BCIwiki (bciwiki.com) — Researchers at Bishop’s University, Canada, posted a preprint to bioRxiv on September 28, 2026. It compared individual differences in visual decoding across brain measurement instruments. It has not been peer reviewed. The study examined electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG, and functional magnetic resonance imaging (fMRI), testing classifiers on recording runs and stimulus identities excluded from training.
Within-instrument decoding was reproducible. Attention, behavior, and peripheral signals affected recording quality but did not explain most stable differences between participants. Source-to-sensor distance strongly predicted modeled signal gain, with the steepest effect in planar gradiometers. Intracranial recordings linked sharper local category selectivity to easier decoding. This association chiefly reflected a smaller response to the nonpreferred category relative to overall visual responsivity.
Cross-site prediction was uneven. EEG and MEG expressed preferred and nonpreferred responses differently, while MEG category separation and task-aligned variance described the information available to the decoder. Simultaneous EEG-MEG recordings contained a shared participant component, but external paired cohorts did not produce a common participant ranking across instruments. Correspondence depended on the measure, the instrument pair, and, in one cohort, participant-linked stimulus sets.
Repeated observations improved performance in participants whose single-observation decoding was weak. The authors interpret stable decoding differences as reflecting the relationship between the individual, the represented distinction, and the instrument. In their account, these differences chiefly determine how much information a recording supplies per observation, rather than constituting a personal attribute independent of measurement conditions.