EEG-Based Decoding of Color and Visual Category Representations Is Reliable Within and Across Sessions

Abstract

The human visual system represents stimuli in a rich and detailed manner. Traditional methods of studying visual representations in humans, such as event-related potentials (ERP), revealed numerous distinctions between the brain activity elicited by different categories of stimuli. However, these methods miss the information embedded in the spatial distributions of brain activity, or patterns, and are not always sensitive to study visual representations of different stimuli at the single participant or single trial level. Time-resolved multivariate pattern classification analysis (MVPA), or Decoding, efficiently extracts the visual representations of stimuli from the EEG topography without a-priori assumptions about the location of the effect in time and space at the single participant level. The rich information this method provides has increased its popularity dramatically in recent years. Yet, different participants show variable quality of decoding performance, and it is unclear if the accuracy of decoding is maintained within participants across multiple sessions, tasks, attentional conditions and visual features. In the current study, participants performed three visual tasks, over two sessions (1-7 days apart). We examined the correlation of decoding accuracy: within the cross-validation set, between sessions, between features (color and category) and to different measurements of the ERP signal and behavioral performance. We also examined how models generalized to different tasks and different attention conditions. We found that decoding accuracies varied substantially across participants, and that decoding accuracy was reliable within participant, over sessions, attention condition and task. This suggests the decodability behaves like an individual trait. Moreover, the spatial patterns underlying the decoding (classification weights) generalized across different tasks, attentional conditions and sessions. This suggests minimal representational drift at the resolution allowed by the EEG. We conclude that EEG decoding is a reliable method, and that visual representations are stable.