stimulus repository, this resource is designed to accelerate mechanistic, clinical, and translational research on misophonia and related sensory-emotional phenomena.

The electroencephalogram (EEG) provides a direct measure of brain electrical activity but is typically contaminated by artifacts, most notably those arising from eye movements. Such artifacts are often removed using blind source separation techniques such as independent component analysis (ICA). However, it remains challenging to determine whether subtracting eye-movement-related components identified by techniques like ICA inadvertently removes neurogenic activity. Addressing this issue is critical to prevent bias in EEG analyses. To this end, we developed complementary deep learning and biophysical modeling approaches for the deconfounding of eye-movement artifacts, leveraging eye-tracking (ET) information. Using a multimodal, open-access dataset, we trained a deep learning model within-recording to predict the component of the EEG explainable from ET. In parallel, we employed a realistic head model to simulate artifacts generated by eye movements. Using this framework, we can distinguish neural and nonneural correlates of eye movements from neural activity not associated with eye movements. Further, this ET-informed framework enabled us to evaluate the sensitivity and specificity of techniques used to isolate and remove eye-movement artifacts. These advances provide a solid framework for future work aiming to further disentangle neural components using tailored experimental paradigms, for example, by dissociating eye movements and visual feedback through virtual reality.