Feasibility of decoding visual information from EEG

Abstract

Decoding visual information, such as visual imagery and perception, from EEG data can be used to improve understanding of the neural representation of visual information and to provide commands for BCI systems. The appeal of EEG as a neuroimaging tool lies in its high temporal resolution, cost-effectiveness, and portability. Nevertheless, the feasibility of using EEG for visual information decoding remains a subject of ongoing inquiry. In this review, we explore the neural correlates of this visual information, specifically focusing on visual features such as colour, shapes, texture, and also naturalistic whole objects. We begin to examine which visual features can be effectively measured using EEG, taking into account its inherent characteristics, such as its measurement depth, limited spatial resolution, and high temporal resolution. Using a systematic approach, the review provides an in-depth analysis of the current state-of-the-art in EEG-based decoding of visual features for BCI purposes. Finally, we address some potential methodological improvements that can be made to the experimental design in EEG visual information decoding studies, such as palette cleansing, augmentation to bolster dataset size, and fusion of neuroimaging techniques.