Introduction

Justification

I study visual perception using different neuroimaging techniques, including EEG/MEG and fMRI. I’m particularly interested in finding out how well we can localize signals across modalities. This original paper reports that combining EEG and MEG can approach the spatial resolution of fMRI when localizing signals in primary visual cortex (V1). This is significant for vision research: if we can get fMRI-like localization without scanning every time, we can run more flexible studies while keeping spatial accuracy in check. I will replicate the core idea on the EEG side, using the same two visual stimuli at 5° eccentricity in the left visual field that the original paper used. Using each subject’s pRF maps and structural MRI-based head models as “ground truth”, I will quantify localization error of EEG source estimates and compare it to the paper’s reported error profiles.

Stimuli and Procedures

This replication project will include conducting EEG experiment and using previously obtained MRI data on the same participants (N=7).

Stimuli will be two small patches presented at 5° eccentricity in the left visual field (same positions as two of the original paper’s four stimuli), designed to drive a circumscribed patch of right-hemisphere early visual cortex (V1). Each patch will be a high-contrast checkerboard (size ~1–2°; exact size matched to our lab’s display geometry), contrast-reversing at a steady rate to boost SSVEP SNR. Subjects fixate centrally with a fixation marker; brief attention checks (e.g., infrequent color change at fixation) help maintain fixation. Each stimulus position will be run in multiple short blocks to collect adequate trials for frequency-domain averaging.

For “ground truth,” I will use each subject’s pRF maps and cortical surfaces from T1 MRI to define the expected cortical locus (and extent) representing each 5° stimulus. For EEG, I will use each subject’s MRI to build a BEM head model, co-register electrodes to the scalp, and compute forward models. I will estimate sources with a standard inverse (e.g., depth-weighted MNE/dSPM or an LCMV beamformer with appropriate regularization). Localization error will be computed as the cortical (geodesic) distance between the EEG peak (or center-of-mass above a fixed threshold) and the pRF-defined target vertex/ROI. I will summarize error per stimulus and per subject, plus uncertainty via bootstrap over epochs.

Potential Challenges

  • Retinotopic variability: the same 5° polar-angle location can fall on gyral vs. sulcal cortex across people; sources buried in a sulcus have weaker EEG fields, inflating error.
  • Ground-truth alignment: pRF maps have their own uncertainty (fit noise, attention/fixation drift). I will propagate pRF ROI uncertainty when scoring error (e.g., distance to the nearest vertex within the pRF ROI).
  • Head model/inverse sensitivity: localization is sensitive to BEM accuracy, conductivity assumptions, coregistration error, inverse depth weighting, and regularization. I will fix parameters a priori and report sensitivity analyses (e.g., ±10% regularization, with/without depth weighting).
  • SNR and sample size: with few subjects, variance will be high. I will maximize SNR (SSVEP averaging, artifact rejection/ICA, notch and band-pass) and report per-subject results plus group medians rather than rely only on NHST.

Repository link: GitHub repo

Original paper (PDF in repo): sharon2007.pdf