Decoding of image properties from single-trial visual evoked potentials recorded by ultra-high-density EEG

Visual evoked potentials (VEPs) recorded by encephalography (EEG) allow us to study the neuronal activity non-invasively and in high temporal resolution. Traditionally, EEG analyses have relied on univariate group-level statistics and trial averaging to detect effects. However, recent advances in high-density EEG enable the investigation of brain responses at the single-subject and single-trial level. In this study, we combine ultra-high-density (uHD) EEG with cross-validated single-trial decoding to bridge both approaches, improving generalizability and reproducibility. Study participants were shown a diverse set of random images while 512 channels from the uHD system recorded their EEG over the occipital lobe. Image properties (contrast, hue, luminance, saturation and spatial frequency) were extracted for each stimuli and VEPs were used for decoding these properties in a cross-validated regression analysis. Additionally, the same data were spatially subsampled to investigate the impact of spatial resolution and electrode density on the decoding performance. Image properties could be decoded from single-trial VEPs, with contrast, saturation and spatial frequency providing the best decoding performances. Grand average decoding performance across all image properties and subjects yielded a Pearson's r of 0.50 between predicted and actual image property score. Greater electrode density improves decoding performance compared to standard EEG as well as subsampled configurations. Image properties robustly modulate early components of the VEP. Importantly, these modulations are pronounced enough to allow for single-trial decoding. Our analyses highlight the importance of electrode density with improvements in decoding performance extending even below 10 mm of inter-electrode distance.