Multi-domain feature fusion for target color decoding from multichannel neural recordings of pigeons

The entopallium is a high-level neural structure of the avian visual system that is related to the processing of color- and shape-related information during target recognition. The mechanism underlying it remains unclear and has drawn a considerable amount of research interest. Decoding has invariably been used in relevant studies to verify whether the extracted response-related features contain visual information. However, local field potentials (LFPs) often contain rich features. In this study, We combine the features of different domains to decode color information, including those in the time and frequency domains as well as features related to non-linear and functional network connectivity. To extract valuable response-related features, three types of feature selection methods are compared. The LFP data from the entopallium of a pigeon were recorded during periods of stimulus presentation in which two colors were randomly presented in each trial. The selected feature set was finally used to decode the colors by using several general classifiers. The results showed that the fusion of multi-domain features helped improve decoding performance, and the optimal feature set was a fusion of multiple domains of features. These results improve our understanding of the avian neural mechanisms involved in encoding color-related information.