Deep Learning for EEG-Based Visual Classification and Reconstruction: Panorama, Trends, Challenges and Opportunities.

Abstract

Deep learning has significantly enhanced the research on the emerging issue of Electroencephalogram (EEG)-based visual classification and reconstruction, which has gained a growth of attention and concern recently. To promote the research progress, at this critical moment, a review work on the deep learning methodology for the issue becomes necessary and important. However, such a work seems absent in the literature. This paper provides the first review on EEG-based visual classification and reconstruction, whose contents can be categorized into the following four main parts: 1) comprehensively summarizing and systematically analyzing the representative deep learning methods from both feature encoding and decoding perspectives; 2) introducing the available benchmark datasets, describing the experimental paradigms, and displaying the method performances; 3) proposing the methodological essences and neuroscientific insights as well as the dynamic closed-loop interaction and promotion between them, which are potentially beneficial for technological innovations and academic progress; 4) discussing the potential challenges of current research and the prospective opportunities in future trends. We expect that this work can shed light on the technological directions and also enlighten the academic breakthroughs for the issue in the not-so-far future.