Estimating Visual Receptive Fields from EEG

The visual receptive field (RF) characterizes the spatiotemporal properties of the visual pathway and serves as a fundamental unit for information encoding. While RFs have been extensively studied across various neural modalities, such as functional Magnetic Resonance Imaging (fMRI), Electrocorticography (ECoG), and Magnetoencephalography (MEG), their investigation via Electroencephalography (EEG) remains limited. In this study, we introduce a stimulation paradigm that combines white noise image sequences with a letter detection task to elicit central visual field EEG responses. Using the aligned/shuffled reverse correlation, we estimate RFs across different resolutions and demonstrate that the resulting RFs exhibit rich spatiotemporal characteristics. To validate the reliability of the estimated RFs, we constructed a visual EEG reconstruction model, which achieved good performance in a classification task. The same RF estimation method was subsequently applied to high-density EEG recordings to investigate the information gain afforded by high-density configurations in visual space. This work fills a gap in the study of visual RFs regarding the EEG modality and may inform the paradigm design of visual brain-computer interfaces.