Spatiotemporal cortical dynamics for rapid scene recognition as revealed by EEG decoding.

Abstract

The human visual system rapidly recognizes the categories and global properties of complex natural scenes. The present study investigated the spatiotemporal dynamics of neural signals involved in ultra-rapid scene recognition using electroencephalography (EEG) decoding. We recorded visual evoked potentials from 11 human observers for 232 natural scenes, each of which belonged to one of 13 natural scene categories (e.g., a bedroom or open country) and had three global properties (naturalness, openness, and roughness). We trained a deep convolutional classification model of the natural scene categories and global properties using EEGNet. Having confirmed that the model successfully classified natural scene categories and the three global properties, we applied Grad-CAM to the EEGNet model to visualize the EEG channels and time points that contributed to the classification. The analysis showed that EEG signals in the occipital lobes at short latencies (approximately 80[~] ms) contributed to the classifications other than roughness, whereas those in the frontal lobes at relatively long latencies ([~] 164 ms) contributed to the classification of naturalness and the individual scene category. These results suggest that different global properties are encoded in different cortical areas and with different timings, and that the encoding of scene categories shifts from the occipital to the frontal lobe over time.