A Quantitative Benchmark of Visual Information in Human Brain Recordings Across fMRI, MEG, and EEG

Noninvasive human brain recordings such as fMRI, MEG, and EEG are widely used to study visual representations, yet their relative information content has never been quantitatively benchmarked under matched experimental conditions. Here, we develop a unified decoding framework that enables fair cross-modal comparison using identical naturalistic stimuli (THINGS) and a common decoding analysis. Using a matched stimulus set, we first quantify modality performance via object-category decoding accuracy. Under these conditions, fMRI yielded the highest accuracy (mean 87%), MEG showed intermediate performance (mean 35%), and single-subject EEG achieved lower accuracy (mean 7%), while remaining reliably above chance level (1%). Notably, EEG performance improved substantially when decoding outputs were aggregated across participants in the form of a stimulus-level relational structure, yielding a group-level EEG measure (28%) with accuracy comparable to MEG. We then examined how decoding performance scales with the number of training stimuli and effective measurement time. These analyses revealed distinct efficiency–precision trade-offs across modalities: MEG and group-level EEG were most efficient for short measurement times, whereas fMRI achieved the highest asymptotic performance when sufficient data were collected. Together, these findings demonstrate that modality performance differs systematically even under tightly matched conditions and that decoding provides a principled way to assess the information content of fMRI, MEG, and EEG. The proposed unified framework offers practical guidelines for modality selection, experimental design, and data collection planning in neuroscience. Highlights ✓ Unified decoding framework enables quantitative, matched comparison of fMRI, MEG, and EEG. ✓ fMRI shows the highest decoding accuracy; MEG intermediate; EEG lower. ✓ Aggregating EEG decoding outputs into a stimulus level relational structure across subjects markedly improves performance to near MEG levels. ✓ MEG and EEG (aggregated across subjects) are most efficient for short recordings, whereas fMRI achieves the highest performance with longer measurement time.