Research

Research projects and scientific interests.

Research Overview

My research centers on Fast Periodic Visual Stimulation (FPVS), an powerful EEG method for measuring rapid, frequency-tagged neural responses to visual stimuli in just a few minutes of recording time. FPVS seems to be still in its infancy, and the scientific community is still attempting to understand the best use cases for FPVS and how it can one day be the bridge between EEG and a clinical setting. The main goal of my work is to help standardize FPVS methods and make them easier to use across cognitive neuroscience domains, and ultimately into clinical practice.

Suggested Reading

If you are unfamiliar with FPVS and would like to learn more, I suggest starting with work by Bruno Rossion and George Stothart. Their research groups produced some of the commonly cited early FPVS applications, especially in face perception and semantic categorization.

  • Rossion, B. (2014). Understanding individual face discrimination by means of fast periodic visual stimulation. Experimental Brain Research, 232(6), 1599-1621. DOI
  • Rossion, B., Torfs, K., Jacques, C., & Liu-Shuang, J. (2015). Fast periodic presentation of natural images reveals a robust face-selective electrophysiological response in the human brain. Journal of Vision, 15(1), 18. DOI
  • Stothart, G., Quadflieg, S., & Milton, A. (2017). A fast and implicit measure of semantic categorisation using steady state visual evoked potentials. Neuropsychologia, 102, 11-18. DOI

Standardizing FPVS Workflows via Software Tools

FPVS has demonstrated the ability to produce strong, reliable neural responses in various research domains, but this method is still only being actively utilized in a small number of labs around the world. Increasing awareness of the effectiveness of FPVS and solving potential barriers to its adoption are important goals of my research.

Many other EEG methods have benefited from dedicated software tools that make them easier to use and more standardized across labs. For example, EEGLAB (via MATLAB) and MNE-Python have streamlined the process of EEG data analysis and made it more accessible to researchers across domains. EEGLab, for example, was instrumental in bringing advanced EEG data analysis methods to a wider audience by reducing friction - it provided a user-friendly interface and standardized workflows that made it easier for researchers to apply complex analyses without needing to write custom code from scratch.

My goal with FPVS is the same: to create dedicated software tools that make it easier for researchers to design FPVS experiments, analyze FPVS data, and share their work in a more standardized way. To learn more about my software development efforts, see the Software page.

Ongoing FPVS Projects

Our lab is using FPVS to study how the brain responds to visual, emotional, and semantic information across several research areas:

  • Dyslexia and individual differences in language-related processing
  • Anxiety-linked differences in emotional face processing
  • Fear of heights and visual sensitivity to height-related stimuli
  • Effects of creatine on emotional processing
  • Effects of hormonal birth control on emotional processing in women across different phases of the menstrual cycle

Across these projects, FPVS provides a way to measure targeted neural responses with short EEG recordings, making it useful for research questions where participant burden, clinical relevance, and reproducibility matter.

Recently Completed FPVS Work on Semantic Categorization

During my research for my M.S. degree, I completed a semantic categorization FPVS study using highly visually similar stimuli. The results suggest that FPVS can still elicit a semantic categorization response even when visual differences between categories are tightly controlled. However, the signal-to-noise ratio was weaker than in other published semantic categorization work, likely due to the low semantic distance and low visual variability of the stimuli used. This manuscript is currently in preparation.

This project helped to clarify both the promise and the limits of FPVS semantic categorization designs: strong visual control is valuable, but reducing semantic distance can also reduce response strength.